thèse, j'ai eu la chance d'en avoir deux avec l'unité FRISE de l'INRAE ! Depuis plus de 5 ans je fais partie de cette grande famille, pleine de vie et de bienveillance.

A tous les doctorants, jeunes docteurs, post-doc ou jeunes tout court ;) de FRISE, Nada, Dihia, Jordan, Emmanuel, Véro, Fatima, Elyamin, Walid, Tanathep, Ahmad merci pour les petits mots d'amitié qui m'ont fait chaud au coeur, pour votre grain de folie et d'énergie ! A toute l'unité FRISE qui m'a accueillie depuis mon parcours recherche, merci pour tout ! C'est grâce à vous que j'ai découvert et aimé faire de la recherche.

Une dédicasse particulière à mon ancien professeur et maintenant ami, Pascal Clain, qui a cru en moi depuis le début, qui m'a toujours poussée et à qui je suis et resterai reconnaissante !

Un grand merci à tous les Industriels, qui ont participé de près ou de loin à ce projet de thèse, particulièrement aux personnes que j'ai interviewé et aux collaborateurs de FAC SOS pour leur motivation, remarques et disponibilité.

A tous mes amis de lycée, JDA+++, qui ont toujours été là, depuis plus de 10 ans, même sans comprendre ce que je fais depuis 3 ans. Votre amitié est une source de joie ! Merci à vous ! A Marine, qui a vécu la même expérience que moi depuis 3 ans, qui a été mon compagnon de route, mon épaule, à toute saison et toute heure, merci infiniement. Cette thèse n'aurait pas été pareil sans toi ! A Pierre, Rose et Camille, mes fidèles amis de coeur, depuis si longtemps… Je suis si reconnaissante de vous avoir dans ma vie ! A mes amis du PR, Bastien, Loup-Noé, Clément, c'est aussi grâce à vous que j'ai continué en recherche, travailler avec vous était plus qu'un plaisir ! A Estelle, ma très chère amie, tu sais l'importance de ton amitié et de ta présence dans ma vie, merci pour ton soutien tout au long de ma thèse et jusqu'au bout ! A mon très cher et tendre Rémi, j'ai eu une véritable chance de t'avoir à mes côtés pendant cette expérience si particulière, tu as toujours trouvé les mots justes, tu m'as soutenue sans faille. Merci pour ton affection, ton soutien jour et nuit et merci d'être toi ! Et finalement, à ma famille, mon père Iraj, ma mère Khadija, et mon frère Isaac, vous avez été dans mon coeur pendant toute cette période. Cette thèse a été une aventure que vous avez rendu exceptionnelle en vous ayant à mes côtés. Vous êtes formidables, votre amour et votre soutien m'ont permis d'en arriver là. Isaac, merci d'être toujours là. Papa, Maman, j'ai de la chance de vous avoir comme parents.

Titre : Modèle de connaissances et exploration de l'espace de conception des systèmes de réfrigération durables -Application à la conception intégrée des systèmes de réfrigération des supermarchés.

Mots clés : exploration d'espace de conception, réfrigération, modèle de connaissance, conception intégrée Résumé : L'impact du changement climatique sur le secteur de la réfrigération a entraîné une augmentation du besoin en froid. L'objectif principal de cette thèse est de proposer une approche de conception intégrée et une plateforme d'exploration d'espace de conception pour aider la prise de décision dans les phases amont de conception et aller vers des systèmes de réfrigération durables. Tout d'abord, un diagnostic de terrain est réalisé pour représenter et évaluer les opportunités de développement au sein du système socio-technique de la réfrigération. Il en résulte que les principaux points d'amélioration du système socio-technique actuel de la réfrigération sont un manque de compréhension des technologies émergentes, une faible exploration de l'espace de conception et une diversité de langage et d'outils entre les parties prenantes.

Ainsi, il est proposé d'aller vers une approche plus intégrée de la conception d'un système de réfrigération grâce à un modèle de connaissances et à une plateforme de simulation et d'exploration de l'espace de conception, cette dernière permettant d'aboutir rapidement à une architecture dimensionnée satisfaisant toutes les parties prenantes. Une vérification de l'aptitude de la plateforme à modéliser correctement le problème, à exprimer les préférences des concepteurs et à aboutir au choix d'une architecture dimensionnée satisfaisante est effectuée au travers d'ateliers impliquant des acteurs de la conception de systèmes de réfrigération. Une validation finale des modèles est réalisée en évaluant huit critères tels que la robustesse et la facilité d'utilisation.

Title : Knowledge model and design space exploration of sustainable refrigeration systems -Application to the integrated design of supermarket refrigeration systems Keywords : design space exploration, refrigeration, knowledge model, integrated design Abstract: The impact of climate change on the refrigeration sector has led to an increase in the need for cooling. The main objective of this thesis is to propose an integrated design approach and a design space exploration platform to help decision making in the upstream design phases and move towards more sustainable systems. First of all, a field diagnosis is carried out to represent and evaluate the development opportunities in the socio-technical system of refrigeration. The result is that the main shortcomings of the current sociotechnical system of refrigeration are a lack of understanding of emerging technologies, poor exploration of the design space and a diversity of language and tools between stakeholders. Thus, it is proposed to move towards a more integrated approach to the design of a refrigeration system thanks to a knowledge model and a simulation and design space exploration platform, the latter allowing to quickly arrive at a dimensioned architecture satisfying all stakeholders. A verification of the platform's ability to correctly model the problem, to express the designers' preferences and to lead to the choice of a satisfactory dimensioned architecture is carried out through workshops involving actors in the design of refrigeration systems. A final validation of the models is carried out by evaluating eight criteria such as robustness and ease of use.

6

The more clearly we can focus our attention on the wonders and realities of the universe about us, the less taste we shall have for destruction.

-Rachel Carson

English summary

The impact of climate change on the refrigeration sector has led to an increase in cooling.

Nearly 20% of the global electricity consumption is currently used for cold production, and it is expected to reach 37% by 2050. The main objective of this thesis is to propose an integrated design approach and a design space exploration platform to help decision making in the upstream design phases and move towards more sustainable systems.

First of all, a field diagnosis is carried out to represent and evaluate the development opportunities in the socio-technical system of refrigeration. This diagnosis is based on interviews, a literature review and observations. The Radical Innovation Design (RID) methodology is then used to build a knowledge book and assess development opportunities.

The result is that the main shortcomings of the current socio-technical system of refrigeration are: (1) lack of understanding of emerging technologies; (2) poor exploration of the design space; (3) diversity of language and tools between stakeholders resulting in underperforming refrigeration systems.

Thus, it is proposed to move towards a more integrated approach to designing a refrigeration system thanks to a knowledge model and a simulation and design space exploration platform, the latter allowing to arrive at a dimensioned architecture satisfying all stakeholders quickly.

In order to capitalise on the knowledge around the design of refrigeration systems, fifteen experts in the field of refrigeration with different roles in various companies were solicited through interviews, in addition to the literature review and field observations. A knowledge model was then proposed to design and simulate supermarket refrigeration systems. Four concepts are developed in relation to the supermarket usage context, solution structure, system properties, and performance, including physical, energy, environmental, economic, and maintainability models. A platform for simulation and exploration of the design space based on a brute-force enumeration of feasible solutions is proposed and a "design by shopping" exploration mode. Verification of the platform's ability to correctly model the problem, express the designers' preferences, and lead to the choice of a satisfactory dimensioned architecture is carried out through workshops involving actors in the design of refrigeration systems. Final validation of the models is carried out by evaluating eight criteria such as robustness and ease of use and comparing the solutions the designers arrived at with a case from the literature.

Acknowledgement/ Remerciement

Cette thèse de doctorat, n'aurait pas vu le jour sans la participation et le soutien d'un bon nombre de personnes.

Je commence cette série de remerciement par mon équipe encadrante, du LGI et de FRISE.

A mon Directeur de thèse Pr. Bernard Yannou, je te remercie sincèrement pour tout le savoir que tu m'as transmis, et ta disponibilité constante.

A mon co-directeur de thèse, Anthony Delahaye, je te suis reconnaissante pour ta bienveillance, ton écoute et tes conseils.

A Minh Hoang et Laurence Fournaison, merci infiniement de votre soutien, vos conseils, dans les moments durs et solitaires.

A François Cluzel et Yann Leroy, je vous remercie d'avoir été là toujours à des étapes crutiales de ma thèse et de votre confiance pour les missions d'enseignement. A tous, je vous suit reconnaissante de m'avoir fait confiance il y a 3 ans. Ce sont vos conseils, critiques et connaissances qui m'ont permis de mener à bien ce projet de thèse.

Je tiens ensuite à exprimer toute ma reconnaissance aux membres de mon jury d'avoir évalué mon travail de thèse.

A Patrick Sebastian et Paul Byrne, pour avoir accepté de rapporter ma thèse. Je vous remercie pour vos remarques et retours reconstructifs qui permettront de développer notre travail de recherche. A Mickael Kokkolaras, pour avoir accepté de présider mon jury de thèse. Je vous remercie pour le bon déroulement de ma soutenance et l'expertise que vous avez apportée.

A Isabelle Blanc et François Puel, merci pour les interrogations soulevées lors de ma soutenance et les discussions qui suscitent de nouveaux champs de recherche à explorer.

Passons à tout le Laboratoire de Génie Industriel… MERCI ! Je ne saurais dire à quel point votre présence pendant ma thèse a été importante. A tous les doctorants, particulièrement à MarcO, Marianna, Fawaz, Robin, Tjark, Emma, Emilien, Joseph, Franscisco, Diego, Gui, Julien, Camille, Benoît… j'aimerais vous dire : merci pour votre énergie revitalisante ; courage pour finir et surtout profitez de cette expérience fantastique ! A tous les jeunes docteurs que j'ai eu la chance de croiser et avec qui j'ai forgé une amitié, Naouress, Bassem, Lara, ainsi que Paul-Camille et Vincent merci infiniment de votre aide, votre amitié et vos encouragements particulièrement dans des périodes compliquées ! A toute l'équipe d'IC, et particluièrement à l'équipe Support, Flore, Franck, François, Yann, Robin et Julien, merci de m'avoir permis de participer à cette aventure et merci pour votre gentillesse ! A Delphine, Carole, Corinne, Matthieu et Sylvie, l'équipe clé du labo qui fait en sorte que tout se passe pour le mieux du début à la fin, merci ! Il est souvent dit que c'est une chance de tomber sur un labo exceptionnel. Pendant cette Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by [START_REF] Neema | Constraint-based design-space exploration and model synthesis[END_REF]. .................

Table of contents

List of figures

List of tables

General introduction

This chapter introduces the general context of this Ph.D. thesis, the motivation and challenges that drove this work around the design of refrigeration systems. This work is based on multi-field and multi-methodology research, including two research disciplines, refrigeration process engineering and design engineering. The first section of this chapter explains the context and motivation of the work. The second section describes the research objectives. The last section details the main outline of the thesis.

Context, motivation and objectives

1.1.

General context

Since the beginning of the industrial revolution, around 1750, the greenhouse effect has been amplified by releasing large quantities of greenhouse gases (GHG) into the atmosphere. The massive use of fossil fuels such as oil, coal or natural gas, deforestation, certain industrial processes and agricultural practices, and the burial of waste have played a major role in increasing GHG emissions. According to data from the Intergovernmental Panel on Climate Change (IPCC), annual greenhouse gas emissions increased by 80% between 1970 and 2014 [START_REF] Blanco | Drivers, Trends and Mitigation[END_REF]. International protocols consider that these high emissions lead to global warming and ocean warming.

The responsibility to fight against climate change is shared by all spheres of society, i.e.

governments, industries, academics and civil society. Education and academic research are important to move toward sustainability, but strict regulation of greenhouse gas emissions by various levels of government is essential [START_REF] Panayotou | Instruments of Change: Motivating and Financing Sustainable Development[END_REF].

In recent decades, the world has been increasingly affected by climate change, causing economic, social and territorial disasters. Through the Montreal protocol in 1987 and Kigali amendments in 2005 and 2016 [START_REF] Heath | Amendment to the Montreal Protocol on Substances That Deplete the Ozone Layer (Kigali Amendment)[END_REF]; the United States. Congress. Senate. Committee on Foreign Relations 1993), the refrigeration industry has been concerned with the environmental issues, banning the use and production of refrigerants responsible for the ozone layer destruction, such as CloroFluoroCarbons (CFCs), and with high Global Warming Potential (GWP), such as HydroFluoroCarbons (HFCs). We use more and more cooling equipment in our daily life, for food safety, for our comfort at home and workplaces, in transportation, for the conservation of medicines and vaccines, etc. Rising temperatures and extreme events such as heat waves and recent pandemics have increased the need for refrigeration. The impact of climate change on the refrigeration sector has resulted in increasing demand for cooling in the food sector, pharmaceuticals, buildings and transport (Schaeffer et al. 2012). Nearly 20% of all electricity consumption is used for cold production. This figure is expected to rise with growing demand and is predicted to reach 37% by 2050 (IEA 2018).

General objectives

In this context, academics and industrials have researched more sustainable and climatefriendly systems over the years. They have studied innovative and promising technologies to improve the energy efficiency of refrigeration equipment and fluid alternatives for HFCs.

Improving the energy efficiency and reducing the production and use of HFCs could avoid 0.5°C of global warming by 2100 (IEA 2020).

Nevertheless, technological improvement of refrigeration equipment is an essential lever in the fight against climate change, but not sufficient. Indeed, research in the refrigeration domain has been widely developed. New technologies are shown to be promising from an energy and efficiency point of view, but levers related to adopting these technologies in the industry persist. It is thus necessary to take the complexity of their socio-economic environment into account to implement effective new technologies.

Thus, to reduce direct and indirect effects related to the production, use and end-of-life of refrigeration systems, it becomes necessary to assess existing and new technologies and offer a global approach that will help manage the research and development (R&D) and decision making.

This Ph.D. thesis is an applied research work, which aims to respond to these challenges by finding realistic solutions to the refrigeration domain's gaps, according to two main objectives.

First, this study provides a framework of understanding for the design process around refrigeration systems. The decision process is defined by identifying the various involved stakeholders, technologies, and knowledge to achieve this first goal. The second goal of this work is to develop relevant design artefacts to support research and industry in the transition toward sustainable refrigeration systems. For this purpose, a global approach based on a knowledge model (ontology) dedicated to refrigeration and a simulation and design space exploration tool is proposed.

Thus, in this thesis, research and industry issues were considered to improve the efficiency and sustainability of refrigeration systems. The scope of this study and its positioning are detailed in the next chapters.

Outline of the thesis

The thesis is organized into seven chapters. Chapter 1, presented here, serves as the general introduction. The context, research fields and positioning of this work are described.

In Chapter 2, a literature review of the research fields and relevant work is described. It highlights the research gaps addressed in the dissertation.

Chapter 3 details the research questions, the objectives to answer these questions and the general research methodology of the thesis.

In each contribution chapter, relevant literature was presented to position the novelty of each chapter contribution and the specific used methodology is discussed.

Chapter 4 presents the results of the refrigeration domain diagnosis. The assessment methodology is discussed, the research gaps and questions were confronted by industrial and academic experts.

Chapters 5, 6 and 7 describe the main contributions. In Chapter 5, a knowledge model was built. The relevant literature and related works are discussed, and the results are presented.

Chapter 6 detailed the development of a digital platform for the exploration of design space.

It is based on the knowledge model and presented through usage scenarios. Finally, Chapter

Literature review on refrigeration systems design and simulation

This chapter presents a literature review and is composed of three main sections. First, the principle, main architectures and components of refrigeration systems are detailed. In the second section, approaches and tools in industrial engineering, specifically system engineering, could be applied to refrigeration systems' design process. The literature review then focuses on the modelling of complex systems, the definition of a design process and how to solve a design problem. Finally, an overview of existing simulation-based tools to support refrigeration systems design is realized. This chapter concludes with a section highlighting the existing gaps in the literature prior to the definition in the next Chapter 3 of the emerging research questions and the associated objectives.

Refrigeration systems

Definitions and applications

The International Energy Agency (IEA) has defined cooling as "any human activity, design or technology that dissipates or reduces temperatures and contributes to achieving: (i) reasonable thermal comfort for people, or (ii) preservation of products and produce (medicines, food, etc.), and (iii) effective and efficient processes." Main applications and equipment in the refrigeration domain are presented in Table 2-1.

Domestic refrigerators

The present Ph.D. thesis focuses on commercial refrigeration. This sector represents 3-5% of energy consumption in European countries (Evans et al. 2014) and is expanding to meet growing demand. In France, retail stores have seen their footfall increase by 10% in 5 years and their number by 8%. Refrigeration equipment (display cabinets or cold rooms) is responsible for 35-50% of the energy consumed by a retail store [START_REF] Timma | Benchmarking Analysis of Energy Consumption in Supermarkets[END_REF].

Cold production by vapour-compression refrigeration

A cold production system is composed of 4 principal components:

 compressor  condenser  expansion valve

System architecture

Different architectures exist to meet a multitude of needs. This section will describe the most frequently used refrigeration systems in retail. The layout of the main components does not vary, but the number of components may differ, and supplementary elements may be added to the system.

Centralized direct expansion system

Most supermarket refrigeration systems use a centralized direct expansion system where the cold production is connected to the entire store through a piping system that circulates and feeds refrigerants to all the display cabinets' evaporators. The cold production system (usually in the machine room) is separated from the display cabinets' location. For a traditional centralized system to perform adequately, a certain number of compressors must be used in order to attain the required power. This arrangement can result in unused compressor capacity and wasted energy consumption. However, this type of system is the easiest one to be installed due to its simple architecture.

The following figure represents a simplified scheme of the system architecture in which ventilators and control components are not presented.

Plug-in system

Plug-in (PI) refrigeration unit is a direct expansion system with compressor, condenser, expansion valve and evaporator associated with a cabinet. For plug-in cabinets, the refrigeration unit is fitted inside the furniture. Thus, no machine room is needed.

Secondary loop system

The secondary loop refrigeration systems are frequently used in industrial refrigeration and commercial comfort cooling and are also known as "Liquid-Chilling Systems" (ASHRAE 2008). This type of system architecture was first introduced to overcome several dangerous aspects of refrigerants (toxicity, flammability, high GWP…) or for system compatibility.

The baseline of this architecture (Figure 234) is composed of two loops. The primary loop, a direct expansion system using a traditional refrigerant (called primary refrigerant), cools the secondary fluid using a secondary fluid / primary fluid heat exchanger (Heat Exchanger in the figure) [START_REF] Wagner | A General Decomposition Methodology for Optimal System Design[END_REF]. This fluid provides the necessary cooling through heat exchangers in cold rooms and display cabinets that replace traditional evaporators in Figure 2-2.

The main advantages of such architecture are confining the primary loop and using environmentally friendly refrigerants to transport the cooling power by the secondary loop. It reduces the amount of primary refrigerant charge and refrigerant leakage due to shorter circuits. The system design is simpler than a distributed direct expansion system which is composed of multiple subsystems. Service and maintenance are easier than a primary centralized system (Horton 2004). The additional cost of pumps and cooler heat exchangers could be offset by combining reduced refrigerant charge and copper pipe length (by using the plastic pipe instead of copper pipe in the secondary loop SL) in supermarket application [START_REF] Delventura | Secondary Loop Systems for the Supermarket Industry[END_REF].

The study by Kazachi and Hinde (Kazachki and Hinde 2006) showed the comparison of a secondary loop system and a traditional direct expansion system in a supermarket. They confirmed the advantages presented above and identified the drawbacks of this system. The presence of circulating pumps and intermediate heat exchangers draws additional energy consumption. From ADEME report (ADEME 2001), it is noticed that the secondary refrigeration used approximately 10% more energy than a direct system. Due to their lower environmental impact than conventional systems, the CO2 or R744 as a refrigerant has been explored by many researchers [START_REF] He | An Optimal Multivariable[END_REF]. CO2 is a natural refrigerant, non-flammable and non-toxic, it has zero ODP (Ozone Depletion Potential), and its Global Warming Potential (GWP) is equal to 1. The ODP and GWP of the most common refrigerants can be found in the next section. CO2 is also economically attractive. As there are no limitations regarding the use of R744 as refrigerant, it is considered one of the most viable solutions for supermarket refrigeration applications [START_REF] Lorentzen | Revival of Carbon Dioxide as a Refrigerant[END_REF].

Many configurations of systems using CO2 have been tested both theoretically and experimentally. The aim was to achieve the best possible performance compared to the conventional refrigerants that are currently used.

The CO2 booster refrigeration system or transcritical refrigeration has been applied in the modern supermarket to substitute for the conventional R404A system. One significant problem concerning the use of CO2 is that its low critical temperature (31 °C) leads to operation in transcritical conditions when the ambient temperature is high. The operation in transcritical conditions demands higher energy consumption compared to systems using conventional refrigerants, which is an obstacle for the use of CO2 in locations with high ambient temperatures [START_REF] Mitsopoulos | Annual Performance of a Supermarket Refrigeration System Using Different Configurations with CO2 Refrigerant[END_REF].

A typical CO2 transcritical refrigeration system for a supermarket application is presented by Ge and Tassou (2011b). The architecture presents four pressure sections: high, intermediate,

Heat exchanger

medium and low. In the first section, the system is composed of a high stage compressor outlet, followed by the gas cooler or condenser depending on ambient conditions, the suction line heat exchanger and the high-pressure control valve. Then starts the second section, where the two-phase refrigerant flows into the receiver and immediately splits out with saturated vapour and liquid. The MT and LT expansion valves control the liquid circulating into their respective evaporators.

The refrigerant out of the LT evaporator is then sucked into the low stage compressor before mixing with the refrigerant from the MT evaporator. A simplified representation of a transcritical system is presented in Figure 2345. Other configurations exist. The most commonly found are the addition of an auxiliary compressor, the integration of liquid and vapour ejectors in parallel to the high-pressure valve, and the utilization of a mechanical subcooling unit after the gas cooler.

Refrigerants

In a vapour-compression refrigeration system, refrigerants are used to ensure the compression cycle. The choice of refrigerant depends on its cost, thermodynamic and chemical properties and working conditions.

Refrigerants can be listed according to their chemical composition (see Table 2-2). Some refrigerants have a high negative impact on the environment, particularly on global warming (CFC, HCFC and HFC) and ozone depletion (CFC). In the European Union, they are subjected to specific regulations. A charge limit is applicable depending on the application (commercial refrigeration or air conditioning) (NF EN 378, 2017).

Industrial engineering to support the design of refrigeration systems

Industrial engineering (IE) is a multidisciplinary research field that designs, studies and seeks to improve part or the entire life cycle of artefacts, i.e. all the processes, humans, systems, interactions, energy, materials, constrains and information from the design to the end of life of an artefact, with all the trades and skills involved. It can be applied to all domains such as healthcare, building, education, architecture, transport, energy.

Industrial engineering can be divided into four categories: system engineering, system and 2021) and [START_REF] Zhong | Inquiry and Experiential Mixed Teaching Method Is Effective Way to Cultivate High-Quality Innovative Talents[END_REF] To support industrial engineering, different fields constitute the theoretical frameworks necessary to fulfil studies objectives. It includes theories, methodologies, tools from mathematics, management, physics, optimisation, design science, behaviour science, psychology.

For our research objectives, we explore in further detail system engineering artefacts, design science, system analysis and optimisation with a focus on relevant works in the refrigeration domain.

Design science

Design science as a formal research discipline appeared during the second half of the 20 th century.

Design and science have many definitions in the literature. Eddington defines science as the "attempt to set in order the facts of experience" in [START_REF] Fuller | A Comprehensive Anticipatory Design Science[END_REF], and design is defined by [START_REF] Ralph | A Proposal for a Formal Definition of the Design Concept[END_REF] as "a specification of an object, manifested by some agents, intended to accomplish goals, in a particular environment, using a set of primitive components, satisfying a set of requirements, subject to some constraints."

The purpose of research in design science is based on finding models, approaches, and tools to improve the design of product or systems and their development processes [START_REF] Papalambros | Design Science: Why, What and How[END_REF]. Since 1960, many models to describe the uniformities in processes have been developed. In this section, a few design approaches that are considered in the present work are shown.

To support design science, a large literature of design philosophy (Yoshikawa 1989) or design theory and methodology (DTM) exists. Numerous authors have contributed to developing methods and diversifying applications domains of DTM.

Thus, the number of design theories is important while there is no consensus between the actors involved in the design process (industrials, researchers, designers…). The domain application, the study perimeters, and specifications… are highlighted by (Le Masson, Dorst, and Subrahmanian 2013) as the main difficulties of using design theories.

Complex system definition and modelling

In the design science literature, different ways exist to define and model a system in various fields as complex systems exist in many domains.

Complex systems definition

Pahl and Beitz (2013) defined dynamic systems in engineering as technical artefacts and systems consisting of sets of ordered and interrelated elements. They also characterized a system by its boundary, which cut its links with the environment. These links determine the system's external behaviour, so that it is possible to define a function expressing the relationship between inputs and outputs.

Over the past years, researchers have developed multiple definitions of complex systems adapted for each of their contexts: design research in a large company, research on eco-design of energy pathways, microbiology [START_REF] Cluzel | Proposition for an Adapted Management Process to Evolve from Unsupervised Life Cycle Assessment of Complex Industrial Systems towards an Eco-Designing Organization[END_REF][START_REF] Lesne | Modélisation Multi-Échelles Des Systèmes Vivants et de Leur Régulation[END_REF][START_REF] White | Case Studies in System of Systems, Enterprise Systems, and Complex Systems Engineering[END_REF]. Each author added elements for their definitions that apply to their field of work: biology, engineering, etc. A consensus exists for multiple common properties that define a complex system as:

-A system composed of subsystems and components that may interact with each other with different types of interaction -A system composed of subsystems and components that may have different types, structures, life cycles and lifetimes -A system that has close interactions and can be influenced by its environment at different levels -A system that is hardly predictable in the long term, even by knowing the properties of its isolated elements

In the next chapter of the thesis, refrigeration systems are considered complex systems. Indeed, a refrigeration system is composed of different components and sub-systems with thermodynamic, chemical, mechanical and physical interactions (see Section 0). The components have structural and chemical specifications, have different lifetimes and do not serve the same purpose. A refrigeration system is in close interaction with its surroundings and is directly influenced by its environments, such as its location and the climate. Finally, a refrigeration system's behaviour is difficult to predict because of the close interaction with its surroundings and the high number of elements influencing the system. The subsystems are also complex systems with particular interactions. Indeed, as the technology in the refrigeration industry is developing, emerging technologies can add their share of complexity due to low maturity and high uncertainties.

Decomposition of a complex system

In order to have a clear and precise look at a system in a product development process, the first step is to decompose it into smaller clusters that will have the same nature.

A complex system can be decomposed in several ways: object, aspect, sequential and modelbased (Pahl and Beitz 2013;[START_REF] Retho | Collaborative methodology for virtual product building to support aerial vehicles with electrical propulsion design[END_REF][START_REF] Wagner | A General Decomposition Methodology for Optimal System Design[END_REF]. Object decomposition divides a system by physical components. Aspect (or discipline) decomposition divides the system according to different disciplines -or specialities. Object and aspect partitioning are "natural" partitions.

Typically large companies employ both types of partitions simultaneously (mixed partition) in a matrix management organization. Sequential decomposition is applicable when partitioned sub-problems are organized by workflow or process logic and presume unidirectionality of design information.

The following figure is taken from the book Engineering Design (Pahl and Beitz 2013), and it

shows the decomposition of a system: S is the system boundary, I1,2,3 are the inputs, O1,2 are the outputs, S1-5 are the subsystems, S21-24 are the sub-subsystems. This kind of system decomposition can be applied to traditional machines and automates the system by having a unique presentation of the properties. The functional specifications of the model are explicit: the input and output flows are clear, and the transformation process is detailed.

Figure 234567. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right)

Engineering design process

The engineering design process is often used to create or redesign new products or systems.

It is composed of design theories, methodologies and tools. We review design process frameworks and the steps to design a system in design theories. Methodologies and tools are developed in the literature to support the design process. In the following section, a general overview of a design process general methodologies to support the development of systems and engineering design tools are presented.

General overview of the design process

Pahl and Beitz (2013) developed the design theory of the systematic approach. The description is essentially based on (1) the fundamentals of technical systems, (2) the fundamentals of the systematic approach, and (3) the general problem-solving process. To solve a technical problem, it is necessary to have a clear and easily reproduced relationship between inputs and outputs: all relationships must be planned and thus designed to meet the specifications. Figure 2-8 details the design process steps from Pahl and Beitz. The authors have proposed a general framework to structure the design process as a sequence of four phases that can be executed iteratively:

-Task Clarification: collecting, formulating and documenting the requirements of the product to be designed.

-Conceptual Design: Identifying a design solution's basic principles and outline (or concept).

-Embodiment Design: elaborating the design into a layout that satisfies various technical and economic criteria.

-Detail Design: finalising the design and preparing production documents.

Other frameworks have been developed (Ulrich and Eppinger 2016;[START_REF] Tayal | Engineering Design Process[END_REF]Otto and Wood 2001;Saaksvuori and Immonen 2008). The traditional steps developed in these theoretical frameworks for a product or system development are listed in the following list: o Feasibility assessment: the purpose of a feasibility assessment is to determine whether the engineer's project can proceed into the design phase. This is based on two criteria: the project needs to be based on an achievable idea, and it needs to be within cost constraints.

o Establishing design requirements: Some design requirements include hardware and software parameters, maintainability, availability, and testability.

o Preliminary design: In this task, the overall system configuration is defined, and schematics, diagrams, and layouts of the project will provide early project configuration.

 Detailed design: the task where the engineer can completely describe a product through solid modelling and drawings.

 Decision of a solution, prototyping, production planning  Production

Design process methodology

The stages of product development have been described by many authors (Pahl and Beitz 2013;[START_REF] Wagner | A General Decomposition Methodology for Optimal System Design[END_REF][START_REF] White | Case Studies in System of Systems, Enterprise Systems, and Complex Systems Engineering[END_REF][START_REF] Mottonen | Design for EXcellence in High Tech Companies[END_REF][START_REF] Tayal | Engineering Design Process[END_REF]Ulrich and Eppinger 2016). They agree on the need of making choices in the early stages to reduce the risk of change at the end and reduce the total life-cycle costs. To address early involvement, design for eXcellence (DfX) is one of the most popular concepts in quality management (Jiang, Shiu, and Tu 2007). DfX is an organised way to harmonize practice. It can reduce costs and increase values. Different types of design objectives are to be addressed: Design for Manufacturing, Design for Assembly, Design for Environment, Design for Sustainability… they depend on the choice of issues that the company wants to address in the early stages of design to avoid changes afterwards. Figure 2-9 shows different types of existing DfX. To achieve the design objective, it is necessary to have a holistic representation of the targeted domain by studying the technologies and the system-human interaction [START_REF] Ropohl | Philosophy of Socio-Technical Systems[END_REF]. For example, in the automotive industry, as product complexity increases and most experienced engineers retire, the development of a holistic representation and the use of DfX provides a framework for design teams to decide in the early stages of development [START_REF] Fatfouta | Towards a Support System for Simulation Aided-Design in the Development Phase of Automotive Industry[END_REF].

The holistic representation of a domain includes the understanding of the socio-technical system. This term is defined in the Oxford dictionary by the infrastructure, technologies, stakeholders with their specific role in the value chain, and the performances of a system [START_REF] Law | socio-technical system[END_REF]). In the refrigeration domain, there are practical problems related to the use of cooling technologies (breakdowns, accidents, lack of efficiency, etc.) and a heterogeneous knowledge of the domain's actors. There is no holistic representation of the refrigeration socio-technical system to our knowledge. This could help understand refrigeration systems with the diversity of technologies, stakeholders, design objects, etc. and thus better address the design problem. In the context of the commercial refrigeration industry, many points of view can be adopted from different stakeholders: refrigerant systems' fabricants, industrial users, supermarket consumers, regulation politics, environmental activists… It exists as many design paths as designers. However, the same design process framework presented in the previous section can be considered. Traditionally, designers have focused mainly on the product's performance, features, and appearance.

In the next sections, we focus on tools and approaches to assess the performances of a system and particularly in the refrigeration domain. We focus on the maintenance that is not widely assessed in the literature and the environmental aspects of refrigeration systems.

Maintenance assessment

The maintenance of the refrigeration system is an important part of the exploitation phase in the life cycle. The maintenance of refrigeration systems is important to ensure the machines' longer efficiency in time and avoid breakdowns and accidents. The maintenance activities are standardized (number per year, certifications for operators according to the refrigerant, procedures, reports, etc.).

Maintenance is most often evaluated quantitatively in the literature by maintenance costs. It is a complicated activity to evaluate, due to the complexity of the systems, the organizations involved, the technologies and the standards that govern them. In the literature, methods to assess the maintenance of refrigeration systems are not widespread. The next table shows the latest studies on maintenance assessment for refrigeration systems or buildings. It highlights that's the most widespread and easy method to assess maintenance is quantitative by cost analysis. another way to assess maintenance based on a qualitative approach can be considered. For example, [START_REF] Geng | A Modeling Approach for Maintenance Safety Evaluation in a Virtual Maintenance Environment[END_REF] proposed a maintenance measurement framework composed of four categories of evaluating elements. For each, an illustration assesses the element into three possible ranks (see .

Literature review on refrigeration systems design and simulation 50 [START_REF] Geng | A Modeling Approach for Maintenance Safety Evaluation in a Virtual Maintenance Environment[END_REF]. to improve the population's life quality and comfort involves more and more environmental impacts. Moreover, the augmentation of energy consumption causes the fast decrease of fossil resources. To design a sustainable product or system, it becomes a necessity to take a new environmental dimension in the development process. The first environmental impact assessment appeared in the 1960's and took various steps of the product lifetime into account (used resources, fabrication, use and recycling end-of-life).

In companies, the reflection of including environmental aspects in the design processes is often considered to reach certain objectives such as diminishing energy consumption, using more environmental-friendly materials and technologies or improving the product's recyclability. The purpose of eco-design is the reduction of the product (or system) environmental impacts during its life cycle. With the enrichment of emission databases, it is possible to assess all aspects of a system life cycle in the design stage from the material extraction to its disposal (or reuse, recycle…) through processing, manufacture, distribution, use, repair and maintenance.

Eco-design is defined in international norms (ISO 14062 2002) and by the European Environment Agency (EEA) as integrating environmental aspects during the system development process. Figure 2-10 shows the environmental aspect integration proposed by Le [START_REF] Pochat | Integrating Ecodesign by Conducting Changes in SMEs[END_REF]. Two main activities are included in the conceptual and detailed design phases: environmental assessment and environmental improvement. Ecodesign includes theory, methods, metrics, and software to enable the assessment, improvement and decision-making during each phase. [START_REF] Pochat | Integrating Ecodesign by Conducting Changes in SMEs[END_REF]Froelich 2007) Vallet et al. (2013) conducted observation in groups of experienced eco-designers and highlighted the similarity between eco-design and traditional design processes in the general structure. They can be differentiated by "the environmental assessment, the solution finding and strategy definition". The next section presents environmental impact assessment tools and how they have been used for refrigeration systems.

Environmental impacts assessment

Environmental impacts of refrigeration systems can be assessed by different indicators such as CO2 emissions and ozone depletion potential, different metrics such as CML or EcoIndicators, and different tools such as SimaPro, OpenLCA, or GaBi. This section presents multiple methods for assessing the environmental impacts of refrigeration systems.

First, Life Cycle Assessment (LCA) is widespread in the academic and industrial worlds. LCA allows having a global and complete picture of the impacts of each process in the lifecycle of a product. In 1994, the international normalization fixed the methodology and the deontology to adopt when an LCA study is conducted. From the ISO norm (ISO 14044 2006), LCA is defined as "a compilation and assessment of inputs, outputs and potential environmental impacts of products system during its lifecycle". An LCA study is conducted in four main steps described in ISO norms 14040 and 14044 and shown in Figure 2 2-11. Steps of an LCA study from (ISO 14044 2006) Secondly, the Total Equivalent Warming Impact (TEWI) is a method for evaluating refrigeration systems' Global Warming Potential (GWP) during the exploitation phase. It characterizes the CO2 emissions of a refrigeration system on the greenhouse effect. It takes into account direct impacts, such as refrigerant leakage, and indirect impacts, such as the CO2 emitted during the production of electricity required to operate the system.

Finally, the Life Cycle Climate Performance (LCCP) evaluates the GWP of refrigeration systems through the whole life cycle. In addition to the emissions calculated with TEWI, the LCCP method proposes to add the CO2 emissions due to the other phases of the life cycle, i.e. manufacturing and end-of-life treatment.

The assessment of the environmental impacts of refrigeration systems is increasingly being studied. However, there is difficulty finding comprehensive data to ensure the robustness of the results. [START_REF] Bovea | Comparative Life Cycle Assessment of Commonly Used Refrigerants in Commercial Refrigeration Systems[END_REF] evaluated the environmental impact of different refrigerants in cooling systems in a supermarket. The study compares different scenarios with three refrigeration systems: a direct system, a system with a secondary loop and a refrigerant, and a distributed system, as well as three types of refrigerants: HCFHs, HFCs and natural refrigerant. With the lowest potential environmental impacts, it appears that the use of a natural fluid is the most suitable fluid to replace HCFCs and HFCs.

In a more recent study, Petersen et al. (2019) investigated the LCCP of different refrigerant architectures: a direct expansion system using R448A, a CO2 transcritical booster and an R290 self-contained system. They compared the systems configurations in different cities and observed that the main impacts came from the electric mix of the region. For example, in countries that use mainly hydroelectric power, the impacts are lower than those that use mainly fossil fuel for electric production. They also highlighted that the direct expansion systems consumed around 7% less than the CO2 transcritical booster and R290 system. Table 2-5 summarizes a few existing papers on environmental impact assessment for cold applications. A general conclusion in these studies is the importance of the systems energy consumption towards climate change indicators. Indeed, it is the most influencing factor during the use phase of the system lifetime. The environmental impact assessment of complex systems, such as refrigeration systems, has some limits highlighted in different studies: it is time-consuming, a lack of studies and available data, and a lack of transparency. [START_REF] Papalambros | Distributed Cooperative Systems Design[END_REF]. The design decisions are taken based on several criteria' analysis, maximisation or minimisation, sometimes conflicting [START_REF] Stadler | Multicriteria Optimization in Engineering and in the Sciences[END_REF]. The mathematical representation of the decision objectives to fulfil is called optimisation.

Multi-disciplinary optimisation (MDO) is an engineering field composed of approaches, methods, and tools to solve design problems of systems with many domains such as automobile, building, space.

Interaction representation

In a multidisciplinary system, such as a refrigeration system, a discipline models a sub-part of a system with equations or simulations, and links input and output variables. For refrigeration systems, various disciplines as energy, economy, environmental impacts, lifetime, ease of installation need to be considered.

MDO can be summarized as developing strategies from current analysis tools and optimization techniques that help the engineers make the best decision during the design process to obtain optimized complex products or systems. Several types of graphs to model the interactions between disciplines could be considered to ensure relevant modelling of the system to optimise. They allow the identification of the connections between the disciples and the optimisation of mathematical models. Design structure matrix (DSM) (see Figure 2-12) provides a clear and readable representation of the system to optimize (see [START_REF] Eppinger | Design Structure Matrix Methods and Applications[END_REF]). It is adapted to physical systems, organisations and processes. If an interaction exists between the disciplines, a cross is displayed in the table. If there is no interaction between the two disciplines, the box is blank. The black circle means that it is the same discipline. The interactions can be sequential, parallel or coupled (see Figure 2 -13).

Multi-objectives and multi-disciplinary optimisation principle

After examining and clearly showing the interaction between disciplines or criteria, a mathematical function is established to determine the values of the unknown variables, called design variable X, that minimize or maximize the objective function f while satisfying the constraints C. The design space is composed of all the feasible solutions (points) satisfying this problem. The need for optimisation methods appears to reduce this space to optimal feasible points.

A mono-objective function can be described in Eq .

{ 𝑚𝑖𝑛 𝑥∈𝑋 𝑓(𝑥) (𝑜𝑟 𝑚𝑎𝑥 𝑓(𝑥)) 𝑤𝑖𝑡ℎ 𝑥 ∈ 𝐶 (2-1)
These methodologies have been extensively studied. Two classes of design optimisation can be cited: mono-objective optimisation that gives a single point as the result of the process and multi-objective optimisation. The problems are multi-disciplinary and multi-objectives (or multi-criteria) in real-life cases. Thus, the optimisation functions are multiple f1, f2, … ,fn. The multi-objective problem can be summarized in Eq (1)(2).

{ 𝑚𝑖𝑛 𝑥∈𝑋,𝑖=1,2 … .𝑛 𝑓 𝑖 (𝑥) (𝑜𝑟 𝑚𝑎𝑥 ) 𝑤𝑖𝑡ℎ 𝑥 ∈ 𝐶 (2-2 )
The development of multi-criteria optimisation has permitted the exploration of solution space and allows the decision-maker to compare several designs. When a solution cannot be decided as better than another, compromises must be made among a set of non-dominated solutions called Pareto frontier and Pareto set (See Figure 2 -14). However, in this case, the design space needs to be fully explicit and understood by the users involved in the decision process.

Design problem resolution

Optimisation methods are used to resolve design problems by highlighting a set of feasible points in the design space. However, different approaches can be applied depending on the design problem and the stakeholder involved in the process. The decision-maker's preferences are taken into account in three different ways in the decision process: a priori, a posteriori or iteratively (Korhonen, Moskowitz, and Wallenius 1992). In an a priori method, the decisionmaker puts his preferences before the optimisation process, for example, weighted sum, minmax, goal programming methods. In a posteriori and iterative methods, the preferences are considered after the optimisation process once the solutions are visualized. In this section, a quick overview of the existing methods is presented.

Point-based design

Deterministic methods are traditional processes where the solutions are particular points in the design space. In this approach, the iterative trade-off process is point-based. For example, a multi-objective optimization for supermarket refrigeration systems was conducted by [START_REF] Glavan | Cost Optimisation of Supermarket Refrigeration System with Hybrid Model[END_REF]. They consider various discrete events: consumer interactions, defrosting schedule, and product refilling. This work aimed to optimise the supermarket total energy costs by constraining two criteria: defrosting schedule and energy optimization actions. They compare the results of different simulations to conclude on the parameters influence. It gives preliminary results before the implementation of the system. However, a limitation of this method highlighted by several works [START_REF] Sobek | Toyota's Principles of Set-Based Concurrent Engineering[END_REF] is the restriction of the design space to an acceptable but not Pareto-optimal solution because of the specifications. The lack of know-how to list all the possible alternatives can result in the limitation of the tested alternatives and thus the design space.

Set-based design

Concurrent engineering (CE) and, more specifically, set-based design (SBD) can prevent the limitation of deterministic approaches by restricting the design space concurrently between the actors. All the decisions are taken in parallel to reduce the design space toward feasible points that fulfil all the objectives. Uncertainty reduction theory reduces the uncertainty through the iterative reduction of the variables range and the decision-maker knowledge gain [START_REF] Canbaz | Improving Process Performance of Distributed Set-Based Design Systems by Controlling Wellbeing Indicators of Design Actors[END_REF]. In the early design stages, constraint problem (CP)

techniques are used to support SBD and uncertainty reduction as it places the decision-maker at the centre of the problem by formalizing the decision in the form of constraints. CP techniques help define consistent feasible design points (Yannou et al. 2005;2013). These approaches reduce the design space progressively and converge to one or multiple feasible solutions.

Design space exploration

The common tasks of designing a product or a system based on optimization methods can make the designers frustrated from the results (Balling 1999). Design space exploration (DSE) can be used to quickly generate and simulate a set of feasible solutions, optimise if metrics are available to compare design solutions, find the architecture, and dimension the components that satisfy the design constraints [START_REF] Kang | An approach for effective design space exploration[END_REF]. DSE can help the designer explore an extended design space by gaining knowledge on the possible alternatives. He can then choose the best solution based on the visualization of all the possibilities (Abi Akle, Yannou, and Minel 2019). The thesis's proposed work is based on the design space exploration approaches. These are further developed in the contribution chapters.

Software and other digital tools have been developed in the last decades to help the simulation and design optimization of refrigeration systems. The next section provides an overview and comparison of some existing tools.

Design and simulation of refrigeration systems

As described previously, refrigeration systems in a supermarket are responsible for almost half of the yearly energy consumption. Therefore, they have a great potential for optimization.

According to audits carried out in European countries (UK, Italy, Switzerland, Denmark, Bulgaria and Belgium) by Evans and al. (Evans et al. 2014), many issues are responsible for energy loss. (Evans et al. 2014) Many tools have been developed to support the design of optimal refrigeration systems and the understanding of existing and new refrigeration technologies. In the next section, the system performance assessment in industrial engineering is discussed, and a list of refrigeration system performances is defined based on the literature and the industry observation. Then, a comparison of different tools is made to identify their shortcomings.

Refrigeration system design problem setting

In this section, the specifications needed to design a refrigeration system are briefly presented, and the criteria or objectives are discussed.

Design of a refrigeration system

To design a refrigeration system, the steps to follow are the typical steps of a design process presented in section II.3. Refrigeration systems can be sized according to cold demand, operating conditions and location (weather conditions, electricity mix…). They can be either built from scratch or can be modified from an existing system. In this case, it is important to assess the interest of this modification beforehand.

They are assessed mainly by technical, environmental and economic performances, but other performances are proposed in the next section.

Different choices are to be done on the configuration to design a refrigeration system. In addition to the dimension of the evaporators for the frigorific power need, the designers choose the architecture (see examples in section 1.) and the components associated. It mainly comprises the type and number of compressors, condensers, expansion valves, and evaporators. Depending on the client's specifications, it also counts the pipeline configuration, the electronics, recovery bottles, and other specific components.

Design criteria and performances

Typically, the design criteria for a system user, for example, a supermarket manager, are the system's costs and energy consumption. In the literature, the simulation and optimization of these two criteria are widely studied (Ge and Tassou 2011a;Acha, Du, and Shah 2016;Ben-Abdallah et al. 2019;[START_REF] Gullo | Energy and Environmental Performance Assessment of R744 Booster Supermarket Refrigeration Systems Operating in Warm Climates[END_REF][START_REF] Mitsopoulos | Annual Performance of a Supermarket Refrigeration System Using Different Configurations with CO2 Refrigerant[END_REF]. For example, Ge and Tassou (2011a) assessed the total energy consumption as the sum of the energy consumption of the various subsystems. They showed that the model can carry out hourly system simulations and can be used to investigate the influence of component design and optimize with a priori method the overall system energy consumption. The model was validated by data collected in an instrumented supermarket.

However, for a supplier and considering the whole life cycle of a refrigeration system, costs and energy consumption are not the only performances to consider when deciding. Indeed, one can add the environmental impacts, the maintenance, the operator knowledge, the risk, etc. Several criteria should be satisfied to design a refrigeration system based on quantitative and qualitative performances. In engineering design, Ulrich and Eppinger defined a product performance as « how well a product implements its intended function. Typical performance characteristics are efficiency, life […], energy consumption. » (Ulrich and Eppinger 2016). The definition of performances for refrigeration systems design problem is discussed in Chapter 5.

Quantitative performances are defined as outputs that can be calculated or estimated through mathematical models:

 Energy consumption: quantity of energy consumed by all the refrigeration system components (compressors, pumps, fans...)

 Environmental impacts: GWP, ODP, acidification, eutrophication, resources depletion, ecotoxicity, human toxicity...

 Economic: different costs of the system (components, installation, maintenance, refrigerant, energy use, etc.)

 Space used: space used by the refrigeration system components, the pipelines, the fans, etc.

Qualitative performances are the ones evaluated qualitatively (description and evaluation according to qualitative criteria):

 Installation conditions: ease of installation of the components and the system and necessary operator's knowledge  Maintenance condition: conditions for the maintenance of components (necessary training of the operators, ergonomic of the machine, accessibility, etc.)

 Technology Readiness Level (TRL): technology maturity, to address the reliability of the selected solution. The exploiting risk increases as the TRL of technology lowers.

 Temperature maintenance capacity: capacity of the system to maintain the temperature through the demand peaks.

 Lifetime: system physical component's lifetime (compressor, condenser, expansion valve, pump…)

The next section compares tools based on the list of criteria or performances. We try to identify the existing gaps related to these platforms.

Comparison of different design and simulation platforms

An overview of existing digital tools for the design and simulation of refrigeration systems is presented in the appendix. The tools can be used for industrial refrigeration system planning (HVACR) and research purposes. This section aims to compare the existing tools for the design of refrigeration systems and identify their main specification and scope. These tools are commonly used, in France or abroad, by refrigeration engineers, designers, consultants and researchers to assess the various possible options.

Literature review on refrigeration systems design and simulation 61

In  Type of licence: free, open-source, or the user needs to pay for its use.

 Scope of the tool: type of architectures and components modelled in the tool.

Outputs

Table 2-7 presents the performances calculated by the different tools. The performances assessed in the design and simulation platforms previously presented are compared.

The platforms provide the user with different performances, quantitative or qualitative. The main results displayed are energy consumption and frigorific power for all of them. Moreover, installation and maintenance costs are often calculated as there is important information for the user to make a decision. The third most common performance is the environmental impact. They are mostly calculated based on the TEWI indicator, which is easy to calculate. The notions of ease of installation, technological maturity and know-how are often put aside because they are very complicated to consider in a classic optimisation approach and the early design stages, i.e. the system is not yet in place.

The interest of this type of platform is to provide users with some tools to dimension a system.

However, users may lack an integrated approach to the design problem, i.e. taking into account several performances simultaneously. The main gap highlighted in the presentation of these platforms for the design and simulation of refrigeration systems is the top-down simulation approach: one needs to first formulate the design problem (inputs), then execute the algorithm implemented in the tool to visualize the results. If the user is satisfied with the results, he selects the solution, and if not, he reiterates the same steps. These tools do not allow users to interact simultaneously between the performances and the system design.

Synthesis and Research gaps

The literature review allowed us to identify research works related to our problem and highlight the main research gaps related to the design and simulation of refrigeration systems toward optimal systems.

The review of refrigeration system design provided an overview of the refrigeration system composition, the performances studied in the literature and industry, and existing simulation tools to support the design of refrigeration systems. The review in system engineering points out the need to understand the system structure and its behaviour in its exploitation environment and the design (or decision) process.

Some limits or research gaps can be listed:

-Research gap 1: Lack of a holistic representation of the refrigeration socio-technical system.

To provide a relevant answer to improving the design process toward optimal solutions, it is necessary to represent and understand how refrigeration systems are built, who takes decisions, how and when in the process decisions are taken, i.e., understand the refrigeration socio-technical system.

Very few works can be found on a holistic representation in the literature.

-Research gap 2: Lack of an integrated approach Refrigeration systems have been widely studied in the literature. The performances of new and existing technologies are evaluated to characterize the system behaviour in use cases, i.e. mechanical and thermodynamics study of the systems; assessment of energy and environmental performances; cost assessment. These models and tools have been developed to support the design of refrigeration systems. However, the review of the tools highlights the lack of tools that could propose an integrated approach. It means that it misses to the users a complete view of the performance of a refrigeration system simultaneously and in the early design phase (detailed and embodiment design).

Indeed, industrial performances are necessary to compare different technologies in real conditions.

The missing performances are mainly qualitative performances such as ease of maintenance and technological maturity and quantitative performances such as cost for different architectures and environmental impacts. The performances also need to be assessed simultaneously in the same supporting tool for an optimal system design.

-Research gap 3: Lack of user integration in the design process Digital tools such as the simulation platforms presented in section 3 have shown their usefulness to perform simple and repetitive tasks, such as choosing components from a catalogue, calculating

Literature review on refrigeration systems design and simulation 65 thermos-physical property data, and dimensioning a system based on energy consumption.

However, the solution proposed is represented by a single point in the design space, as in a classical design process. If there are hundreds or thousands of possible solutions, this approach is irrelevant.

Indeed, it can be a time-consuming and tiresome activity for the user, and the obtained solutions depend on the designer's expertise (Tweedie et al. 1996). Moreover, as explained in the literature review, the traditional design process can frustrate users with the results obtained. Indeed, he is not a part of the process, i.e. intervention at the beginning and the end.

Based on this literature review, it is possible to identify the different approaches and design artefacts of system engineering and industrial design that are relevant to fill the research gaps. Research questions and objectives were defined to answer each research gap, and a relevant methodology was proposed, discussed and explained in Chapter 3.

Research gaps, research questions and general methodology

This chapter describes the relationships between research gaps, research questions and objectives. The general methodology of the thesis is discussed. Finally, the positioning of our work between research disciplines and methodologies is presented.

1. Research gaps and questions

1.1.

Research gaps

The literature review highlighted some following research gaps:

-Research gap 1: Lack of a holistic representation of the refrigeration socio-technical system (see Chapter 2 for the definition).

-Research gap 2: Lack of an integrated approach in the design process of refrigeration systems, i.e. some performances are missing to ensure a relevant decision process, simultaneously and in early design stages.

-Research gap 3: Lack of users' integration in the design process, i.e. a top-down approach where the solution is a single point in the solution space, and the users are not part of the process.

Research questions

From the research gaps, two research questions emerge:

Research question 1: How to represent and assess the refrigeration socio-technical system?

This research question is related to the first research gap previously defined. The first research question implies two objectives. The first one is to provide a structured representation of the refrigeration socio-technical system. In order to answer the first research objective, knowledge on the domain and the technical tools need to be gathered, and the stakeholders and the activities related to the lifecycle of a refrigeration system need to be identified. The second objective is to confront our hypothesis and research gaps by evaluating the current improvement opportunities in the refrigeration socio-technical system. A domain diagnosis is conducted to collect data.

This question leads to two objectives:

Objective 1.1: Establish a structured book of knowledge described as a synthetic, informative and graphically illustrated report of the existing refrigeration socio-technical system.

Objective 1.2: Assess the current improvement paths in the refrigeration socio-technical system with a suitable methodology.

A proper methodology to answer this research question is discussed in the next Chapter.

Research question 2: How to support decision-making for the design of refrigeration systems in early design stages and in the most integrated way?

We have highlighted that the supporting tools and the design process usually established are topdown (descendant) and point-based. Thus, in the present work, the lack of an integrated approach (research gap 2) and the lack of users' integration in the process (research gap 3) are addressed. It means providing all the performances simultaneously to the decision-maker in the early design phases. First, a knowledge model defines the decision problem, i.e. a formal representation to ensure that all necessary information is integrated into the problem. Then, a platform for design space (solutions and performances) exploration is implemented to provide the users with a quick refrigeration system solution and allow the user to explore "what-if" scenarios. Appropriate verification and validation framework are constructed.

The second question leads to two objectives:

Objective 2.1: Build a knowledge model with a structured language (ontology) for the design of refrigeration systems.

Objective 2.2: Integrate this knowledge model into a multi-objective simulation and design space exploration platform for supermarket refrigeration systems.

Research methodology and positioning

Research framework

In academic research, it exists different types of scientific approach: descriptive or analytical; applied or fundamental; qualitative or quantitative; conceptual or empirical; simulation or laboratory; etc.

(see [START_REF] Kothari | Research Methodology: Methods and Techniques[END_REF] for more details).

This Ph.D. thesis, as applied research, uses both descriptive and analytical methodologies through the assessment of existing information in literature (analytical research) and the grounded investigation by conducting surveys and diagnosing refrigeration socio-technical system (descriptive study). The results are mainly qualitative as we are interested in investigating and improving the design process (human behaviour, steps, tools, etc.) while using quantitative measurements to describe the behaviour of a refrigeration system. Conceptual research theories are used to develop a conceptual framework, and empirical work serves to observe how the approaches are perceived in the field. We can synthesize our research framework by defining it as a multi-field research approach.

Design science has discussed different research frameworks [START_REF] Blessing | DRM: A Design Reseach Methodology[END_REF][START_REF] Tie | Grounded Theory Research: A Design Framework for Novice Researchers[END_REF][START_REF] Cross | Research in Design Thinking[END_REF][START_REF] Glaser | The Discovery of Grounded Theory; Strategies for Qualitative Research[END_REF].

We From the DRM, the chosen methodological framework for our work is presented in Table 3-

Positioning of the thesis

This Ph.D. thesis is at the crossroads of academic research and the refrigeration industry (see Figure 3-3).

The refrigeration industry, more particularly commercial refrigeration, is our field of application.

Contact with the industrial and regulatory actors has allowed us to collect several qualitative and quantitative data on the field of refrigeration. This industrial knowledge is necessary to understand the design process dynamics and validate our approach.

Our work uses methods, tools, and contributions from two disciplines, refrigeration process engineering and industrial engineering within the academic research. The approach adopted is multidisciplinary and multi-methodologies. Industrial engineering proposes tools to understand the design, installation, exploitation, lifecycle management of systems, products, humans, materials etc.

A more detailed definition is given in Chapter 2. Several methodologies are combined to propose a relevant solution to our problem from industrial engineering. Design Theory and Methodology (DTM), Model-based System engineering (MBSE), design process theory, and decision aid theory and methodology are considered

The refrigeration process engineering provided models and data. It includes multi-physics models of refrigeration systems, thermodynamic, physical and mechanical models, the physical structuration of system components and the system behaviour. Finally, a significant amount of parameters and data related to the operation of the systems were collected, in particular, to verify the obtained results.

Figure 3-3. Positioning of our research in the industry and academic research

In the next chapters, the contributions of this thesis are presented.

Socio-technical diagnosis of refrigeration systems in France

This chapter resumes the field diagnosis conducted to better understand the refrigeration domain in

France. The diagnosis first consisted in exploring the refrigeration socio-technical system based on:

interviews with interlocutors of different roles, companies, knowledge; studies of the laws, standards, and regulations specific to the field as well as the guides of good practices; a collection of literature data on refrigeration technologies, evaluation of the performances; observation of running systems as well as in the process of design by starting from a customer need until the end of life of the machine.

We then used the Radical Innovation Design (RID) methodology to organize this data collection in order to construct a knowledge book systematized in four categories which are the four dimensions of RID: stakeholders (called user profiles) who encounter issues (called problems) during their activities, themselves segmented into life cycle phases (called use situations) and finally that of existing solutions that stakeholders can apply to reduce or eliminate a problem in a given activity.

Seven matrices are constructed using the 4-dimensional categories. Then, these matrices are computed to determine where to simultaneously address the creation of expected values for the stakeholders involved and address orphaned value buckets. These value buckets are defined as the main problems encountered by key stakeholders in frequent use situations. Several indicators are then calculated to allow an in-depth analysis: first, the matrices of the value pockets, and then a series of effectiveness indicators to assess the individual capacity of each solution to eradicate the identified problems.

A conclusion toward a solution is finally proposed.

Exploring the refrigeration socio-technical system

Numerous regulations and laws govern refrigeration systems (RSs) to curb their high environmental impact [START_REF] Aprea | An experimental evaluation of the greenhouse effect in the substitution of R134a with CO2[END_REF][START_REF] Islam | Assessment of total equivalent warming impact (TEWI) of supermarket refrigeration systems[END_REF]. Managing an RS from its design to its end-of-life is complex, owing to the number of disciplines, stakeholders, roles, and skills involved. The present field study chose the retail refrigeration sector because of its magnitude. This sector represents 3-5% of energy consumption in European countries and is expanding to meet growing demand. In France, retail stores have seen their footfall increase by 10% in 5 years and their number by 8%. Refrigeration equipment (display cabinets or cold rooms) is responsible for 35-50% of the energy consumed by a retail store.

Studies of RSs in retail have mainly focused on developing tools and technologies [START_REF] Ben-Abdallah | Experimental investigation of the use of PCM in an open display cabinet for energy management purposes[END_REF], optimizing one or multiple technical performance metrics, especially in the design stage under simplified conditions (Ge and Tassou, 2011;[START_REF] Kolokotroni | Supermarket Energy Use in the UK[END_REF], implementing performance indicators to improve energy efficiency (Acha et al., 2016), and understanding how an RS operates in a supermarket [START_REF] Bahman | Analysis of energy savings in a supermarket refrigeration/HVAC system[END_REF]. CO2-based technologies and other energyefficient solutions such as heat recovery are widely popular as an advantageous alternative system in the retail industry. Studies have shown that CO2-based systems have a high potential for enhancement [START_REF] Gullo | Transcritical R744 refrigeration systems for supermarket applications: Current status and future perspectives[END_REF]. However, non-technical issues from the socio-technical RS generate a range of problems and lead to sub-optimal system performance. Here we define the socio-technical RS as the set of stakeholders, processes, organizations and technical solutions Law ( 2009) defines a socio-technical system as the infrastructure, stakeholders and their roles, technology, regulation, and performance of a system. In [START_REF] Hess | Sociotechnical matters: Reviewing and integrating science and technology studies with energy social science[END_REF], socio-technical systems are defined as "heterogeneous ensembles of people, artefacts, infrastructures, research, cultural categories, standards and laws, and natural resources". In [START_REF] Sovacool | Climate change and industrial F-gases: A critical and systematic review of developments, sociotechnical systems and policy options for Socio-technical diagnosis of refrigeration systems in France 107 reducing synthetic greenhouse gas emissions[END_REF], a sociotechnical approach is used to address climate change challenges from fluorinated greenhouse gases (F-gases). These authors do not merely consider F-gases as by-products; they also address the framework of infrastructure, social institutions and the products that use the gases. In the present contribution, a socio-technical RS includes the infrastructures from the RS design through to its final disposal, the RS operating environment, stakeholders throughout its lifecycle phases (use, maintenance, technical update, end-of-life), the technical components of the RS (e.g., compressor, condenser, refrigerant), and the laws and standards related to the RS and its performance.

Our research objective was to identify major areas for improvement of the activities related to the socio-technical RS and the stakeholders' roles. We drew up a prioritized list of improvement paths from a structural diagnosis to innovate in a more integrated design strategy and platform. The purpose of the Radical Innovation Design (RID) methodology (Yannou, 2015;[START_REF] Yannou | Capturing the relevant problems leading to pain-and usagedriven innovations: The Dependency Structure Modeling Value Bucket algorithm[END_REF] is to analyze a complex activity, determine its improvement paths (called "value buckets"), and use them in an innovative design or management process to increase the performance of that activity.

To analyse the often low environmental performance of buildings, Lamé et al. ( 2017) used RID methodology to analyze the defects in the socio-technical system of specification-designimplementation-use-maintenance of a building the fragmentation in the involvement of the actors, which explained why environmental performance was poor. They were able to show difficulties in implementing lifecycle assessment (LCA), eco-design approaches, and environmental standards, together with the influence of the lack of consultation among the players in this value chain. Lamé et al. ( 2018) analyzed the activity system of a dental radiologist to derive value buckets from which they ideated, going on to define innovative socio-technical layout solutions. [START_REF] Bekhradi | Categorizing user pains, usage situations and existing solutions in front end of innovation: the case of smart lighting project[END_REF] investigated do-it-yourself activities to seek an innovative solution for a universal accent light. The utility of following a RID process is discussed by Yannou et al. (2013), and the details of the RID process are given in [START_REF] Yannou | Capturing the relevant problems leading to pain-and usagedriven innovations: The Dependency Structure Modeling Value Bucket algorithm[END_REF].

We used the RID methodology to build a cognitive model of the activities related to a socio-technical RS and analyze it to delineate major areas for improvement. The RID process first requires a thorough investigation of a socio-technical RS, yielding a first contribution: knowledge gathering on sociotechnical RSs in France. The RID process then segments this knowledge into four dimensions, with categories of stakeholders (called "user profiles") who experience difficulties or lack of performance (called "problems") during their activities, themselves segmented into lifecycle stages (called "usage situations"). The fourth dimension consists of the existing solutions that stakeholders may apply to mitigate or remove a difficulty in a given activity. In an innovation approach, emphasis is placed on value buckets (VBs), for which existing solutions are of little or no help. The second contribution of this work is to identify and prioritize improvement areas in a socio-technical RS using two RID 2. RID methodology and process of this study

More on the RID methodology and process

At a macro level, the RID process has three stages (see Figure 4-1):

 Observing the socio-technical system and learning about its current activities, shaping it into a cognitive model,  Exploring this cognitive model and deciding on the innovation targets,  Ideating, designing, and checking that the innovative solutions effectively enhance the activities' key performance indicators.

Here, the authors detail the two first stages for a socio-technical RS. 1) and ( 2), defined once and for all (for building the cognitive model of current activity), a problem-solving process ("ideate and design") and an "explore and decide" step comprising two decision-making stages and tools, namely RID Comparator to visualize effectiveness ratios and shrink the market space (i.e., the user profiles, usage situations, and problems) (3), and RID Compass to visualize value buckets and decide the ambition perimeter ( 4) before moving on to ideation, i.e., starting creativity. The value buckets selected in the ambition perimeter are qualified questions to initiate ideation. After imagining a dreamt or ideal usage scenario ( 5), new innovative solutions are designed with a new product-service-organization (PSO) architecture ( 6) and a business model (BM) [START_REF] Sun | Experimental investigation on twin screw refrigeration compressor with different capacity control methods[END_REF]. Finally, a prototype is made [START_REF] Ben-Abdallah | Experimental investigation of the use of PCM in an open display cabinet for energy management purposes[END_REF], experimented with in real or virtual conditions to assess the activity's performance. RID Comparator is then used again to check the dominance of the innovative solutions within the targeted market space. The most important terms are defined in Appendix 1.  RID Comparator answers the question, "What is the value of solutions (existing and novel)

regarding the potential to improve the activity, i.e., eradicate quantities of pains?" Several types of effectiveness indicators are computed to compare the solutions.

 RID Compass answers the question, "What innovations merit development?" within the shrunk market space. Only a subset of strategic value buckets is selected in the ambition perimeter and serves as qualified ideation questions. Developing a solution from a value bucket guarantees added utility for avoiding or limiting problems during usage situations where competitors are, on average, poorly effective. It precisely orients toward "blue oceans" as described in Blue Ocean Strategy [START_REF] Kim | Value innovation: a leap into the blue ocean[END_REF].

In the present work, we do not provide the algorithmic details and formulas to compute decisionmaking indicators. The reader will find them in [START_REF] Yannou | Capturing the relevant problems leading to pain-and usagedriven innovations: The Dependency Structure Modeling Value Bucket algorithm[END_REF] and [START_REF] Lamé | Usage-driven problem design for radical innovation in healthcare[END_REF]. If no publications were found on commercial socio-technical RS analysis, the study's scope was extended to similar systems such as household refrigerators, buildings and energy systems [START_REF] Cagno | Industrial sustainability performance measurement systems: A novel framework[END_REF][START_REF] Hesloin | Consideration of value chain actors in the analysis of environmental performance for eco-design and incentive for sustainable behavior : case study of energy-using products road transport refrigeration units[END_REF][START_REF] Lamé | Ecodesign tools in the construction sector: Analyzing usage inadequacies with designers' needs[END_REF][START_REF] Mignon | System-and actor-level challenges for diffusion of renewable electricity technologies: an international comparison[END_REF].

Fifteen interviews in different companies and disciplines were conducted to collect qualitative data.

The interviewees were chosen to be representative of the stakeholders of a socio-technical RS, at different levels of hierarchy, according to their activities and their availability. The list of the interviewees are presented in Table 4-1. Each interview lasted about one hour and was semi-guided (open questions were asked). The interviews covered six topics: (i) identity (role, background, position along the value chain), (ii) current regulation and anticipation, (iii) the technology of the systems (limits, skills, and development),

(iv) the design process, (v) maintenance, and (vi) sustainability positioning (also social and economic concerns). The description of each topic is presented in Table 4-2. Table 4-2 3. Knowledge book of the socio-technical RS

RS lifecycle process

Based on the data collected during the first step of interviews and a literature review, we ascertained that the main life-cycle stages of an RS were similar to any typical product life-cycle stages (Saaksvuori and Immonen, 2008;[START_REF] Stark | Product lifecycle management[END_REF]

Users involved in the socio-technical RS

Since the signing of the 1987 Montreal Protocol, changing existing installations has proven difficult.

In addition, from 2015, the European law on F-gases regulates refrigerants, which are to be replaced by naturally occurring gases in an industrial plant by 2030. Experience, knowledge and expertise must thus all be rebuilt in the next decade. This evolution needs flexibility in companies to respond to the new regulation. This section provides knowledge of the current stakeholders' organizational process.

The semi-guided interviews provide answers concerning the stakeholders' interactions, the timeline of the process, what their activities are, and the problems they encounter.

The interviews highlight the fragmented process of managing an RS. The interviewees had different images of the overall socio-technical system, as information is widely scattered, for example, on the tasks and activities specific to each stakeholder or available infrastructure such as EOL treatment choices. They nonetheless answered unanimously on the key roles, the constraints of adapting to the actors they interacted with (budget, deadlines, and company's mindset), and the sector's evolution.

EU parliament and Council

Organization which votes EU directives on frigorific equipment

Design office

Person or industrial team that provides RS design recommendations and design the system (ex: teams in companies such as Axima, FAC SOS)

Consulting firms Person or industrial team that provides RS design recommendations (ex:

MF consulting firm)

Refrigeration engineer

Person or industrial team that installs RS, maintain, manage the EOL and build some RS components (ex: Persons working for suppling companies at FAC SOS or INRAE's refrigeration engineers)

Supplier

Person or industrial team that provides RS installation, RS maintenance and RS EOL management services (ex: FAC SOS company)

Components designer

Person or industrial team that designs RS components or system from functional specification (ex: BITZER's study officer)

Manufacturer

Person or industrial team that builds RS components or systems (ex: BITZER)

Final user

User of a RS to maintain cold for refrigeration of food in cold rooms and display cabinets or user of HVAC systems (ex: Retail stores, Commercial kitchen, cold food storage such as Carrefour, Brioche Dorée)

Building design parties

Individuals or organisations that are involved in the design, construction and life cycle management of the building (ex: Architects cabinets)

EOL treatment parties

Individuals or organisations involved in the EOL treatment of RS (ex:

recycling company such as EcoSystem)

As refrigeration systems are complex systems to design, install, use, and manage, the number of stakeholders involved is important. Thus, the actors' interactions and dependencies are numerous, which can be complex because of a particular business language. We use first a Dependency  4: actor "i" is essential to the existence of actor "j"  3: actor "i" can jeopardize the accomplishment of actor "j"'s missions  2: actor "i" can jeopardize the success of actor "j"'s projects  1: actor "i" can challenge the management processes of actor "j" in a limited way in time and space  0: actor "i" has little influence on actor "j"

The total scores of each stakeholder are visualized in the influence/dependency matrix. Insights on the dependency and influence of each stakeholder are generated, and it becomes possible to develop a specific approach and strategy for the identified stakeholders.

Figure 4-5 shows the graph generated. As expected, politics have a real impact on the evolution of refrigeration systems by defining regulation as well as technological transfer centres that help new technological clusters market-ready. They are placed as the most influencing stakeholders. We can observe that EOL parties are highly dependent stakeholders as they depend on the willingness of other actors to manage the waste of their system. Design offices and refrigeration engineers, as in charge of designing and managing the refrigeration system, are also very dependent on the other actors. Indeed, they have as much flexibility as given by the final user requirements and constraints.

Relay actors are defined as stakeholders that are an active part of the general process, both influencing and dependent of the process' outcomes.

INFLUENTS

RELAY DEPENDENT AUTONOMOUS

The twelve identified user-profiles and their direct interactions are summarized in an interaction diagram in Figure 456. The users inside the dashed perimeter are the actors directly involved in designing and exploiting an RS.

During the design stage, the final user of the RS (e.g., a supermarket) interacts only with one actor: a supplier. The supplier, whose role is to link manufacturing to final use, is the most participatory stakeholder. The supplier also has the specific characteristic of being involved at every lifecycle stage.

Once the equipment is installed and running, the only stakeholders concerned by the exploitation stage are the final user (as the daily user) and the refrigeration engineers via the supplier for maintenance tasks. In the final stage of the system lifecycle, the activities of EOL treatment parties are limited to the transport and treatment (dismantling, recycling, disposal) of the machine. The refrigeration technicians from the supply company oversee the draining of the refrigerant from the equipment. The system's final user is involved from time to time in the EOL of the system, but this is mostly the supplier company's responsibility. Around the project, a set of parties will write

Problems identified in the socio-technical RS

Problems in RID are defined as "issues or lacks performance experimented by users during usage situations belonging to an activity field" [START_REF] Yannou | Adapting the FBS Model of Designing for Usage-Driven Innovation Processes[END_REF]. The problems presented in this section stem from the interviews, observations, and literature review. They are either specific to one or common to multiple usage situations. We collected the issues arising in the design and the exploitation phases from the interviews. The end-of-life (EOL) treatment phase gaps are documented by [START_REF] Ardente | Analysis of end-of-life treatments of commercial refrigerating appliances: Bridging product and waste policies[END_REF] and updated with the experts' interviews. A holistic representation of the problems is constructed during the data treatment and to better understand the underlying phenomenon. Causality graphs serve to organize the data collected into three areas: causes, consequences, and problems. Such graphical representation has been made for each of the usage situations. Figure 4-7 shows the causal graph for the design, manufacture, and installation phase. We then placed each of the problems into four main categories: costs (orange boxes in the figures), knowledge management (blue boxes in the figures), the interaction between stakeholders (red boxes in the figures), and tools (green boxes in the figures). This problems' categorization is adapted from [START_REF] Pärttö | Explaining failures in innovative thought processes in engineering design[END_REF], and the fifteen main problems highlighted in this study are summarized in Table 4-4.

Cost issues

There is a need for rapid change in the systems to respond to the evolution of regulation. The leaders of the refrigeration market (the five largest companies represent 42% of the market) structure the technical advances. A small company will have more difficulty recruiting competent operators for these new developments and keeping up with the latest changes. The lack of structural adaptability in companies makes the risk of high costs inevitable. In the exploitation phase, the risk increases with the likelihood of a component's obsolescence, human error, and refrigerant leakage. The impact is an increase in costs, mostly for the final user. Moreover, the lack of knowledge about the ability of EOL parties to treat their waste sometimes leads to waste being left on-site and so means a poor recycling rate. During the exploitation, breakdowns are frequent but not fatal as the system functions remain robust. They can be mechanical, electrical, or design-related breakdowns and lead to high repair costs or loss of frozen food. The system's electricity consumption is the most critical operational cost mainly for maintaining systems and energy loss (Evans et al., 2014). small or medium companies, with a falling number of applications. New technological clusters are often not fully understood in all the usage situations as there is no time to keep up with the changes [START_REF] Minetto | Non-technological barriers to the diffusion of energy-efficient HVAC&R solutions in the food retail sector[END_REF]. For example, as CO2-based technologies are deployed, the operators accustomed to HFC systems are faced with pressurized equipment that is new to them.

Knowledge management issues

Interaction issues between stakeholders

Multiple problems of communication between stakeholders were found. The interviews emphasized that a change in regulation results in a disturbance of the final user's advice process. For example, the manufacturer is now responsible for the whole lifecycle process for air conditioning. Refrigeration engineers and operators work for the manufacturer and not a supply company. As adaptation times to new standards are not properly allowed for the final customer demands, the recommendations are not optimal. During the design phase, the frequent problem is that the final user specifications given to the supplier can constrain the design space. During the exploitation phase, either during regular use or maintenance, customers can hear an uncomfortable noise. This problem is not frequent and does not significantly damage the users but can harm the company's image. Refrigerant use can cause human health problems (toxic or flammable refrigerant) or require high-pressure equipment such as CO2-based systems. Accidents, though extremely rare, can be fatal.

Tool issues

Most design offices are subject to a strong time constraint during the RS design phase, leading to sub-optimal design space exploration. More than a hundred tools exist to design an RS, but they are not considered entirely efficient. Most companies use LCA to calculate the environmental impacts of the systems. The exploitation and the EOL treatment phases carry a greater risk of higher negative impacts due to frequent refrigerant leakage and high electricity consumption, resulting in high indirect impacts, as shown in a preliminary study presented in [START_REF] Salehy | Life cycle assessment of hypermarket refrigeration system: effet of location and choice of architecture[END_REF]. However, it does not consider the EOL when large industrial systems components are sometimes left on-site primarily because of higher costs of dismantling components or lack of knowledge of the EOL treatment parties that can manage the EOL.

Existing solutions

For each of the usage situations defined previously (cf. Section III.1), the users have different solutions that can help them accomplish their tasks and mitigate the problems they encounter. We identified six different solution categories:

 Software-based solutions comprise the programs and instructions to design and monitor the RS during each usage situation, such as CoolTool (CoolTool Technology GmbH) and Excel spreadsheets.

 Knowledge management-based solutions comprise documents, best practices guides, certifications and techniques to guide operators, improve activities, and raise user awareness during each usage situation.

 Methodological solutions comprise the methods and approaches used in the design phase to palliate potential problems arising in the exploitation and EOL phases, such as Design of X methodologies [START_REF] Bralla | Design for excellence[END_REF].

 Technical solutions comprise the technical clusters (at all readiness levels). They can include the system architecture, the physical components, digitization, etc.

 Tool solutions comprise all the tangible tools used for the RS installation, maintenance, dismantling, and EOL treatment, such as a pump for refrigerant or oil draining or overband magnets for ferrous metal separation.

 EN 378 groups the standard guidelines (NF EN 378, 2017).

4. Explore and decide the improvement paths of the socio-technical RS

Data treatment

To summarize, the activities of the socio-technical RS were represented in RID by twelve user profiles (Figure 4-6), three macro lifecycle stages (or usage situations), fifteen problems (Table 4-4) and six categories of existing solutions. Seven matrices were then filled with quantifying data on a scale from 0 = never to 5 = frequently, based on the qualitative data collected and following a precise question (see Figure 4-3). Actors can use a single solution during their activities or a combination of different solutions from the same category or different categories. For example, during the design phase, most activities use software solutions such as AutoCAD for the system drawings and Excel sheets for the calculation of the frigorific power, compressor needed, and sometimes by the EN378 guidelines and/or methodological solutions. Technological solutions and tools are only used to manufacture components or put the system in place.

Building an RID cognitive model of activity needs simultaneous answers to many precise questions but qualitatively, with the rough estimation of an order of magnitude, which is finally easy once the knowledge has accumulated. The other matrices filling process is explained in Appendix 2.

As explained in Section 2.1, we analyzed two sets of results: first the effectiveness indicators computed with RID Comparator, and second the overall matrix of value buckets computed with RID Compass. To explore the improvement paths in the socio-technical RS, we analyzed the global matrix of value buckets shown in Figure 4-12. The matrix was computed to establish where to innovate to maximise users' utility when no existing solutions were effective. The highest scores in the matrix highlight the opportunities of development for a competitive advantage, i.e., where competitors have not intensively innovated.

RID Comparator

The three most important value buckets occur during the first usage situation, i.e., design, manufacture and installation. This is the most complex stage because many stakeholders are involved, each with their way of acting.

The three VBs identified by the value bucket algorithm (Figure 4-12) are:

 VB1: the lack of understanding, i.e., adaptation of the new technical clusters (#7).

 VB2: the disturbed process of interaction between stakeholders, i.e., the organizational structure in the process (#8,9).

 VB3: lack of common tools, i.e., the knowledge shared by all the users involved in the project (including EOL) with a technical cluster database (#13).

The most important VBs occur for knowledge management problems and interactions between stakeholders. Even though the interviewees' most crucial performance metric was cost, this does not appear in the value buckets (VBs). The systematic care taken with operational conditions, i.e., regular maintenance, backup mode to prevent complete plant stoppage, and change of non-optimal components, might explain this. Moreover, a national financial incentive such as CEEs (energy saving certificates) in

France to change installations encourages companies to improve their systems. The refrigeration sector is one of the first industrial fields to be impacted and made aware of environmental consequences. Accordingly, reducing the system's environmental impacts was not rated as important in the VBs, as the interviewees considered that there were solutions such as ecodesign tools and technologies such as natural refrigerants to reduce emissions.

The EOL was rated the least impactful usage situation. As an RS comes under WEEE, the solutions to manage its EOL once technicians have drained off the refrigerant are highly developed. The only marked challenge for the EOL parties is to be informed of any disruptive technological change. They need to be aware of technological developments if EOL management changes.

During the exploitation phase, the system robustness influences the problems encountered. As RSs are robust due to extensive research on system optimization, interviewees did not indicate this stage as the most challenging one. However, as summers get hotter, more and more systems run as least once in their backup degraded mode. In the years to come, refrigeration technicians and project managers consider that they will have to face the problems of providing a system that can run at peak cold need (typically on the hottest day of the year).

More than a hundred tool solutions exist. However, the effectiveness indicators of the solutions presented in Figure 4-10 and Figure 4-11 show that they do not fully address the current industrial problems. Moreover, the problems of knowledge management and interaction between stakeholders do not have many possible solutions. This is one of the issues most difficult to manage for the interviewed user. We observed confusion among the stakeholders about the whole sociotechnical system, mostly because of the large number of companies, regulations, solutions, and possibilities.

Applying the RID process revealed the importance of the design, manufacture, and installation phases. The interviewees considered this usage situation as the most challenging one: it is then that multiple actors from different companies or teams collaborate, bringing their knowledge, preferences, tools, and experience.

Knowledge management and interaction between stakeholders are two important problem categories identified by [START_REF] Pärttö | Explaining failures in innovative thought processes in engineering design[END_REF] To close the loop of the RID methodology, our two solution components will be prototyped and tested in virtual and real environments. Finally, their ability to act on identified value buckets will be assessed by comparing, using RID Comparator, our solution with the existing ones.

Discussion and conclusion

Our objective was to improve the overall design of a refrigeration system (RS) for the food industry (as typically found in a supermarket). The areas of improvement of the socio-technical system lifecycle stages of the RS were analyzed to answer the question, "Are the causes of poor system performance due to a lack of tools, a lack of design know-how, or a whole set of organizational problems caused by a lack of consultation between the players in the value chain?"

Radical Innovation Design (RID) has proved efficient for analyzing complex socio-technical systems made of a set of activities to result in effectiveness indicators and a set of prioritized value buckets (VB), after a series of precise steps: an in-depth investigation, a segmentation in four dimensions, the evaluation of their relationships, and finally the computation of decision-making indicators.

A field study yielded the necessary data for analysis by RID. During the interviews, the issues revealed the importance of the first step in an RS lifecycle: design, manufacture and installation. Different actors are involved in the process, and yet interactions between them are lacking. The RID algorithms advanced three main stakeholder problems during the first lifecycle stage. They concern the comprehension of new technologies (VB1), the lack of a common tool corresponding to all stakeholders' expectations (VB2), and the interaction between stakeholders (VB3),

This analysis shows us how to start from these identified value buckets to develop a twofold innovative solution. We thus propose (i) an integrated model-based design platform for modelling and simulating the refrigerant system under design and (ii) a new organization of actors over an RS lifecycle. For the integrated platform, we identified a gap related to the disturbed process of advice (VB2) that results in sub-optimal design space exploration. This is mostly due to the final user constraints (budget, mindset, company's image, deadline, and operational and installation conditions) and objectives (e.g., minimizing energy consumption and costs, maximizing performance). In the early design stage, we seek to better integrate stakeholders' constraints and objectives in a shared design platform to ensure the collaborative design and overcome the lack of interaction between stakeholders (VB3). Simplified models of RS performance not considered by existing platforms will be used to assess it earlier in the design process, including low-readiness level technologies, thus ensuring a better understanding of those technologies (VB1). Performance metrics such as maintenance costs and ease, availability performance, space used, and adaptation to the regulation's evolution are all relevant. Lastly, once our innovative solution is developed, the RID methodology will enable us to assess its overall ability to remove or lessen quantities of pains we detected in this study, to make explicit its advantages over existing solutions and prove more objectively the creation of usefulness for RS stakeholders.

Knowledge model for the design of refrigeration systems

To capitalize knowledge around the design and multi-performances simulation of refrigeration systems, the knowledge of fifteen experts belonging to several companies with different roles was collected, in To palliate these problems, coordination of the actors for the design or development of technologies is an essential measure. This coordination is likely to happen on the condition that these actors share the same language, articulating concepts properly defined. Ontologies are systems of fundamental concepts that conceptualize the real domain concerned and provide shareable data support for wider use than a traditional knowledge database [START_REF] Karray | A Formal Ontology for Industrial Maintenance[END_REF]. The developed ontology provides a representation that can be used to support the design phase. It ensures the formalization and reuse of refrigeration simulation knowledge [START_REF] Noy | Ontology Development 101: A Guide to Creating Your First Ontology[END_REF].

The interest in developing ontologies is growing in engineering design because it involves knowledge sharing and the development of a common standard language [START_REF] Ahmed | A Methodology for Creating Ontologies for Engineering Design[END_REF]. The key use for ontology is highlighted in the literature. Yang and Zang (2006) have shown that building design object with an ontology ensures capture and sharable information. Ontologies bring many benefits highlighted in the literature and have been proven efficient in different domains such as automobile, aeronautics to support decision making in the early design process by defining in a formalized way the design problem, the constraints, structure, objectives, etc. (Munir and Sheraz Anjum 2018; L. Yang, Cormican, and Yu 2019). Indeed, understanding a complex problem by using proper models and automated knowledge, exploring more alternatives and improving prediction significantly improve human performance for the resolution of the problem [START_REF] Liang | Development of a Knowledge-Based Model Management System: Special Focus Article[END_REF].

The main objective of the development of this knowledge model for designing a refrigeration system is to capitalize in a systemic representation the industrial knowledge of different experts and the theoretical knowledge from literature to define the design problem properly. The results of this data acquisition allow us to establish the modelling and multi-performances simulation process of the behaviour of a refrigeration system architecture in different usage scenarios, such as finding the best design solution or observing the impacts of a refrigerant change in the performances of a refrigeration system. The developed knowledge model is the conceptual creation of designing an energy-consuming system that meets functional requirements to fulfil the stakeholder's most crucial targets of quality -or performances -, costs, delays, and environmental impacts. The development of technologies based on artificial intelligence and CAE/CAD (Computer Assisted Engineering/Computer-Aided Design) has facilitated access to information related to the structure and form of objects, although design know-how used in the conceptual design phase remains hidden due to its subjective nature and implicitness [START_REF] Kitamura | Ontology-Based Systematization of Functional Knowledge[END_REF]. The ontology proposed would be usable by all the stakeholders of the domain listed and defined in the diagnosis chapter to ensure a shareable semantic and the usage of a common tool, a design and simulation platform, that would support the designer and researchers in their activities.

The first section of this chapter presents the construction of the ontology. Different types of ontology have been listed, depending on the knowledge and the application (Gómez-Pérez 1999; Guarino understand what is included in these ontologies, the following points describe a few of the most common categories found in literature: General ontologies, also called common ontologies, are domain-neutral as it capitalizes knowledge with basic notions and concepts related to things, time, space, event. An example of such ontologies is given in [START_REF] Soininen | Towards a General Ontology of Configuration[END_REF], in which they propose a general ontology for the configuration of any system. Meta-ontologies or generic ontologies are also general enough to be reused across domains. An example is given in [START_REF] Borst | Engineering Ontologies[END_REF], where the authors define physical system simulation. Domain ontologies provide vocabulary about concepts, theory, the relation between concepts, principles of a particular domain, capturing "static knowledge in a problem-solving independent way" (Gómez-Pérez 1999). Task ontologies are concerned with problem-solving, not related to a particular domain but the activity.

Domain-task ontologies include terms used to solve problems in a particular domain. Finally, application ontologies capitalise knowledge for modelling a particular domain. A well-known ontology in the design of a system is the Function-Structure-Behaviour (FBS) from (J. S. Gero and Kannengiesser 2014) that will be commented on in the section.

To our knowledge, no ontology-based model has been developed to support the refrigeration systems design process, more specifically, an integrated simulation for the design space exploration.

The second section presents the knowledge model for the design of refrigeration systems in an industrial context in an object-oriented representation through the definitions of classes, their properties and instances. Classes represent the domain concepts, and the association relations represent their interactions. Further, we propose that this ontology be generalized for the whole energy network systems in a store.

The ontology developed follows the classical steps of an ontology construction presented in session 2, from the specification to the implementation of the ontology directly on a simulation and design space exploration platform. These steps are presented in section 3.

The last section presents the evaluation of the ontology in two ways: from a specific case study and with expert validations.

A conclusion ends this chapter.

2. Construction process of the knowledge model

Development of an ontology

Ontology development has been studied by multiple researchers for years. Ontologies comprise definitions for objects and types of objects as well as their semantics and relations in a formal way, forcing a shared understanding of the refrigeration domain. In contrast to a mere taxonomy, which represents a hierarchically organized vocabulary of generic and specialized concepts, an ontology extends this idea by means of concept relations or constraints to enhance semantic interpretation [START_REF] Gruber | A Translation Approach to Portable Ontology Specifications[END_REF][START_REF] Gruber | Toward Principles for the Design of Ontologies Used for Knowledge Sharing?[END_REF]. The main components of ontologies are a hierarchy of concepts representing types of entities, relations between concepts, restrictions on relations, and instances.

To construct an ontology, multiple methodologies have been developed in the design science domain. As constructing an ontology is a creative process, it necessarily starts with identifying the ontology's motivation, scope, and purpose. Depending on the methodology, different protocols exist, such as answering a list of questions, brainstorming. Then, the creative process needs to be iterative to refine the ontology. To build the concepts, relations, classes, one needs to first identify if a possible reuse of existing ontologies is possible. Then, workshops, brainstorming sessions, interviews, literature reviews, and observations can bring knowledge to propose a conceptualisation of the acquired knowledge. Table 5-1 gives a summary of the different methodologies. -Specification: Define the scope, purpose, end-users, intended use by answering the question from [START_REF] Pinto | Ontologies: How Can They Be Built?[END_REF].

-Check for reusable ontology -Knowledge acquisition -Conceptualization and formalization using UML graphs

The next step corresponds to the implementation of the knowledge model on a design and simulation platform that different stakeholders can use. Finally, the last step consists of evaluating the ontology as in the METHONDOLOGY by verification and validation. The next section presents in detail the construction process of our knowledge model.

Methodology for the knowledge model construction

The construction of the knowledge model proposed in this work follows the usual steps that are presented in Section 1.1:

-Specification, during which one defines the scope, purpose, users and intended use of the knowledge model.

-Knowledge acquisition comprises all the data acquisition processes and the data acquired that provides inputs to the knowledge model.

-Conceptualization, based on the knowledge model, this step defines the concepts, classes, relations related to the specifications.

-Formalization, based on the conceptualization that defined the vocabulary, this step consists of clearly formalizing the concepts through schemes, -Implementation, which is the development step using the formal scheme to implement the model with oriented object language, web ontology languages like OWL, or directly on a design platform, as applied in this work.

-Evaluation, which is the final step to assess the quality, i.e., the accuracy, completeness, correctness, clarity, coverage of the ontology. Different possibilities exist to assess the knowledge model and its implementation: using ontology metrics, application through scenario testing, user testing and data comparison. For more detail, see [START_REF] Hlomani | Approaches, Methods, Metrics, Measures, and Subjectivity in Ontology Evaluation: A Survey[END_REF]. The evaluation of our ontology includes users and scenario testing. It will be detailed in Chapter 6.

Figure 5-1 shows the simplified scheme of the research methodology adopted to develop an ontology and adapted from the methods presented previously.

From the specification to the formalization of the knowledge, the first steps, with all the data acquisition and iteration, took one year of work. The specification defines the purpose of the ontology construction, the perimeter of the study, the intended use and users. The knowledge acquisition process, the number of experts, literature reviews are detailed in the following sections.

The conceptualization step consists in capitalizing the data acquired based on existing knowledge models, languages, and representation. Before implementing the ontology in a platform, the final step consists of formalizing the conceptual representation through object-oriented representation to ensure that developers understand the proposed knowledge model and that no information is lost in the implementation process.

After the formalization phase, the ontology is implemented into a platform for the design and simulation of the refrigeration system. The classes, attributes and relations are used for objectoriented implementation. The ontology is then verified by two approaches: step-by-step verifications and use case verification with a supermarket refrigeration system. Five workshops were organised to challenge and verify the proposed ontology with experts (researchers, technicians, and frigorific engineers).

Then, the knowledge model and platform usage validation is made through scenario validation with experts. This scenario-based validation is detailed in the next chapter.

To position our knowledge model concerning existing works, we present related works from the literature review in the next section.

Related ontologies

To understand the development of an ontology in the domain of system engineering, we focus our attention on general and meta-ontologies related to physical systems, physics-based simulations, engineering design optimizations, system engineering; domain ontologies related to refrigeration domain, buildings; and application ontologies for industrial design objects. These ontologies help to support CAE/CAD. The literature review of related work justifies the different parts described in our proposed knowledge model for the design of a refrigeration system:

-A description of the context to design an RS (user, objectives, specification, constraints).

-A description of the objects and concepts useful to describe an RS solution (compressor, expansion valve, refrigerant).

-A description of the RS multi-physics simulation (performances, physical behaviour…) and a description of the decision-making process (performances, trade-offs…)

General and meta-ontologies

Generic and meta-ontologies help align heterogeneous knowledge in CAE/CAD (Computer Assisted

Engineering/Computer-Aided Design) [START_REF] Mascardi | A Comparison of Upper Ontologies[END_REF]. Extending a general ontology to a domain-specific, in our case, the refrigeration domain can help to share terms and data.

For the design of refrigeration systems and the multi-performances simulation, we have explored existing work on a physical system, physics-based simulation, design optimization or system engineering.

General Formal Ontology (GFO) is an ontology for conceptual modelling. It is composed of multiple sub-ontologies related to the biomedical domain. One GFO sub-ontology is the Chemical Entities of Biological Interest ontology (ChEBI). Even though the first application domain of this sub-ontology is the biomedical, one concept of "refrigerant" is interesting for the refrigeration domain. They define refrigerant as "A substance used in a thermodynamic heat pump cycle or refrigeration cycle that undergoes a phase change from a gas to a liquid and back. Refrigerant is an application". It can have different instances defined in the ontology as follows "ammonia has role refrigerant". Figure 5-2 shows the different instantiations capitalised in ChEBI (https://www.ebi.ac.uk/chebi). [START_REF] Borst | Engineering Ontologies[END_REF] To support the construction of our ontology, the optimisation domain has been investigated as a tool widely used in the engineering design process. To ensure the integrity of the optimisation problem, the optimization process, and the resulting optimized solutions, [START_REF] Witherell | Ontologies for Supporting Engineering Design Optimization[END_REF] have captured this knowledge using the instantiation of an ontology its implementation into a prototype computational knowledge-based tool called ONTOP. They acquire the knowledge on compiling and organising optimisation terms in literature. They then define a formal taxonomy. The authors highlight that the knowledge problem can then be stored through a class instance, each instance specifically defined by the attributes of the design is optimized. Building Intelligence Modelling (BIM) ontology is an informal and semi-structured domain ontology.

It includes concepts, attributes, relations and general knowledge related to the Architecture, Engineering, Construction and Operations (AECO) industry. First, BIM can be defined as a "methodology to manage the essential building design and project data in digital format throughout the building's life-cycle" [START_REF] Penttilä | Describing the Changes in Architectural Information Technology to Understand Design Complexity and Free-Form Architectural Expression[END_REF]). BIM ontology is composed of four macro-level knowledge objects that include 50 concepts, 14 attributes, 160 relations, and knowledge sets which are combinations of the three previously cited objects. [START_REF] Succar | Building Information Modelling Framework: A Research and Delivery Foundation for Industry Stakeholders[END_REF] identifies three fields of activity (see . The technology field develops soft and hardware to support the building life stages (design, construction, operation). The process field corresponds to the stakeholders involved in the building development. Finally, the policy field regroups the parties developing items to help decision making.

Figure 5-5. The three fields of BIM activity from [START_REF] Succar | Building Information Modelling Framework: A Research and Delivery Foundation for Industry Stakeholders[END_REF] As buildings and refrigeration systems are complex systems that have been shown in the diagnosis to have similar lifecycles, the framework and concepts used in BIM can be used in our ontology.

Application ontology: design of a system

In the last decade, we observed a trend toward more general theories to offer abstract approaches and an ontology of the design process. Application ontologies are developed for a specific activity and need the knowledge of a particular domain. We focus our research on the design domain for the design-related activity.

One of the first theories, called General Design Theory (GDT), was explored in 1989 by Yoshikawa (Yoshikawa 1989). Based on the same idea, John Gero developed an application ontology for the design activity FBS for Function, Behaviour and Structure (J. S. Gero 1990; J. Gero and Kannengiesser 2004; J. S. [START_REF] Gero | The Function-Behaviour-Structure Ontology of Design[END_REF]. This ontology is now very well-known and used in the design society. The idea is that the designer creates a structure (S) that will fulfil several functions (F).

Each function is expected to behave in a certain manner (Be). The real behaviour of the structure (Bs) can be different from the one expected. 3. Knowledge model proposition

Specification

What is the purpose? As described in the previous paragraphs, different ontologies exist to capitalise knowledge in a general, formal way, either generic and meta or related to a domain, task, or application. The ontology developed in this study provides a representation that can be used to support the design process in the refrigeration domain. As shown in the literature review and the industrial diagnosis, a formal representation can reduce the highlighted gaps: reduce the industry's fragmentation, improve efficiency and effectiveness of solutions, and lower the negative impacts of inadequate system design. It can be used to enable the decision-making process of designing a refrigeration system from the system specification to its final design.

What is the scope? The ontology must include information on the system's environment, stakeholders of the domain, the system's structure (components and links), resources, activity, documents, and regulation.

Who are the intended end-users? The main end-users are the stakeholders presented in the diagnosis chapter. Different scenarios of such an approach are tested on the users to validate its usefulness, reusability and relevance. Refrigeration system end-user, such as supermarkets as demonstrated in our application case study; supplier and designer of the system; researchers of the refrigeration domain are the targets of our works. Other stakeholders such as EOL parties can be considered.

What is the intended use?

The ontology intends to be used with a refrigeration system simulation and exploration platform. Four high-level scenarios can be explored: (1) observe the impacts of a change on the performances of the system; (2) help designers in the building of a new refrigeration system; (3) visualize a greater decision space by showing all the feasible solutions; (4) evaluate the potential of a low Technology Readiness Level (TRL) technology.

Knowledge acquisition

Gaining important knowledge of the refrigeration domain is necessary to develop an ontology. Two types of knowledge acquisitions have been conducted: top-down and bottom-up [START_REF] Garcia | Grounding Knowledge Acquisition with Ontology Explanation: A Case Study[END_REF]). The first one consists of acquiring the tacit knowledge of refrigeration domain experts. The second approach focuses on acquiring knowledge thanks to specific cases. Our study's top-down approach fits well as domain knowledge and experts were available [START_REF] Garcia | Grounding Knowledge Acquisition with Ontology Explanation: A Case Study[END_REF].

Conceptual model and formalization

In the proposed ontology, we formalized a conceptual model based on the knowledge acquired and from the different existing ontologies presented in section 2. We have classified our concepts into four main categories:

(1) Context related, a description of the context to design an RS (user, objectives, specification, constraints).

(2) System design solution related, a description of the objects and concepts useful to describe an RS solution (compressor, expansion valve, refrigerant…).

(3) Properties, which are set and unchangeable data used for the calculation of performances that are related to the physics principles, or acquired from environmental impact database, from manufacturer's catalogue or experts.

(4) Performances assessment-related, a description of the RS multi-physics simulation (performances, physical behaviour…) and a description of the decision-making process (performances, trade-offs...)

We use a merging of different domain ontologies to feed ours, which means that some of the concepts are reused directly from the ontologies and framework as presented in the previous sections (example for refrigerant) and adapted for our intended use to be integrated into a platform.

To represent the knowledge model in a clear and understandable way, object-oriented representation is chosen. It is a way to represent ontologies through classes, class properties, and instances. Classes represent domain concepts, and association relationships represent their interactions (Gruber and Özsu, 2009). We choose to formalize the knowledge model by using Unified

Modeling Language (UML). The ease of comprehension and interpretation for human beings by using this language is highlighted in [START_REF] Cranefield | UML and the Semantic Web[END_REF] and [START_REF] Cranefield | UML as an Ontology Modelling Language[END_REF]. Moreover, UML is a modelling language object-oriented using classes, attributes, properties, and relations that are easily modular and changeable. Thus, this modelling language is used in this study to understand the ontology better. Thus, we propose that the scenario in our knowledge model is defined as follow:

The scenario expresses the context related to the knowledge model usage by a particular user (stakeholder) to respond to its objective in a specific system environment.

A stakeholder class is defined by its identity as an actor of the domain, expertise, and mindset. The list of the refrigeration domain stakeholders is the one defined in the industrial diagnosis in Chapter 2. A stakeholder is trying to achieve an objective that requires the simulation and exploration of refrigeration systems in a specific system environment.

A system environment-class can be related to requirements. It defines where the refrigeration system operates and the constraints. It is composed of a location, conservation set-points temperatures, and a certain volume to be cooled. In the case of a supermarket refrigeration system, the system environment is also defined by the store area, the working and opening hours, number of employees.

The volume to be cooled can be a combination of food storage facilities (cold rooms and vertical/horizontal display cabinets), their numbers and the mass of food. In some cases, a frigorific power can replace the volume to be cooled.

System design solution

To answer a design problem for a physical system such as a car or a table, one must first define the structure of the design solution (J. [START_REF] Gero | The Situated Function-Behaviour-Structure Framework[END_REF]. It comprises design variables that describe the system components and their relations. This structural description of the system constitutes the decision space. The domain experts established the choice of design variables and their ranges of variation [START_REF] Rivier | Analyse et Optimisation Multicritères d'un Procédé de Transfert Thermique et de Séchage Pour Une Application En Afrique de l'Ouest[END_REF]).

An RS design solution is composed of two main classes: (1) A system architecture and ( 2) modules:

technical components as described in Figure 56789. A system architecture class of the refrigeration system solution defines the arrangement of the modules between them, directly impacting the performances to assess.

The modules are Objects as defined in the Basic Formal Ontology (BFO). In this ontology, an object is defined as a 'material entity that is spatially extended in three dimensions [and] causally unified' [START_REF] Arp | Building Ontologies with Basic Formal Ontology[END_REF]. The technological modules class are composed of the main physical elements of a refrigeration system: a refrigerant fluid, a compressor type, a condenser type, an evaporator, an expansion valve, pipelines and zero, one or more technological clusters. Two types of relations are exploited in the knowledge model to express the connexions between sub-systems:

The relation made of developed in the PSO is a formalized relation of the material constitution theory [START_REF] Korman | Material Constitution[END_REF]). In the PhysSys ontology [START_REF] Borst | Engineering Ontologies[END_REF], the same relation can be found as a system decomposition into sub-systems. As an example of a constitution relation used in our knowledge model, we can illustrate that: a compressor is made of steel, aluminium, copper.

To describe the space location of the instances, the PSO relation located in is used. As examples for refrigeration systems, we can describe that the refrigerant is located in the compressor, condenser, evaporator, expansion valve and pipelines, or that the evaporator is located in the environment to be cooled.

Properties

Each RS solution defines a set of properties. [START_REF] Ashby | Engineering Materials 1: An Introduction to Properties[END_REF] define two types of properties, intrinsic or attributive. The first one refers to the mechanic, physical data of an item, whereas the other type refers to the quality data such as cost, sustainability, ease of manufacture. We define the data as information on the items related to the system design solution stored and used to calculate performances.

Based on these definitions, we consider the properties as all the quantitative or qualitative data related to the context, the system design solution, which are stored in a database, unchangeable and necessary to simulate the performances of a refrigeration system.

The properties are thermodynamics data, material substance distribution, cost data, components' manufacturing specifications… of the system elements put into mathematical models to simulate the system performances. These data classes are composed of:

-Thermodynamics and physics data, such as the density of a refrigerant, a state change temperature, a heat exchange coefficient, etc.

-Data from manufacturers of technical components such as the power supply of a compressor, the inlet or outlet diameters, noise etc.

-Cost data set by energy suppliers such as the price of electricity, calculated from samples such as the installation price of a compressor according to the power, or capitalized from experts' knowledge such as labour cost -Environmental impact data from databases such as EcoInvent to the scenario. To do so, we should first characterize the performances and then define how the system behaves. The system behaviour can be either defined with physical modelling, i.e. the system answers to principles of physics through mathematical models, or with empirical modelling, i.e.

experimentations and observations gather information on the system behaviour [START_REF] Atherton | Searching for Improvement[END_REF].

A performance is defined by (Ulrich and Eppinger 2016) by "how well a product implements its intended function. Typical performance characteristics are efficiency, life […], energy consumption."

In mathematical optimization, performance is considered as a response that "describes the overall fitness of the design for its intended purpose" [START_REF] Atherton | Searching for Improvement[END_REF]. In the FBS framework, the performances are described Behaviour (B) variables that are "attributes derived or expected to be derived from the structure (S) variables of the object, i.e. what it does." (J. [START_REF] Gero | The Situated Function-Behaviour-Structure Framework[END_REF], physical things that vary in time (quantity, flow, effort, energy and power) in the PhysSys ontology [START_REF] Borst | Engineering Ontologies[END_REF] or as physical behaviour in the meta-ontology PSO [START_REF] Cheong | Physics-Based Simulation Ontology: An Ontology to Support Modelling and Reuse of Data for Physics-Based Simulation[END_REF].

To formalize the performances necessary in our knowledge model and based on the listed ontologies, we distinguish two types of performance classes:

-

Summary

For a better understanding of the knowledge model, the description of a supermarket refrigeration system is shown as an example in the next section. Table 3 gives a summary of the macro concepts, their definitions, and the classes associated. The instantiation of the system design solution, the properties and the local performance class are explained in the case study and the appendices.

Table 5-3. List of concepts for the refrigeration system design level, their definitions, and their classes associated with retail refrigeration

Inter-dependence between concepts exists for the calculation of performances. classes are modelled. For example, the calculation of energy consumptions for each display cabinet or cold room depends on the refrigeration system design solution and the calculation of the frigorific power. The frigorific power is dependent on the system environment class and the properties class.

The maintenance score, technology readiness level (TRL) and installation costs depend on the refrigeration system design solution, itself related to the properties class.

Other inter-relations not shown in Figure 5-12 include relations between TRL and maintenance, costs and maintenance, environmental impacts and system design solution, etc.

Description of a supermarket refrigeration system design problem

The proposed design problem focuses on the specific domain of retail refrigeration systems in which a large panel of experts, data and technologies are available. We are interested in several aspects of refrigeration systems during their life cycle to integrate them in the early design phase. They correspond to the situations of use presented in the industrial diagnosis: (1) specification, dimensioning to installation, (2) the use phase and maintenance of the systems, and (3) the end of life of the machine.

The chosen problem aims at modelling a typical retail refrigeration system to find the best solution.

The first step consists of modelling the context, i.e., the user, objective and system environment.

Then, a mapping between the input data and the outputs (local and global performances) is shown.

Finally, a system design solution is detailed.

Context modelling

The context is modelled using the knowledge model. To define the design problem, one must first set the context or scenario. It is composed of three classes: user, objective, and system environment.

The extended sub-classes (Cheong and Butscher 2019) associated to the three classes of the scenario are shown in Table 5-4.

More (or less) sub-classes for each concept can be added (or withdrawn) to fulfil a different problem modelling. As an example, if the problem consists of modelling an air conditioning system, the extended sub-classes do not include the number of display cabinets but include as system environment the sizing of the store wall, the number of clients... Extended sub-classes for objectives can also be modified to add comfort analysis.

Each extended sub-class has one or more instances. As an example, for the shown use case, Paris is an instance of location. Depending on the objective, the local and global performances can change, as well as the design solution, the decision or visualization of the results.

System design solution definition classes

The decision space is defined with the structure and the modules or technical components necessary to compose a refrigeration system. For negative and positive temperatures, the RS structure is the same for the main components (compressor, condenser, evaporator, expansion valve, refrigerant and pipelines). The class and the extended sub-classes to define a system design solution in a supermarket case study is presented in Table 5-5. Depending on the architecture, specific components may be used. As an example, transcritical architecture requires to have components that can support CO2 as a refrigerant. Other design constraints are defined as incompatibilities [START_REF] Bussemaker | System Architecture Design Space Exploration: An Approach to Modeling and Optimization[END_REF][START_REF] Arora | Chapter 3 -Graphical Optimization and Basic Concepts[END_REF]. For example, the diameter of the pipelines must fit the entry or exit of a component.

Table 5-

Local and global performances assessment

The assessment of the global performances depends on the calculation of the local performances themselves depending on the context. An example of mapping the context to two classes of global performances: energy consumption and environmental impacts, is shown.

Energy consumption simulation

The energy consumption balance equation is defined as follow.

𝐸 𝑡𝑜𝑡𝑎𝑙 = ∑ 𝑄 (5-1)

Where ∑ 𝑄 depends on the number of display cabinets, cold rooms, perimeter (yearly, monthly or daily) and the system structure. As an example, the balance equation for one day for a display cabinet at positive or negative set-point temperature is given in the following equation.

𝑄 𝑝𝑜𝑠 𝑜𝑟 𝑛𝑒𝑔 = 𝑄 ̇𝑑𝑎𝑦 * 𝑂𝑝𝑒𝑛𝑖𝑛𝑔𝐻𝑜𝑢𝑟𝑠 + 𝑄 ̇𝑛𝑖𝑔ℎ𝑡 * 𝐶𝑙𝑜𝑠𝑖𝑛𝑔𝐻𝑜𝑢𝑟𝑠 + 𝑄 ̇𝑑𝑒𝑓𝑟𝑜𝑠𝑡 * 𝑡 𝑑𝑒𝑓𝑟𝑜𝑠𝑡 Where 𝑄 𝑝os or 𝑛𝑒𝑔 is in kWh/day and where 𝑄 ̇𝑑𝑎𝑦 = 𝑄 ̇𝑤𝑎𝑙𝑙 + 𝑄 ̇ℎ𝑤 + 𝑄 ̇𝑙𝑖𝑔ℎ𝑡𝑖𝑛𝑔 + 𝑄 ̇𝑖𝑛𝑓𝑖𝑙𝑡𝑟𝑎𝑡𝑖𝑜𝑛_𝑑𝑎𝑦 + 𝑄 ̇𝑟𝑎𝑑𝑖𝑎𝑡𝑖𝑜𝑛_𝑑𝑎𝑦 + 𝑄 ̇𝑓𝑎𝑛 And 𝑄 ̇𝑛𝑖𝑔ℎ𝑡 = 𝑄 ̇𝑤𝑎𝑙𝑙 + 𝑄 ̇ℎ𝑤 + 𝑄 ̇𝑖𝑛𝑓𝑖𝑙𝑡𝑟𝑎𝑡𝑖𝑜𝑛_𝑛𝑖𝑔ℎ𝑡 + 𝑄 ̇𝑓𝑎𝑛 Where 𝑄 𝑋 ̇ are in Watt and correspond to the frigorific power.

Then, each variable answers to thermodynamics laws and models that can be found in Arias (2005) and [START_REF] Salehy | Energy Performances Assessment for Sustainable Design Recommendations: Case Study of a Supermarket's Refrigeration System[END_REF].

To calculate the energy consumption, a set of properties are necessary to be defined. Other global performances can be simulated for the design of a refrigeration system and depending on the users' objectives. For supermarket refrigeration systems, the total cost, the maintenance, and the technological readiness level are described.

To calculate the total cost, the capital costs (CAPEX) and the operational costs (OPEX) are summed.

They include the purchase of the technical modules, the labour cost depending on the installation architecture for capital cost and the electric consumption costs, the maintenance labour costs and the refrigerant refill costs through the exploitation phase.

The maintenance is calculated as a score that depends on the architecture and the refrigerant. It is based on four sub-classes: accessibility, error proofing, ergonomic and physical injury. For example, the score of accessibility of the components in a primary direct system will be higher than for plugin systems as the components will be less accessible for the technician.

To have a complete view of the local and global performance calculations of the design problem for a supermarket refrigeration system, all mathematical models are displayed in Appendix 3.

Discussion and conclusion

Generalisability of the knowledge model

The knowledge model implemented in a design and exploration platform can be generalized to other usages. Two generalisability possibilities are explored:

-Modelling different kinds of refrigeration systems -Modelling the whole energy system

Modelling other kinds of refrigeration systems

It is intended to be as modular as possible, and adding different types of refrigeration systems would be possible if the classes, relationships, instances and equations are fulfilled.

The main concepts, classes and relationships would not change, but the extended sub-classes, instances and mathematical equations would. Indeed, an example for a refrigeration system in a food manufacturing plant requires the same concepts of scenario, design space, properties, local and global performances, and the associated classes. However, the extended sub-class related to the system environment will change by adding the process of food fabrication (number of fabrication machines, the volume of food, the type of food, etc.).

Modelling the entire supermarket energy system

To better represent the design problem and design an optimal system, the entire energy system of a supermarket must be modelled. It means that the heating system, air conditioning, lighting, heated water, and other energy systems should be considered for further work.

The types of the energy system in a supermarket implies to add more classes in the knowledge model. For example, in the scenario concepts, one would add classes of "heat system environment", "electrical energy network environment", etc. The systems solutions would have a different breakdown structure by adding heat system components, electrical network components, different technological modules that can allow heat recovery from the refrigeration systems, etc. Adding different technical modules in the system design solution means that the developers need to add more instances and their associated mathematical equations to ensure the correctness and completeness of the model. As shown in the knowledge acquisition section, one must follow the same steps to define the different phases of building a knowledge model, from the specification to the verification and validation shown in Chapter 5 and Chapter 6.

Conclusion

This chapter answers the question on how to improve the design of refrigeration systems in the early design stages (conceptual and embodiment study) by first representing the type of knowledge necessary to design refrigeration systems. Related works on the design and simulation of physical systems have been done, but to our knowledge, formal representation for refrigeration systems design has never been explored. Moreover, this work is the first step toward developing a digital platform whose goal is to. This platform ensures an optimal refrigeration design space exploration.

This first step described in this chapter proposes a knowledge model (or ontology) with a structured language for refrigeration system design based on data collected from research and industry. The knowledge model is based on existing ontologies and the experts' know-how retrieved through interviews, observations, literature reviews. Four macro-concepts are defined (1) the scenario, (2) refrigeration system design solution, (3) the properties, and ( 4) the performances to be assessed.

The sub-concepts, relationships and instantiations are described to represent the process of designing refrigeration systems realistically. This proposal considers the different stakeholders involved in the RS life cycle. It supports them in the design stage activities by efficiently defining and formalizing the design problem, and it improves the interaction between the different stakeholders by facilitating communication.

The knowledge model serves as a design problem representation that is then fed into a design and exploration platform to help stakeholders make aware decisions during the development phase. The choice of supporting tool to explore the design and performance space as well as the implementation of the knowledge model and the verification steps are discussed in the next chapter.

A simulation platform for exploring design spaces of refrigeration systems

This chapter presents the simulation and design space exploration platform. Based on the knowledge model previously shown, the design problem is implemented using the Geeglee tool. In order to find an optimal solution(s), the platform allows enumerating the set of possible solutions. Indeed, the solutions answering the design problem of supermarket refrigeration systems are first brute-force enumerated.

Then, an exploration interface can be used to address different user objectives by providing an exploration of the solution and performance space. Two usage scenarios are shown in this chapter. The first example is that of a supermarket manager seeking to observe the changes of a refrigerant on the performance of his existing system. The second example is that of a designer seeking to quickly assess what the best solution for his client within budgetary and environmental constraints is. Verifications are made through workshops and a use case application to ensure completeness and correctness of the modelling process and outcomes.

Introduction

To improve the design activities in the early stages of the design process, as shown in the diagnosis,

we propose an integrated approach, i.e. a conceptual framework and a simulation tool, to support the stakeholders in the design process. Chapter 5 proposes a knowledge model for the design of refrigeration systems and, in particular for supermarkets. Four main classes are built to represent clearly our problem: scenario, refrigeration system design solution, properties, and performances assessment. It allows us to answer our research gap 2 on the lack of integrated approach for refrigeration systems design. In this chapter, an answer to our research gap 3 is proposed. The objective is to implement the knowledge model into a multi-objective simulation and design space exploration platform for supermarket refrigeration systems.

First, design space exploration and supporting tools are introduced in section 2. Then, the supporting tool chosen to implement our model is discussed in section 3.

In section 4, the implementation of the design problem and the dimensioning steps to search for a feasible solution are presented. Section 5 presents the exploration of refrigeration systems design space through two usage examples.

Finally, section 6 presents the discussion on how we provide an answer to research gap 3 as well as the limitations. A short conclusion summarizes our study and introduces the next chapter.

Design space exploration

As expressed in the previous chapter, there are at least five performances (or objectives) to optimize for the design of a refrigeration system: energy consumption, environmental impacts, cost, technological maturity and maintenance. This has the consequence of increasing the number of potential optimal solutions and increasing the complexity of the trade-off needed to be addressed.

Model-Based System Engineering (MBSE) and optimization techniques are often used to find the more suitable solution for complex system development (see Chapter 2). During the diagnosis, we highlighted a top-down approach to refrigeration system design based on evaluating a very limited number of design solutions (called point-based design). The limitations of this approach are the restriction of the design solutions and the lack of user interaction in the process (research gap 3).

Design Space Exploration paradigms

Design space exploration (DSE) paradigm offers an answer to the limitations. Design space exploration is developed to improve the common tasks of designing a product or a system in the early design stages [START_REF] Scaravetti | Design space exploration in embodiment design: an application to the design of aircraft air conditioners[END_REF]. It can improve user-interaction in the process (Balling, 1999). DSE consists of assessing design alternatives. It can be used for quickly generating and simulating a set of feasible solutions, optimizing if metrics are available to compare design solutions, finding the architecture and dimensioning the components that satisfy the design constraints [START_REF] Kang | An approach for effective design space exploration[END_REF].

To understand how DSE can be implemented, we focus the next sections on DSE supporting techniques and tools.

Design Space Exploration supporting techniques

One important limitation to the use of DSE is the number of alternatives to evaluate. Indeed, as complex systems can have millions of possible alternatives, a proper representation of design space is necessary to ensure a relevant DSE. Different techniques exist to support the design space exploration.

Research in computer science has developed the Constraints Programming (CP) methods to respond to this limitation. It is used to solve optimisation problems and construct a feasible design space [START_REF] Devanathan | Creating Polytope Representation of Design Spaces for Visual Exploration Using Consistency Technique[END_REF][START_REF] Rossi | Handbook of constraint programming[END_REF][START_REF] Scaravetti | Structuring of embodiment design problem based on the product lifecycle[END_REF][START_REF] Yannou | Use of Constraint Programming for Design[END_REF].

A constraint satisfaction problem (CSP) is defined by a set of variables V belonging to a set of domain D and answering to a set of constraints C. CSPs serve for enumerating feasible space, in the case of discrete variables, of all feasible combinations of parameters (which verify constraints and specifications). Solving a CSP consists of solving two steps: searching for a solution and then consistently filtering, i.e. finding the set of feasible solutions by filtering all infeasible solutions [START_REF] Kumar | Algorithms for constraint-satisfaction problems: A survey[END_REF].

The search techniques comprise backtracking methods, also said constraint propagation methods.

If there are many more infeasible solutions than feasible solutions, backtracking techniques (branch and bound, branch and prune) avoid wasting time trying to enumerate these infeasible solutions as soon as possible enumeration and also allow to find an optimum for a single-objective function quickly. Filtering techniques comprise consistency-based algorithms such as path-, arc-, hull-, and box-consistency [START_REF] Devanathan | Creating Polytope Representation of Design Spaces for Visual Exploration Using Consistency Technique[END_REF][START_REF] Rossi | Handbook of constraint programming[END_REF].

Another less sophisticated method is to enumerate all solutions -feasible and infeasible -with tree search-based techniques such as generate-and-test, or brute-force search. After this exhaustive enumeration, we check whether the parameter combinations verify all the design specifications and constraints, making the solution feasible. In our study, we used this simpler approach of solution brute-force enumeration followed by a feasibility check. It works well at the condition that the apparent combinatorics of the design space is not too important.

DSE supporting tools using brute-force enumeration

DSE needs to be integrated into a digital tool to compute the high number of possible solutions and further explore the feasible solutions by "what if" hypotheses. The digital tool needs to meet two specifications: appropriate computational costs [START_REF] Kang | An approach for effective design space exploration[END_REF] and appropriate results visualization [START_REF] Ligetti | Assessing the impact of graphical design interfaces on design efficiency and effectiveness[END_REF].

There are several ways to represent the set of possible solutions depending on the type of constraints: 1D intervals, 2D polyline or polygons, 3D polyhedrons. The advantage of representing the results in 1D intervals is simplifying information visualization, especially for complex multidisciplinary systems. 2D and 3D representations provide information transmission for constraint resolution problems on two or more objectives.

In their work on Interactive Visualization Artefacts (IVAs), Tweedie et al. (1996) developed the Influence Explorer tool, presented in Figure 6-1, where the solutions are presented in histograms and 1D intervals. The tool is based on a generate-and-test algorithm, where the solutions are enumerated in an exhaustive way as all the combinations of the parameters belong to a discrete set of values.

The performances are then calculated, and we check if the design specification and constraints are verified for a parameter-performance tuple. When the tuples are feasible, i.e. they check all the constraints, they are coloured in red and black while the "infeasible" points are coloured in white and grey in Figure 6-1. The designer can interact simultaneously with the parameters and the performances using the sliders to reduce or extend the solutions. The designer can either find the parameters corresponding to the required performance or the performances for a particular set of parameters. This method puts the user at the centre of the decision. [START_REF] Eriksson | Design of experiments[END_REF]. The decision process is iterative, first the user discovers solutions and investigates the problem then the design space is explored using the knowledge investigation.

HEEDS MDO (Siemens, 2014) is another DSE supporting tool, allowing the user to perform design optimization and find a set of Pareto optimal solutions using different optimization algorithms (Genetic algorithm particle swarm, SHERPA, etc.), DOE and sensitivity analysis. One interesting feature is the possibility for the tool to retrieve the data from other software, including CAD tools or Excel.

Different post-processing tools are available to explore the solutions using tables, 2D plotlines, colours, point shapes... Finally, we present the modelling and simulation tool Geeglee (Holley, 2011) for complex systems design exploration. The tool allows to first generate a design space by enumerating all the combinations of parameters. Then, an a posteriori exploration of the solutions is done by a user. He can thus discover relevant and sometimes non-standard design solutions, considered "blind spots", which meet his objectives [START_REF] Du Manoir Geoffroy | Architecture and design definition processes: Return of experiment about complementary MBSE tools to model consistent architecture layers and to support design trade-offs through a set based concurrent engineering approach[END_REF]). The "blind spot" solutions for decisionmakers are the ones that they would not have thought of or that they would have found difficult to identify, as shown in aeronautics for finding optimal architecture of Mars helicopter drone [START_REF] Phan | Optimal architectures for a Mars helicopter drone: exploring the design space with Geeglee[END_REF]. Geeglee allows to easily brute-force enumerate all tuples of parameter combinations and provides a visualization interface for exploring the generated space.

Overview of our strategy

After studying DSE and its supporting tools, we formulate our strategy for design space exploration in this section.

The design problem has been studied, and a framework has been proposed in the knowledge model developed in Chapter 4. The design solution belongs to a certain domain, and they are linked mathematically to the global performances. To better explore the design space, Tweedie et al. (1996) encourage a pre-computation step dedicated to the generation of a design space before exploring the field of design points and selecting one or a set of best solutions. We follow this strategy to implement the design space exploration platform:

 First, a dimensioning algorithm allows us to compute all the combinations of the parameters constrained by the supermarket specification. It is based on a brute-force enumeration of the n-tuples of feasible solutions (dotted box in Figure 6-2). The solution space is in a discrete set for our study because we seek to dimension an optimal refrigeration system.

 After the first computation of design points, the design space is displayed in an exploration tool. All the solutions are presented, and the user can navigate in the design space, applying

successive "what-if" hypotheses and looking to the evolution of the feasible design space.

The user can reduce the design space by applying new constraints, also called decisions, on the performance space such as reduction of energy, costs, environmental impacts or on the decision space such as removing alternatives of refrigerant, compressor, architecture (see . Applying these decisions crops the design space to obtain an optimal solution.

The process is iterative by allowing the users to interact simultaneously with the performance space and the decision space (Abi Akle et al., 2019), to reduce or enlarge the domains of definition of the solutions and performances to converge towards one or several "crisp solutions". This paradigm is called "Design by Shopping" (Balling, 1999).

Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by [START_REF] Neema | Constraint-based design-space exploration and model synthesis[END_REF].

3. Supporting tool for developing a simulation and exploration of design space

Choice of simulation support tool

We seek to (1) simulate the behaviour of a refrigeration system and dimension it in a discrete solution space and (2) explore the design spaces under uncertainties for different stakeholders.

Different DSE tools exist, as presented in section 1.3. The choice for the simulation tool has been made based on the tool's availability, the developing interface, the capacity of the tool to support our knowledge model, the time and memory necessary for the computation of design points.

Geeglee was chosen as a DSE support tool for our model. One advantage is the visual interface of the tool that allows us to notice development errors easily. Moreover, the simplified visualization of the tool permits enumerate the system's structure and set up an incompatibility configurator in the form of a matrix. Finally, the programming language close to the common language facilitates the development. The tool can be in relation to another programming platform such as Excel and another programming language like Python. The tool framework is adapted to our knowledge model (see section 2.2.). It allows us to implement hundreds of equations and data. A collaboration with the Geeglee developing company has provided the free use of the tool as well as a guided accompaniment. However, the computational memory for our design problem requires a difficult storage capacity to access. Moreover, the visualization of the results has some limitations that we highlight in the chapter's discussion.

Modelling structure and language

Geeglee is divided into two complementary tools: Geeglee Engineering Patterns (GEP) and Geeglee

Engineering Intelligence (GEI). GEP is the tool used to model the system of interest, while GEI is used to visualize and analyse the design space data generated from the model created in GEP. GEI allows the designer to play with the design space by reducing the uncertainty to an optimal solution in a design-by-shopping manner by over-constraining the specification and making design choices [START_REF] Phan | Optimal architectures for a Mars helicopter drone: exploring the design space with Geeglee[END_REF].

The GEP tool is composed of four main sections:

(1) High Level Requirement (HLR) where the specifications are described (inputs, constraints, objectives...).

(2) Product Breakdown Structure (PBS) where the decision space is described (technical options for the system of interest and incompatibilities between technical solutions).

( The knowledge model representing the design problem is implemented using Geeglee tool with Excel, Latex, and Python in the platform. Five months were dedicated to the implementation in the platform with a full-time intern for three months. Several workshops with a graphic designer were used to refine the model and the graphical interface to fit several test scenarios.

Implementation

Implementation of the design problem in the tool

The simplified scheme of the design problem and the relation with the four objects of Geeglee is shown in Figure 6-3. The system environment is considered the inputs of the model. Different alternatives for each technical module of the system structure are implemented. The global performances are calculated thanks to mathematical equations concerning the inputs and the system structure. In our design problem, around 300 equations are computed divided into six main groups related to the calculation of the cooling power, the energy consumption, the environmental impacts, the maintenance score, the total costs and the system maturity. A part of the mathematical models implemented in the tool is presented in Chapter 5. The other equations defining the performances can be found in appendix 3.

Dimensioning a feasible solution

After implementing the variables, parameters and equations, the generation of the design problem is based on the resolution of a CSP.

Figure 6-9 shows the steps followed to dimension one solution according to the system environment constraints to search the feasible solutions. It gives a simplified example of the design steps to compute a feasible solution. It does not mean that this design point is the best solution. The algorithm repeats the steps for all the possible solutions, and the set of feasible design points generates the design space.

The system environment, i.e. location, supermarket specifications, defrost specifications, cold room (or display cabinets) specifications, allows us to calculate the frigorific power for each cold room or display cabinet. Then they are added to calculate the supermarket cold need, here only for food conservation. From this step, the required cooling capacity is calculated based on industrial experience and should be approximately 30% more than the calculated frigorific power. The required capacity can be divided between 1, 2, or more compressors (and the same for condensers). The required compressors and condensers must be discrete variables from [1; ∞[.

In Figure 6-9, the blue dash boxes are the parameters, and the red dash boxes are the incompatibilities parameters. The local performances are in the plum coloured boxes. The green coloured boxes are the refrigeration system structural solution.

As shown in Figure 6-9, there is a high interdependence between the variables of the problem thus the components' selection.

Figure 6-9. Simplified scheme of a design point computation algorithm

Once the design space is generated, all the feasible solutions are visualized in an exploration interface.

Simulation and exploration tool

In the previous section, we have seen how the design problem is set, how to dimension the feasible solutions based on the system environment constraints and how it is implemented using Geeglee.

In this section, we study how the visualization interface is used for design space exploration. Once the design space is generated (step 1 in Figure 6-10) as presented in the previous section, the user has access to an exploration platform. He follows two steps: setting the system environment (step 2 in Figure 6-10) and exploring, or reducing, the design space (step 3 in Figure 6-10).  Scenario 1: A supermarket manager has been proposed to change the refrigerant of its existing system using R404 to meet the regulatory requirements F-gas II. He would like to first test the performance of a new system with the new refrigerant R1234yf, and then make a decision, "Given the changes observed in the model, would he change the refrigerant in his existing system?".

 Scenario 2: After receiving a request to build a refrigeration system from scratch in a supermarket in Ile de France, the designer wants to design a refrigeration system to meet their initial budget and environmental constraints. This system must be able to respond to climatic and market evolutions. The company know-how constraints must be considered (in this case, the company's operators are not trained to use CO2 for the moment).

Setting the system environment for Scenarios 1 & 2

The same system environment is proposed based on the ADEME case study (ADEME and Enertech, 2001). We briefly present hereafter this case study. For more details, see Appendix 3. Figure 6-11 provides a simplified scheme of the case study.

The supermarket is composed of 25 display cabinets (DC) with a positive set-point temperature of 2°C, eight display cabinets and one cold room with a respective negative set-point temperature of -18°C and -20°C. The display cabinets can be vertical or horizontal with a respective mass of 50 kg and 30 kg food. To simplify the modelling, we consider that the food at positive temperatures is fruits in bulk and ice cream at negative temperatures. The total energy consumption for a whole year measured in the report is around 291 000 kWh. Then, the next step for both the supermarket manager (scenario 1) and the designer (scenario 2), is to set the system environment. For the store characteristics, the user selects the location Paris, the store area of 2500m², the opening hours (the store is open to customers) from 8:00 am to 8:00 pm, the working hours (the store is open for the supermarket employees) from 5:00 am to 9:00 pm and the number of employees working in the cold rooms of 10 persons.

The user selects then food conservation characteristics. First, it comprises the type of food (refrigerated or frozen) to establish the set-point temperature, i.e. 2°C for refrigerated food and -18°C for frozen food. These set-point temperatures are chosen based on the case study but could evolve. Secondly, the user selects the number of cold rooms and display cabinets. Finally, he chooses the mass of food. There is only one possible value for the mass of food for simplification purposes. Once the system environment is set, the feasible solutions are displayed in tabs 2. and 3. in the interface. Different possibilities of exploration are offered to the user:

 Evaluate an existing refrigeration system (Tab 2.1):

 Simulate its overall performance  Simulate the impact of a technical component change on its performance  Identify the best combinations of components according to a single or multi-objective analysis (From Tab 2.2 to Tab 2.6). This simulation can be used either on an existing system or for a new system. Several explorations are proposed on the platform that can allow the user to reduce the design space depending on the choices:

 Identify the components with the lowest total cost (evaluated according to installation and operational costs over the system's lifetime)

 Identify the components with the lowest energy consumption (evaluated per year according to the type and number of equipment, i.e. sales cabinet and cold room)

 Identify the components with the lowest environmental impact (assessed over the entire life cycle of the system)

 Identify the components and the most mature system (evaluated by the Technological Readiness Level (TRL) scale)

 Identify the components with the best maintenance score (assessed by a maintenance criteria scale)

 Identify the components with the lowest total cost and environmental impacts

Analysis of a single point in the design space: Scenario 1

In Scenario 1, the supermarket manager wants to observe the change in the performance of his existing system if he changes refrigerant (from R404 to R1234yf). The platform's goal is to provide a simulation of a single point in the design space.

On tab 2.1 of the interface, the manager must first select the components of his existing system, as shown in Figure 6 Once the components are selected, the supermarket manager has access to the performance space of his existing system. He has access to the global energy consumption, the environmental impacts, the total cost, the maintenance score and the technology maturity (obviously at maximum for an already existing system). He then selects the refrigerant change and can observe the changes in the performances. Figure 6-16 provides the comparison of the performances. It is interesting to note several points in the evolution of the global performances:

 The system's total cost increases by approximately 40% mostly due to the increase of the system energy consumption.

 The energy consumption increases by approximately 20%. This can be explained by the fact that only the type of refrigerant was changed in the simulation. The components already in place for the R404 refrigerant are not adequate for the new fluid with a different chemical composition and thus a different behaviour.

 The system's environmental impacts have a slight decrease because the R1234yf has a GWP of less than 1, compared to the R404 GWP of 3943. The difference could be greater if the system's energy consumption did not increase.

 There is a slight increase in the maintenance score because of the novelty of the R1234yf.

The significance of this difference must be discussed.

It is important to highlight the trade-off that faces the supermarket manager in this example. He can observe that the single refrigerant change can alter his system efficiency and increase the operational cost. However, the change decreases the system's environmental impacts and meets the coming regulation. To have more knowledge before making his decision, he can remove one or more constraints on one of the other components. For example, by removing the constraints on the compressor and condenser, new solutions are offered to him with better energy performances. Exploring the design space: Scenario 2

For Scenario 2, the designer wants to find the best refrigeration system corresponding to his client's requirements. The client provides a constraint on the initial cost and wants to minimize the environmental impacts of his system. The goal for the platform is to provide the set of all the feasible solutions and the Pareto-optimal solutions.

To meet his objective of finding the best system composition, the designer explores the design space by interacting simultaneously with the performance space and the design space using tables, 1D intervals and 2D plots, as shown in Figure 6-17. The interval bounds are the minimum and maximum of the performance space. In these intervals, all the feasible solutions are displayed.

Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right)

Then, the designer can choose to minimize the environmental impacts by using the 1D interval sliders to minimize the environmental impacts as shown in Figure 6-19.

Verification

The knowledge model described in Chapter 5 allows us to set the design problem in a clear framework. The platform's verification serves two purposes: to verify that the ontology corresponds to the domain's reality and to ensure that the proposed platform has been correctly implemented.

Not to confuse with the validation of users' objectives. The verification is based on three criteria [START_REF] Brank | A survey of ontology evaluation techniques[END_REF]:

1. Minimum ontological commitment, i.e., fidelity of the domain representation to the actual knowledge of the domain: the completeness and correctness of the equations, the relevance of the architectures, the components, the data.

2. The appropriateness of the initial organization of the knowledge represented in the ontology, 3. The choice of relations.

The verification is done in two parts: (1) a step-by-step verification throughout the knowledge model construction and its implementation on the platform, ( 2) a global verification by comparing the results with a real case study to verify the accuracy of our mathematical models. For the discussion, multiple questions were investigated, and the outcomes of the investigation are detailed in Table 7.

Step-by-step verification

Table 7. Verification investigation: questions and outcomes of the workshops

Verification by application

The objective of the first verification is to evaluate the first criteria, which is the fidelity of the knowledge model to the actual knowledge of the domain: the completeness, the correctness of the equations, the relevance of the architectures, the components, and the data.

To verify if the physics-based models, the system architecture model (components modelling and their interaction) and the problem modelling (intermediary variables) are well implemented in the platform, we simulate the global performance: energy consumption of a refrigeration system, for food conservation only, of a supermarket located in the Ile-de-France department. We verify the correctness of this global performance, thus ensuring that the equations, architecture, and local performance related to the energy consumption are all correct.

The data are taken from our main case study based on the ADEME report (ADEME and Enertech, 2001) presented in section 5.1 and the appendix. The energy consumption data in the report are collected thanks to different sets of sensors. As the data reported are from 2010, the refrigerant regulation from 2010 is applied, thus using R134 and 404 as refrigerants. The technological components of the solution are adapted from the alternatives implemented in the platform.

To verify the correctness of our model, the system structure should be the same or as close as possible to the real system in place. The system architectures for both temperature and the three types of conservation (horizontal, vertical display cabinets and cold room) are primary direct expansion architecture. As the type of compressor were not available, we only focused on the compressor power to establish the technological modules.

The comparison of the results obtained with the model and the data from the ADEME case study are

shown in Other factors can explain the differences:

 The simplification hypothesis for the food conservation (mass, type of food, type of cold room and display cabinets);

 The technological modules chosen for the tests are newly developed technologies and the energy consumption has been well optimized in the last decade;

 The general hypothesis on the supermarket specifications (number of employees, opening hours, constant temperature in the store, the humidity level, etc.)

 The simplified parameters for the dimensioning of the evaporator and heat exchanger.

Comparing the energy consumption between the simulation and the data from the report allows us to verify the correctness of our mathematical models. The results were also shown to several people from an installation company to ensure that the difference was acceptable.

It is necessary to emphasize that we verify the accuracy of our model by calculating a single overall performance. Indeed, the environmental impacts are verified by comparing different architectures.

The costs and maintenance scores attributed to the different architectures and components are discussed and the assumptions made are verified with a group of industrial actors.

Discussion and limitations

The proposed design and exploration platform answers our third research gap, i.e. the lack of user integration in the design process. We propose a digital tool to support the decision process addressed to different stakeholders. Indeed, the simple visualisation of the results provides a quick understanding of the challenges.

Two scenarios corresponding to two design activities are presented to explain the design space exploration strategy implemented in the platform. The tool serves different purposes. First, it can simulate the performances of a refrigeration system for commercial refrigeration. Secondly, it can provide design solutions and allow users to interact simultaneously between performances and solutions spaces. The user can thus navigate easily to discover one optimal solution that corresponds to his expectations, as explained in the "design by shopping" paradigm.

An integrated approach is implemented in the platform, based on the knowledge model proposed in Chapter 5. Five criteria corresponding to the five global performances are modelled and can help the user reduce the design space toward one solution.

This platform has shown the feasibility and interest of developing a platform for the design space exploration of refrigeration systems. It allows users to explore different scenarios for reducing energy performances simultaneously with the environmental impacts or reducing only the costs.

Some limitations of this study can be discussed.

 First, one performance has been calculated and compared to a real use case, the modelling of the other performances was discussed with industrials to check only if the assumptions made are correct. It would be interesting to obtain other data and compare the results obtained with the simulation.

 The second possible limitation is the visualization of the results that can be too primary to make a final decision. Information can be lost with a simple 1D interval representation.

 The design space and the optimal solution is limited to our choice of components implemented in the platform. For better platform usage, the model should be extended to add more alternatives, types of components, and more precise components characteristics.

Conclusion

The knowledge model is implemented in a design and exploration platform supported by Geeglee computer-aided design tool. Because of the support tool structure that fits well to the structure of the proposed knowledge model, the choice to use such a support tool is justified.

The verification of the knowledge model consists of two steps:

-A part-by-part verification, i.e., verification of the implementation of the platform by testing the implementation step-by-step to verify the correctness of the chosen architecture implemented, the technical components, their specifications, the equations of each local performance. At least two different persons make this verification.

-A verification of a global performance: energy consumption, to ensure the completeness and correctness of the knowledge model implemented in the platform by an application of a case study. The verification serves to prove that the model matches the performances of a real case study. The difference in the results between the model and the reference case study are discussed with experts to assess whether the model is considered correct despite the 12% difference.

Moreover, two conference papers allowed us to present the results and to compare our models on energy consumption and the environmental impacts of the solutions.

Two scenarios are shown as usage examples of the platform. It is now necessary to validate the approach proposed. Indeed, the interest and usage of the platform must meet the objectives of the domain's actors. They each have specific knowledge, and our platform must prove its robustness when confronted with industrial activities.

In the next Chapter, the platform is validated by evaluating eight criteria through two validation sessions with two panels of actors from industry and research.

Validation of the knowledge model by scenarios and by experts

This chapter presents the evaluation process of our two design artefacts: the knowledge model and the design space exploration platform. First, the validation framework and the criteria are chosen based on relevant literature. Then, the experimental protocol is displayed. Scenario-based validation of the knowledge model and the proposed platform are conducted with different stakeholders.

The results of the validation session are discussed, and the proposed design artefacts are evaluated.

Introduction

To answer our second research question on how to support the decision process for refrigeration systems in the early design stages, we proposed developing a knowledge model (Chapter 5) and a design space exploration platform (Chapter 6).

The chapter is divided into six sections. The first introduces the objective of this chapter and its position in this dissertation. Section 2 provides the validation framework, discussed with the appropriate literature review. In the third section, the experimental protocol of the validation session The proposed research outcomes in this Ph.D. thesis need to be assessed with a proper framework [START_REF] Pries-Heje | Strategies for design science research evaluation[END_REF]. Thus, this chapter aims to evaluate the proposed knowledge model and design platform through analysis and empirical study using eight criteria. The final goal of the chapter is to validate the utility, quality, and originality of our design artefacts. We use a descriptive evaluation to fulfil our objective by developing appropriate scenarios that validate the criteria [START_REF] Hevner | Design science in information systems research[END_REF].

Validation methodology

Description of the methodology

The importance of a relevant validation framework is highlighted in many works in design science [START_REF] Hevner | Design science in information systems research[END_REF][START_REF] Peffers | Design Science Research Evaluation[END_REF][START_REF] Rosson | Chapter 7 -Usability Evaluation[END_REF][START_REF] Sonnenberg | Evaluation Patterns for Design Science Research Artefacts[END_REF], where different types of design artefacts can be identified:

-Constructs are set of concepts that form a domain based on statements, other concepts... [START_REF] Cleven | Design Alternatives for the Evaluation of Design Science Research Artifacts[END_REF] -Models are "simplified representation of reality documented using a formal notation or language." [START_REF] Peffers | Design Science Research Evaluation[END_REF] -Methods are logical and conceptual steps to perform an activity.

-Instantiations are concrete realisations of the other artefacts through implementing a hardware or software or part thereof.

-Frameworks are meta-models.

-Theories are cause-effect relationships observation [START_REF] Cleven | Design Alternatives for the Evaluation of Design Science Research Artifacts[END_REF][START_REF] Venable | The role of theory and theorising in design science research[END_REF] In the present work, two design artefacts are proposed. First, a knowledge model (or ontology) was developed and second, a design space exploration platform, i.e. an instantiation of the model, was implemented. A strategy must be developed to evaluate our design artefacts. This involves the choice of appropriate evaluation criteria. Moreover, a proper evaluation framework should be developed, with users' choice, the experimental protocol and the interpretation of the results.

To assess the relevance of a design artefact, one must assess the different artefact's dimensions. [START_REF] Prat | Artifact Evaluation in Information Systems Design-Science Research-a Holistic View[END_REF] propose a synthesis of the system dimensions to evaluate the associated criteria and sub-criteria shown in Figure 7-2. For each of the dimensions, the criteria proposed in the literature help to answer a more general question:

-Goal: Does the artefact meet the intended purpose?

-Environment: Is the artefact consistent with its environment?

-Structure: Is the artefact's structure (technical aspect) valid (complete, clear...)?

-Activity: Is the artefact functional (activity aspect)?

-Evolution: Does the artefact adapt to its environment?

Figure 7-2. Hierarchy of criteria for design artefact evaluation from [START_REF] Prat | Artifact Evaluation in Information Systems Design-Science Research-a Holistic View[END_REF].

The literature presented above allowed a validation framework based on eight criteria associated with the five dimensions. Their definitions are presented in Table 7-1.

Table 7- The degree to which the platform gives a holistic view of the problem: Does the proposed artefacts (ontology and platform) enrich the user understanding of the design process?

Evolution

Robustness

The response of the platform to changes in the environment: Are the results relevant if the inputs vary?

Three types of evaluation [START_REF] Cleven | Design Alternatives for the Evaluation of Design Science Research Artifacts[END_REF][START_REF] Hevner | Design science in information systems research[END_REF] are used in our validation procedure. The first category of evaluation is achieved through observations. It comprises observation of the testers' behaviour and post-session comparison of the results, i.e., design solutions obtained during the session. The second category of evaluation is quantitative measurement post-session using a questionnaire and measuring the responses on a scale from 0 (disagree entirely / absolutely not) to 5 (agree entirely / absolutely). The third category of evaluation is qualitative analysis post-session of the discussion and written feedback. For each criterion, one, two or three evaluations are proposed, as summarized in Table 7-2. We have proposed a set of criteria and forms of evaluation for our validation process. The participants of the validation process are now discussed.

Target participants

To target the participants, we distinguish four general baseline scenarios or user stories [START_REF] Cohn | User Stories Applied: For Agile Software Development[END_REF] that correspond to the activity description of four different stakeholders. As described in the industrial diagnosis chapter, many actors are involved in the design process of a refrigeration system. Some of the experts interviewed during the diagnosis are the targeted testers of the proposed approach. Table 7-3 summarizes the four stakeholders with their associated tasks. Each of them has of course, its own knowledge of the domain (minimum, medium or expert knowledge). The testers involved in the validation process were not part of the ontology construction nor the design space exploration platform. observe the impacts on the performances of the system and make a decision based on the results.

Designer/Design office

Design a new refrigeration system from a client's request: find the best system structure by making a trade-off between different performances and in respect of the client's requirements.

Supplier/Manufacturer

Evaluate how the addition of a new component in the design space will impact all the performances of a refrigeration system: assess the potential of a technical module.

Researcher

Evaluate the potential of a new promising technology: identify the most influential parameters on the global performances in an industrial use case.

To illustrate the approach, we present the case of a designer commissioned by the supermarket manager. The script for the experimental protocol is based on the task of a designer to find the best solution for a design problem given by a supermarket manager.

Description of the experimental protocol

Two validation sessions with the same scenario were held with different types of actors. The validation sessions lasted around 2:30 hours, and they are divided into three parts (Figure 7-3).

Figure 7-3. Timeline of the validation session

The first part consists in introducing the session. A presentation is made on the objectives of the validation, the concepts, and the tool. A first questionnaire is provided to evaluate the relevance of the testers. They are asked about their current position, their training, if they have ever participated in the design of a refrigeration system, their level of knowledge about computer-aided design (CAD) tools and refrigeration, and how they perceive the use of CAD tools, i.e. the usefulness of such tools.

The second part consists of a test session of the platform. Before leaving the testers in complete autonomy, a tutorial of the platform is explained with the two usage examples presented in Chapter 6, i.e. a supermarket manager wants to observe the evolution of the performance if he changes the refrigerant of his existing system and a designer wants to find the best refrigeration system solution for his client's objectives. With the demonstration of the platform, the efficacy, validity and completeness (technical) are discussed. Once the testers have understood how the platform works, an application is provided. The application specifications are based on our main case study presented in the verification section of Chapter 6 and reminded in Figure 7-4. The context and constraints are fictive. The testers are provided with the script of the scenario. As described in the knowledge model, a scenario is composed of a user, its objective(s) and a system environment. The testers put themselves in the shoes of a refrigeration system designer. They receive a simplified specification with the commercial refrigeration needs of a supermarket, and the regulation for refrigerant is supposed to be the same as in the report (HFCs are still available). Figure 7-4 shows the simplified scenario/script given to the testers. During the session, the testers' behaviour is observed to evaluate the ease of use by measuring the frequency of solicitations. Observing the decision process logic, with and without the client's constraints, serves as a form of evaluation for completeness (decision-aid support function). After the validation sessions, the design solutions obtained through the different tests are compared to evaluate the consistency. The third part consists of filling a post-questionnaire and collecting feedback through written answers and discussion with the participants. It allows the artefacts' completeness (integrated approach) and robustness to be evaluated.

Results

Participants

In the validation sessions, four participants were present. They belong to the same refrigeration system design and supply company. It is a medium company specialized in the design, installation, maintenance and life cycle management of refrigeration systems. The company field of activity is commercial refrigeration, industrial refrigeration and air conditioning. The participants have different experiences in the refrigeration domain, and they all have a specific mission within the company. The four participants are a project manager more involved in client relation and design, a project manager more involved in the installation and life cycle management of the machines, a technician manager and the company CEO.

Analysis of the "autonomous" testing

After presenting the use case to the testers, they are expected to find a design solution for the supermarket manager with the support of the platform. They identified the system environment and the client's constraints in the script. The first step for each group was to set the client's specifications in the platform, as shown in Chapter 6. Then, participants were autonomous on the whole decision process.

Once the environment is set up, participants have access to the design space comprising all feasible solutions. During the first exploration of the design space, the participants do not have access to the 2D line plot with the Pareto space (See Chapter 6). They can only crop the design space one constraint at a time in an imposed sequence.

In this first step-by-step approach, they all followed the same decision process. The first constraint applied to the decision space is the client's maximum budget. From approximately 30,000 initial solutions (without budget constraint), the design space is reduced to 8,700 solutions for all the tests (with budget constraint). Once the design space is cropped, all participants applied a second action to minimize the environmental impacts by moving the slider related to environmental impacts.

However, different behaviours are observed among the participants: they can move the slider completely to the minimum, reduce the environmental impacts by half, or reduce the slider slightly as presented in Figure 7-5. For the second part of the testing, the users have access to the Pareto space (see Chapter 6).

All of them chose to continue exploring before making a decision mostly because of the rapidity to simulate the design space and to see how other constraints on the criteria can change the solution.

This action of reiteration and observation allows the participants to investigate the problem.

Moreover, they have the possibility on the design space exploration platform to obtain Pareto optimal solutions on the two criteria: economic and environmental. By selecting the filter to display only non-dominated solutions, the design space is cropped from 30,000 initial possibilities to 366 solutions. From the qualitative data collected during the sessions, the participants found the provided option "useful" and "facilitating the decision".

Once the solutions are displayed on the platform, each participant's knowledge and experience of refrigeration systems is what makes them choose a solution. For example, one participant excludes plug-in systems from the solution mostly due to a lack of knowledge of the system. One participant was interested in a particular refrigerant: he chose the alternative R290 and observed the other performances of the system; then, he decided that this refrigerant was not interesting and opted for another option.

Two solutions are found in the first approach. One participant made three filtering actions on first the cost, then two distinct ones on the environmental impacts before finding a solution: secondary architecture using R717 as a primary fluid.

The other participant found a design solution based on the Pareto space provided. No other constraints were made, and they both obtained the same design solution: a transcritical booster.

After this stage, they were asked if the found solution among the proposed alternatives satisfied them. For two of the three participants, the solution satisfies the requirement. The unsatisfied participant is the one who took the decision by constraining to the minimum the environmental impacts. Indeed, the design solutions were only two and the participant chose a solution by default among the two.

A second part during the autonomous testing was proposed to the participants. The constraints from the clients are "removed", and we observed the logic for reducing uncertainty, particularly the decision-making logic [START_REF] Aspers | Forms of uncertainty reduction: decision, valuation, and contest[END_REF]. One important observation is the diversity of the strategies.

Indeed, four different scenarios are witnessed on four participants, as presented in Table 7-4. It was asked for the participants not to communicate with each other and choose how to crop the design space. For the users with the higher responsibilities in the company, the most important criterion is the maturity of the technology developed. This is explained according to the participants by the significant risk that can be taken in developing a low TRL technology. During the discussion, participants reached a consensus on economic criteria: total cost is an important factor in decisionmaking. However, access to results on the environmental performance of the systems could be an additional argument in case of hesitation on a set of solutions. Another interesting point observed during the test is participant 4, who is closer to the technicians and for whom maintenance is the most important criterion for choosing the design solution. In fact, his thought process focused on the ease of operation during the installation and operation of the system.

Table 7- The results obtained for the four scenarios of cropping the design space are presented in Table 7-5.

It is interesting to observe that without particular constraints, the results depend on the user background. Our platform allows designers from different backgrounds to intuitively apply their successive filters in the order that suits them (design by shopping) to arrive at their preferred solution(s).

The first solution found by participant 1 corresponds to the system implemented in the case study system. The energy consumption is of 252,696.3 kWh. These results are in line with placing technological maturity as the first criterion. Indeed, direct expansion systems are very common and well known in the industry.

Secondly, the two proposed solutions correspond to the requirements of the participants. The energy consumption of the secondary loop system (participant 2) is of 252,696.3 kWh and the costs is approximately 10% more than the direct expansion system. However, the environmental impacts are reduced by 15%. The participant made a trade-off between cost and environmental impacts to choose this solution.

The third system (participant 3) achieves approximately the same energy consumption performance as the first participant, as it uses a direct expansion system. Moreover, in the model the installation cost depends mainly on the architecture, so the costs vary only because of the difference in the refrigerant. The two participants were offered with the same set of solutions and they did not choose the same result as they each have their own experience.

Finally, the cost of the transcritical system (participant 4) is also more expensive than the direct expansion system, mainly because of the higher energy consumption of this system, but the environmental impacts are reduced.

Table 7- The result of the autonomous testing allowed us to provide a recommendation of usage of the artefacts. Remember that the knowledge model provides a formal representation of the design problem. Then it is implemented in a design and simulation platform. Because of the data and the availability of the testers for commercial refrigeration, we proposed a use case on the design of a supermarket refrigeration system. We recommend using the platform as follows:

 First, the design problem should be clearly defined following the established knowledge model.

 Then, the user can enter the system environment before generating the design space, as was done in our case to facilitate the computation.

 Finally, the design space exploration can be done in several ways: either by navigating iteratively between the performance and solution spaces to reduce uncertainties or by visualizing the criteria two by two as it was done for the environmental impacts and the cost.

In the latter case, the non-dominated solutions can better understand the problem, as shown in our evaluation process.

We have analysed the concrete results and the design solution obtained for the participants. In the next section, we provide the results of the evaluation criteria.

Evaluation of the criteria

In this section, the evaluation of the knowledge model and the design platform based on the eight criteria is discussed.

Efficacy

The efficacy is evaluated by observing if the platform provides a result at every use of the platform.

The platform displayed a result at each test for all the validation sessions. Indeed, zero, one or more design solutions are provided. The participant evaluates the fluidity of the platform on the reaction time of the tool when an action is taken. They all consider that the platform is fluid enough.

Validity

The participants were asked to grade how they perceived the usage scenario proposed in the questionnaire. From the post-session analysis of the questionnaire, all the participants found that the approach is relevant and corresponds to real design problems that they can meet during their activities.

Ease of use

Two evaluation forms serve to evaluate the criteria: observation and quantitative measure of the frequency of solicitation. During the autonomous testing, the participants asked comprehension questions about reading the graphs. Indeed, one limitation is highlighted on the interpretability of the 1D intervals. Some information is lost with the proposed visualization of results. In fact, it would be more relevant to have access to plotlines on the performance results of the different alternatives in addition to the 1D representation. Thus, for a better understanding of the design solutions, a comparison of the solutions with all the performances would help the usage of the platform. For example, using 2D plotlines, as shown in Figure 7-6, could be a simpler way to represent the solution.

A presentation to the participants of this type of graph allowed us to consider an evolution of the representation of the design space. In this type of graph, the "best" solution is highlighted. The user can then explore the performance space and observe how constraining a global performance can change the "best" design solution. For the panels of testers, the implemented global performances are relevant. The level of detail is sufficient to investigate the alternatives.

On a technical level, other aspects of the design problem can be added. For the physical aspects of the solution, it would be relevant to add the modelling of the components degradation during the system's life cycle. Moreover, for usage in an industrial use case, the level of detail for the evaporator is too macroscopic. Instead of proposing default evaporators as proposed in the platform, the evaporator/heat exchanger sizing should also be visualized, and different choices are necessary for the designer to make decisions.

It is important to note that the completeness of the platform, i.e. the number of global performances provided, satisfies the interrogated testers. However, the correctness of all these performances has not been completely tested because of the lack of available data. Indeed, for environmental impacts, the results obtained in the platform were compared with the literature data presented in Chapter 2.

The maintenance score and the costs hypothesis are discussed with industrial experts. However, it is necessary to compare the results for these three performances with a real case study for future development.

In addition, a discussion could be held with two actors of end-of-life treatment of refrigeration systems. Environmental performance is summarized in our design artefacts as the overall global warming potential of the system during its life cycle. Therefore, it would be relevant to include all categories that can be found in LCA and include an end-of-life treatment cost in the cost calculations.

Consistency

The comparison of the design solution obtained for each of the participants points out that the results vary depending on the mindset of the participant. However, for the same inputs, they all provided a satisfying solution.

The validation session was done with only one computer on which the platform was developed, so the users were not able to solve the problem at the same time, which may have affected the solution obtained in the end. Indeed, they could see the other testers' reduction order, whicht might have influenced their choice afterwards.

Completeness (Decision aid support function)

The preliminary analysis of the decision support functionality of the platform allowed us to conclude on the interest found by the testers. First, the accessibility in the same supporting tool is relevant. It was also highlighted that the platform could quickly evaluate a set of possible design solutions for a set of specifications. The testers can thus become aware of the diversity of the possible solutions and quickly dimension a feasible solution. Amongst the written feedback, it can be noted that participants found the exploration tool to be "time saving in the selection of the system configuration", "facilitates decision making" or "serves as a repository".

The "path" to arrive at an optimal solution depends on the user, and so the solutions can diverge (see example in Section 4.2 with the results obtained according to the path).

Finally, the visualization of the technical maturity, environmental performances and maintenance score provides originality of the platform and brings a different vision to the user. In the case of a supply company, additional arguments can be provided to the client to go towards a particular design solution. Remember that a problem identified in the diagnostic is the lack of communication between several actors involved in the decision process. The platform addresses several levels of knowledge and can thus help to improve communication. In summary, all the testers found that the platform could be used in their business.

space was compared. For the various tests, the platform provides a result. The results obtained cannot be evaluated in a robust way due to the lack of information available during this thesis. However, we can conclude on the modularity of the proposed platform. All the participants interviewed found the platform to be of high quality. The modularity of our platform serves to ensure a possible evolution by respecting the steps followed in this study, i.e. the adaptation of the knowledge model and the implementation in the tool.

For further evaluation of the platform, the test with another industrial case study should be carried out.

Discussion and limitations

In this chapter, the objective is to validate our artefact based on eight criteria: efficacy, validity, ease of use, completeness (technical), consistency, the completeness for the decision support, the integrated approach and robustness.

The users have shown interest in the proposed platform during the validation sessions. Indeed, the platform is usable in industrial conditions and provides results. The testers have described the tool as powerful, enabling design problems to be solved by visualising essential performance, including CO2 emissions and energy consumption. Our approach's essential interest is linked to the possibility of implementing different kinds of performances within the same platform necessary to solve a design problem for refrigeration systems. We were thus able to show that a certain number of tradeoffs must be made to reach an optimal solution. Moreover, a top-down approach was highlighted in the literature review, excluding the user in the design or decision process. On the contrary, the present platform is based on a design space exploration paradigm that puts the user at the center of the decision process. It has been observed that each user, having his own experience of the domain and of design, can have very different results depending on his choice of criteria to lower uncertainties.

The platform was used to have a quick idea of feasible solutions through a simplified but realistic supermarket case study. To evaluate the robustness of our proposed artefacts more thoroughly, it would be relevant to consider testing the platform with another industrial case study.

Due to lack of time and available actors, some scenarios have not been tested. Indeed, we proposed four main scenarios that correspond to real activities that could be observed during the field diagnosis. In the validation process, we only focused on the design task consisting of finding a design solution for a client need. The other activities should be tested in future developments.

A demonstration of the platform with end-of-life actors for refrigeration systems revealed some possible evolutions. First, the stakeholders noted the relevance of such a tool to include waste management objectives. We recall that refrigeration systems are considered waste electrical and electronic equipment (WEEE) and subject to waste treatment standards. However, one point raised by stakeholders during the diagnosis is the lack of visibility of new technologies available and therefore degraded waste management. Several scenarios could be implemented in the platform:

the reuse of components such as compressors in a solution and the possibility to see how the components are constructed (metal composition, interlocking...)

Recommendations for the use of the platform could be proposed. On the organizational scheme that can be found in companies that use BIM, we propose a person in charge of the enrichment and the proper functioning of the artefacts, knowledge model and platform. Indeed, a Building Information Modeling (BIM) manager is a person in charge of implementing BIM within a company and ensuring the proper functioning and use of BIM tools.

Research question 2: How to support decision-making for the design of refrigeration systems in early design stages and in the most integrated way?

The second research question arises from the lack of an integrated approach (research gap 2) and user integration (research gap 3) in the design process.

To address the lack of an integrated approach, we developed a knowledge model, or ontology, to design refrigeration systems. Four classes were defined to describe the scenario, the refrigeration system structure, the properties and the performances to assess. The knowledge model was used to set the design problem for the refrigeration system. It reuses existing ontologies found in the literature related to the design, optimization and simulation of complex systems. A standardized language was finally implemented into a design space exploration platform from this knowledge model.

We developed a platform based on the design space exploration paradigm to address the lack of user integration. The proposed strategy is based on two steps: a brute-force enumeration and an exploration of the design space composed of the decision space and the performances space. The platform allows users to find solutions to their design problems quickly. Moreover, the platform enables users to navigate between the solution space and the performance space in a quasisimultaneous manner. Users can thus explore a multitude of "what-if" scenarios, i.e. if the user changes a parameter in the solution space, how will the performance space respond and vice versa.

The platform should be used in early design phases. Indeed, integrating several environmental, economic, maintenance and technological maturity performances within the same platform brings new knowledge to users from different trades. Therefore, it is better if the users are involved in the design process early.

The proposed solution addresses the limitations of bottom-up approaches by placing the user at the centre of the decision. Indeed, the validation step showed that the design space is explored differently for each user as the choice of an optimal system involves (i) the choice of users to restrict the performance or solution space and (ii) the users' understanding of the system.

Finally, the platform addresses several users with different levels of knowledge. It allows users from different backgrounds to intuitively apply their filters in the order that suits them (design-byshopping) to arrive at their preferred solution(s).

Our artefacts, i.e. the knowledge model and the design space exploration platform, are evaluated through eight criteria: efficacy, validity, ease of use, technical completeness, consistency, functionality completeness for decision support and integrated approach, robustness.

According to the validation results, the users found a real interest in using the proposed design space exploration platform. The design space can be satisfactorily reduced with successive choices to come up with an optimal solution, in contrast to the traditional optimisation approaches applied in other refrigeration system design tools. Indeed, it allows the design actors to be more involved in the design process, understand the stakes of the tradeoffs, and be confronted with other points of view than their own, which cannot be done with classic optimisation.

Limitations

In this Ph.D. thesis, some limitations on the design artefacts can be highlighted:

 The knowledge model can be used and further developed only by a person with a high knowledge about ontologies.

 The proposed platform for the design space exploration of refrigeration systems provides different performances visualization. However, the visualization of the results on the exploration interface is too basic. Indeed, the results in 1D intervals can confuse the user and lose information.

 The verification of the energy consumption has been made based on a case study from 2010.

The other global performances were either compared with literature, or the hypothesis confronted by refrigeration experts. However, to ensure the robustness of the model, comparisons with a real and actual case study should be made.

 The validation process allowed us to prove the interest of our study and the developed artefacts. However, due to lack of time and available testers, only two validation sessions were conducted with experts from the same company. Extending the validation session to other experts and other design activities would be relevant. Moreover, only one case study has been used and to improve the detail level of the model and its quality, and several other scenarios should be applied.

Perspectives

Several points can be considered to improve the work presented and improve the limitations of this work.

 First of all, updating the field knowledge is necessary to make the model evolve. Moreover, the proposed knowledge model can be generalized to other areas. As a first step, we recommend further modelling of the supermarket, taking into account the integrity of the supermarket energy system.

 The experts interviewed showed an interest in the platform developed. However, for use in an industrial context, especially for installers, it would be necessary to allow the designer to size the evaporators. In the same idea, the modelling of the behaviour of all components should be more developed.

 A discussion in a session with end-of-life stakeholders led to the prospect of developing the tool for end-of-life scenarios. Examples include adding reused components, integrating dismantling costs, offering a split view of the system components to prepare the treatment channels for new market technologies.

 Finally, a short-term perspective would be the addition of low maturity technologies to test a scenario with a research actor. In particular, it would be interesting to test technologies developed in the laboratory, such as Thermal Energy Storage (TES) with phase change materials (PCMs) on an industrial case and to understand what elements are missing for possible industrial development.

General conclusion

This research work is at the crossroads of two disciplines, refrigeration process engineering and industrial engineering, to propose solutions aiming to improve the design process of commercial refrigeration systems.

In the first chapter of this thesis, we presented the general context and our research objective to offer a global approach that will help manage the research and development (R&D) and decision making for refrigeration.

The second chapter offers a literature review on relevant work related to our main research objective.

Three focuses are made on: refrigeration systems structure and particularly commercial refrigeration, industrial engineering tools, and refrigeration systems modelling platforms to support the design of refrigeration systems. Industrial engineering exploration allows us to identify relevant methods and tools for supporting complex systems design. Moreover, a comparison of the existing relevant tools to support refrigeration systems design is established. This allows us to identify the research gaps toward an integrated solution to design sustainable refrigeration systems.

The third chapter proposes two research questions and the associated objectives, based on the research gaps analysis. The research methodology driving this thesis is also presented. Our thesis framework is based on the design research methodology (DRM) (Bleesing and [START_REF] Blessing | DRM: A Design Reseach Methodology[END_REF] framework.

We proposed in the fourth chapter an answer to our first research question on how to represent of the refrigeration socio-technical system. For this purpose, the RID (Radical Innovation Design) (Yannou, 2015) methodology was used to build a book of knowledge divided into four categories: users, life cycle stages, problems, and existing solutions. Two RID tools were used, the RID comparator and the RID compass, to highlight development opportunities, called value buckets. The main shortcomings that emerged from this analysis are: (1) a lack of understanding of the technologies, (2) a weak exploration of the design space, (3) language and tool gap between the stakeholders resulting once again in an underperforming refrigeration system.

The diagnosis confronted our proposed research objective and confirmed our second research question on how to support decision making for the design of refrigeration systems in early design stages and in the most integrated way. A solution in two parts was proposed: the building of a knowledge model, or ontology, and the implementation of a simulation and design space exploration platform for refrigeration systems.

The fifth chapter develops the knowledge model. The knowledge model reuses other ontologies and proposes a novel framework for refrigeration systems design. Four macro-concepts were defined:

the scenario, the refrigeration system structure, the properties and the performances to assess. An example for a supermarket refrigeration system is presented to explain the usage of the knowledge model. The knowledge model is used as a clear and integrated representation of the design problem to implement in a digital tool.

The sixth chapter presents how the knowledge model is implemented in a design space exploration platform. The Geeglee tool was chosen for this implementation. It is based on two main steps: a brute-force search to generate all the feasible solutions and an exploration phase based on the design by shopping paradigm. Five performances are assessed simultaneously: energy consumption, environmental impacts, total cost, maintenance score and technological maturity. A verification process has been established. First, a step-by-step verification was made throughout the implementation process using experts' workshops. Second, an application verification is made to compare the results with a real use case.

The seventh chapter presents the evaluation of the design artefacts by validating eight criteria: efficacy, validity, ease of use, technical completeness, consistency, functionality completeness for decision support and integrated approach, robustness.

Finally, the answers to our research questions have been discussed in the eighth chapter. The limitations and perspectives of our research contributions are listed.

The proposed approach showed the value of using design space exploration to size a supermarket refrigeration system. The starting point was developing a knowledge model to represent the problem correctly. This approach could be extended to other design problems, especially for designing other thermal systems, following the same steps applied in this research work.

Appendices

1. Appendix 1: Glossary

Name Definition Ambition perimeter

A subset of important value buckets that people also desire, complying with strategic considerations (core competencies, brand image, expected benefits, technological and market roadmaps, easiness to develop, etc.).

Business model (BM) solution

The plan for creating, delivering, and capturing the value from a PSO architecture.

Cognitive model of activity (after RID)

A simplified representation of how an activity is lived by some user profiles (here RS stakeholders), facing some problems or lack of performance during usage situations (here RS lifecycle stages), and for which existing solutions are variably effective.

Dreamt usage scenario

A usage scenario of the future desired activity where the entire solution is not represented but its desired effects are illustrated in narrative form.

Effectiveness indicators

Set of indicators given as a percentage, expressing the ability of solutions (existing ones or the innovative one) to eradicate a part of the quantities of pains for some problems or for some usage situations.

Ideation

Process of generating new ideas. In RID, a first "scenario creativity" session transforms value buckets into one or several dreamt usage scenarios, and a second "solution creativity" session transforms a dreamt usage scenario into a PSO architecture and a BM solution.

Product-service-organization (PSO) architecture

A cohesive solution of product, service and organization (the network of stakeholders that operates the product-service system)

Quantity of pains

Quantity of pains is an original extensive semi-quantitative measurement scale made of (a size of a User Profile) × (a frequency of Usage Situation) × (an occurrence and gravity of a Problem).

Radical Innovation Design (RID)

RID is a methodology to innovate on (i) an activity from the viewpoint of its beneficiaries or (ii) on a technical component's role from the viewpoint of the system performance. It is a Design Thinking process with visual management facilities, decision-making tools, together with a project and knowledge management facilities.

Socio-technical RS

Set of stakeholders, processes, organization and technical solutions involved in the activities linked to the RS along its lifecycle phases (design, manufacture, use, maintenance, technological updates, end of life).

Value bucket (VB)

A major problem (with serious consequences) for a given user profile that arises during a frequent usage situation and for which existing solutions provide too little or no relief the exploitation phase, the risk increases with the possibility of a component's obsolescence, human error and refrigerant leakage. The impact is an increase of costs for the final user mostly.

Risk of unexpected investment costs

In the first usage situation, a risk can occur if all the scenarios are not taken into account: If the time of respond to the new regulation from the installer is too long, a risk of high costs is inevitable and a lack of adaptability can lead to bankruptcy in the worst scenario. For academic or R&D researchers, an inconclusive research is yet rare but can occur and, in most cases, does not lead to lots of damage. In the EOL treatment, unexpected costs occur without the knowledge of the concerned actor as in most cases there is a lack of knowledge on the possibility of EOL parties to treat their waste. It can lead to waste left on site (cause of poor recycling rate).

Risk of break down

In the exploitation phase, break down are frequent but as the system are robust, it is not fatal. It can be mechanical, electric or design related break downs. As the outside temperature increases in the summer and the maximal temperature has never occurred before, more systems are found to not be designed to support it.

Risk of high operational costs (OPEX)

In the exploitation phase, the electric consumption of the system is the most important operational cost. The most frequent reasons are the maintenance of the systems and the energy loss. (Evans et al., 2014)

Knowledge management

Decrease of the recruitment quality

As the sector is changing, the formations of new technicians or engineer decrease. From a 2016 study by the DARES (Direction de l'Animation de la Recherche, des Etudes et des Statistiques, dépendant du ministère du Travail), more than 60% of new graduates are not qualified enough of a work.

Lack of refrigeration technicians and engineers

As industrial demand increases, the refrigeration sector is lacking refrigeration technicians or engineer.

From the professional organism called SNEFFCA, the sector is always recruiting (more than 4 000 recruitments per year for 2 000 small or medium companies) but the number of applications reduces.

Lack of understanding of new technological clusters

In all the usage situation, new technological clusters are often not completely understood as there is no time to keep up with the changes.

Interactions between stakeholders

Disturbed process of advice to the final usage of the system As the regulation change and the initial structure of the companies change according to the change in regulation, the process of advice for the final user is disturbed. The top down approach in the current process during the first LC phase leads often to non-optimal system recommendations.

Lack of flexibility for the suppliers

During the first LC phase, a frequent problem is that the final user specifications given to the supplier can constrains the design space of the design office.

Risk of discomfort for the final user's customers During the exploitation phase, either during normal use or maintenance, an uncomfortable noise can be heard. This problem is not frequent and has no significant damage on the users but can harm the company image.

Risk of high danger for human health

In all the LC phases, the use of refrigerant can be source of human's health problems. It can be toxic or inflammable and thus can start a fire or damage the health of a human in case of ingestion. Moreover, the current use of CO2-based systems can be dangerous as the equipment are under high pressure. An accident is extremely rare but can be fatal.

Tool

Poor exploration of the design space

During the RS design phase, the design office is subject to a strong time constrain that prevent an optimal exploration of the design space.

Lack of common efficient tools

During each phase of the LC, multiple tools are used by the stakeholders. Each actor can have specific tools either internally developed or tools used in multiple domains. Some of them are considered not efficient because they do not answer to an industrial need.

Risk of high environmental impacts

The environmental impacts of the systems occurs in all the LC phases. During the exploitation and the EOl treatment, a risk of higher negative impacts is important due mostly to frequent the refrigerant leakage and high electric consumption resulting in high indirect impacts.

Poor recycling rate

In the EOL phase of the process, it is mandatory to empty the refrigerant from the machine. However, the components of big industrial systems can be left on site, mostly because of higher costs of dismantling components or because of lack of knowledge of the EOL treatment parties that can manage the EOL.

2.2.

UsEs matrix During the design phase of a refrigeration system LC, most activities are made with software solutions and helped sometimes by the EN378 guidelines and/or by methodological solutions. Technical solutions and tools are only used for manufacture of components or to put in place the system.

During the exploitation phase, most of the solutions are necessary. Indeed, the exploitation phase of a RS is the longest period. KM and tool solutions are the frequently used, one for the formation of the refrigeration engineer on maintenance or technicians and the other are necessary for the maintenance of the machine. EN 378 guidelines are mostly used in this phase as there are norms that need to be respected (such as a frequency of maintenance, the components that need to be checked…).

The break down are reported directly to the supplier by the intermediary of software that detect a break down and put the machine in a degraded state to maintain the system running. Then, at the intervention of the technicians, technological and methodological solutions are used to change the broken components of the machine or to upgrade the system.

In the last phase, the tool solutions are always used by the practionners to handle the EOL of RS. A RS is submitted to the norms for DEEE product and the norm EN 378 as a refrigerant is contained inside the system.

EsP matrix

Matrix 𝐸𝑠𝑃 crosses Existing Solutions and Problems. The question asked to fill the matrix is: "To what extent does this existing solution eliminate or lessen the problem?"

The figure gives the detail of EOL treatment of refrigeration systems in France once the system has been emptied. Based on the expert's interviews and the documentation, it appears that most of the user parties appear in the first phase of the LC. Indeed, once the machine is installed and running, the frequent users concerned by the exploitation phase are the final user (as the user of the machine on a daily base) and the refrigeration engineers by the intermediary of the supplier. They must frequently verify the system (directive 2002/91/CE), check of refrigerant leakage, maintain the system or repair the break downs. If the system breaks and there is no usual way of repairing the system, the manufacturer company is contacted, and the R&D team can be contacted to palliate to the issue. Professional organism such as Bureau Veritas sometimes inspect the running system to report the performances of the system (optimized dimensions, measurement of the energetic performance, upgrade recom-mendation…). The EOL of the system is the phase that require the less actors. The EOL treatment parties are frequently necessary for the transport, treatment of the machine. The refrigeration technicians oversee emptying the refrigerant in the machine. The final user of the system is involved from time to time in the EOL of the machine, but it is mostly the responsibility of the supplier company.

USsize

The process starts with an initial need from a final user, i.e., a retail store, a cuisine. A project manager from a supply company or a consulting firm under the management takes over the project. The cold need is then analyzed to give recommendations for installation. It is usually done by a design office that is an integrated team of the supply company or a consulting firm. Based on the frigorific power, designers will dimension the system (i.e., calculation of the compressor power, the dimension of condenser, needed space for the system) subjected to the operational and financial constraints. The operational constraints are brought by the building structure, for example, the size of the machine room or the available space on the roof. These companies often work with subcontractors that are the manufacturers of components or systems. The R&D team from the manufacturing companies oversees the development of new components or systems that will meet the market needs (provided by the company marketing teams). They are composed of components designers in charge of drawing the products and providing recommendations and correction of the supplier demand.

Once the system is detailed and approved by the final user, a contract is signed to process the system's manufacture and installation. The handling of a refrigeration system, i.e., the installation, the maintenance, the dismantling, is subject to a set of regulations. Refrigeration engineers or technicians must be certified in order to put into service, maintain or dismantle the system (especially for refrigerant handling).

For the EOL treatment of the RS, EOL parties regroup all the actors that can handle commercial refrigeration systems. It can be eco-organisms (for example, Ecosystem), wastes treatment companies, public authorities in charge of waste treatment.

Around the project, a set of parties will write several regulations regrouped in France's Environmental code. Training facilities, unions, and associations such as the International Institut of Refrigeration, the RPF (Revue professionnelle du Froid) regrouped in the category of professional organisms provide the certifications, regulation watch, and guidelines.

Academic researchers are also external parties that anticipate new technologies through theoretical and experimental projects. They can directly relate with R&D teams or through a technological transfer center to make the technology market-ready. From RPF (Revue Professionnelle du Froid), 729 companies for manufacturing activities (including R&D, components drawing…) and 20 professional organism, organisms of research and organisms for refrigeration trainings are listed.

UPsize matrix

From (Evans et al., 2014), we consider that 4-5% of refrigeration production is for commercial use. ("Compressor TRL"+"Condenser TRL"+"Evaporator TRL"+"Expansion valve TRL"+"Pipeline discharge TRL"+"Pipeline suction TRL"+"Refrigerant TRL"+"Material of discharge pipeline TRL"+"Material of suction pipeline TRL")/9

TRL_Average [level] ("Compressor TRL"+"Condenser TRL"+"Evaporator TRL"+"Expansion valve TRL"+"Pipeline discharge TRL"+"Pipeline suction TRL"+"Refrigerant TRL"+"Material of discharge pipeline TRL"+"Material of suction pipeline TRL"+"Circulation pump TRL"+"Heat exchanger TRL"+"Secondary refrigerant pipeline TRL"+"Secondary refrigerant TRL")/13

TRL_Average [level] ("Compressor TRL"+"Condenser TRL"+"Evaporator TRL"+"Expansion valve TRL"+"Pipeline discharge TRL"+"Pipeline suction TRL"+"Refrigerant TRL"+"Material of discharge pipeline TRL"+"Material of suction pipeline TRL"+"Transcritical Comperssor TRL"+"Transcritical pipeline suction TRL"+"Transcritical pipleline discharge TRL")/12

TRL_mean [level] floor(mean("Compressor TRL","Condenser TRL","Evaporator TRL","Expansion valve TRL","Pipeline discharge TRL","Pipeline suction TRL","Refrigerant TRL","Material of discharge pipeline TRL","Material of suction pipeline TRL"))

TRL_mean [level] floor(mean("Compressor TRL","Condenser TRL","Evaporator TRL","Expansion valve TRL","Pipeline discharge TRL","Pipeline suction TRL","Refrigerant TRL","Material of discharge pipeline TRL","Material of suction pipeline TRL","Circulation pump TRL","Heat exchanger TRL","Secondary refrigerant pipeline TRL","Secondary refrigerant TRL"))

TRL_mean [level] floor(mean("Compressor TRL","Condenser TRL","Evaporator TRL","Expansion valve TRL","Pipeline discharge TRL","Pipeline suction TRL","Refrigerant TRL","Material of discharge pipeline TRL","Material of suction pipeline TRL","Transcritical Comperssor TRL","Transcritical pipeline suction TRL","Transcritical pipleline discharge TRL"))

TRL_min [level] min("Compressor TRL","Condenser TRL","Evaporator TRL","Expansion valve TRL","Pipeline discharge TRL","Pipeline suction TRL","Refrigerant TRL","Material of discharge pipeline TRL","Material of suction pipeline TRL")

TRL_min [level] min("Compressor TRL","Condenser TRL","Evaporator TRL","Expansion valve TRL","Pipeline discharge TRL","Pipeline suction TRL","Refrigerant TRL","Material of discharge pipeline TRL","Material of suction pipeline TRL","Circulation pump TRL","Heat exchanger TRL","Secondary refrigerant pipeline TRL","Secondary refrigerant TRL")

TRL_min [level] min("Compressor TRL","Condenser TRL","Evaporator TRL","Expansion valve TRL","Pipeline discharge TRL","Pipeline suction TRL","Refrigerant TRL","Material of discharge pipeline TRL","Material of suction pipeline TRL","Transcritical Comperssor TRL","Transcritical pipeline suction TRL","Transcritical pipleline discharge TRL")

Total storage unit status "Cold storage unit"."storage unit status"+"Horizontal DC"."storage unit status"+"Vertivcal DC"."storage unit status"

Total storage unit status if(('Not selected'="Cold storage unit"."storage unit status") and (('Not selected'="Horizontal DC"."storage unit status" and 'Selected'="Vertivcal DC"."storage unit status") or ('Selected'="Horizontal DC"."storage unit status" and 'Not selected'="Vertivcal DC"."storage unit status"))) then 1 else 0 Required compressor capacity for the refrigeration system [kW] "Frigorific power for all CS and DC [kW]"

Compressor characteristics

Required compressor capacity for the refrigeration system [kW] "Frigorific power for one horizontal DC [kW]"+"Frigorific power for one vertical DC [kW] Hourly Q_dot_ColdStorage [kW] "Hourly Q_dot_Airex [kW]"+"Hourly Q_dot_light [kW]"+"Hourly Q_dot_people [kW]"+"Hourly Q_dot_food for CS [kW]"+"Hourly Q_dot_trans [kW] Frigorific power for one horizontal DC during closed time [kW] frigorific_power("Hourly Q_night_hor [kW]","pinch_h [°C]","eta [#]","T_c [°C]","Supermarket"."Ambient temperature [°C]",0,0,0,0,0,0,0,0,0,0,0)

DC Energy consumption

Frigorific power for one horizontal DC during working time [kW] frigorific_power("Hourly Q_day_hor [kW]

DC Energy consumption

Frigorific power for one horizontal DC during working time [kW] frigorific_power("Hourly Q_day_hor [kW]","pinch_h [°C]","eta [#]","T_c [°C]","Supermarket"."Ambient temperature [°C]",0,0,0,0,0,0,0,0,0,0,0) DC Energy consumption Frigorific power for one vertical DC [kW] max("Frigorific power for one vertical DC during closed time [kW]","Frigorific power for one vertical DC during working time [kW]")

DC Energy consumption

Frigorific power for one vertical DC during closed time [kW] frigorific_power("Hourly Q_night_ver [kW] Frigorific power for one vertical DC during closed time [kW] frigorific_power("Hourly Q_night_ver [kW]","pinch_h [°C]","eta [#]","T_c [°C]","Supermarket"."Ambient temperature [°C]",0,0,0,0,0,0,0,0,0,0,0)

DC Energy consumption

Frigorific power for one vertical DC during working time [kW] frigorific_power("Hourly Q_day_ver [kW]

DC Energy consumption

Frigorific power for one vertical DC during working time [kW] frigorific_power("Hourly Q_day_ver [kW]","pinch_h [°C]","eta [#]","T_c [°C]","Supermarket"."Ambient temperature [°C]",0,0,0,0,0,0,0,0,0,0,0) DC Energy consumption Hourly Q_day_hor [kW] "Hourly Q_wall_hor [kW] ((if('Copper'="pipeline discharge material"."name") then ("GWP copper [kg CO2 eq/kg]") elseif('Plastic'="pipeline discharge material"."name") then ("GWP plastic [kg CO2 eq/kg]") else ("GWP steel [kg CO2 eq/kg]"))*"Mass of all TC discharge pipes [kg]")+((if('Copper'="pipeline suction material"."name") then ("GWP copper [kg CO2 eq/kg]") elseif('Plastic'="pipeline suction material"."name") then ("GWP plastic [kg CO2 eq/kg]") else ("GWP steel [kg CO2 eq/kg]"))*"Mass of all TC suction pipe [kg]")

Transcritical

TC_Frigorific power for all CS [kW] if('Transcritical Cold storage unit'="Transcritical storage"."name") then "TC_Frigorific power for CS [kW]"*"Number of Transcritical CS units [#]" else 0

Transcritical

TC_Frigorific power for all CS and DC [kW] "TC_Frigorific power for all CS [kW]"+"TC_Frigorific power for all DC [kW]" Transcritical TC_Frigorific power for all DC [kW] if('Transcritical Cold storage unit'<>"Transcritical storage"."name") then "TC_Frigorific power for one DC [kW] "TC_Hourly Q_dot_Airex [kW]"+"TC_Hourly Q_dot_light [kW]"+"TC_Hourly Q_dot_people [kW]"+"TC_Hourly Q_dot_food for CS [kW]"+"TC_Hourly Q_dot_trans [kW]" Transcritical TC_Hourly Q_dot_food for CS [kW] " Dans le deuxième chapitre, nous répondons au premier objectif en diagnostiquant le système sociotechnique des systèmes de réfrigération en France grâce à la méthodologie Radical Innovation Design (RID) [2,3]. Dans le troisième chapitre, un état de l'art sur les différents travaux de développement de plateforme de modélisation et de simulation sont explorés afin de définir une approche de recherche répondant aux lacunes mises en avant dans le diagnostic. Le chapitre quatre présente le modèle de connaissance développé pour concevoir un système de réfrigération de supermarché en phase amont de conception. Le chapitre conclut par une phase de validation de ce modèle auprès d'experts.

Le dernier chapitre montre la mise en place du modèle de connaissance dans la plateforme de simulation, l'exploration de scénario d'utilisation de la plateforme et la validation par des experts du domaine.

Enfin, une conclusion générale finalise ce mémoire.

2. Diagnostic du système socio-technique de réfrigération Une revue de littérature approfondie est menée afin de trouver une solution innovante qui permettra de pallier les poches de valeurs identifiées dans ce diagnostic. Une solution développant une plateforme intégrée basée sur des modèles est préconisée pour combler ces poches de valeurs.

Revue de littérature vers une solution innovante

En partant du résultat du diagnostic, et pour pallier les poches de valeurs identifiées, nous cherchons à construire la connaissance autour des systèmes de réfrigération pour la mettre en place dans une plateforme de conception intégrée qui servira à modéliser et simuler puis optimiser une architecture de système réfrigérant et, plus généralement, celle d'un système énergétique thermique. Nous avons mené une revue de la littérature avec une centaines d'articles, nous amenant à classifier les travaux antérieurs sur la modélisation et la simulation/optimisation de systèmes frigorifiques et énergétiques en plusieurs catégories d'études : -Les études ayant pour objectif la modélisation et simulation de composants (ex : compresseur, con-denseur…) individuels avec des modèles mathématiques très détaillés et spécifiques pour l'objectif de l'étude (améliorer une efficacité ou montrer une performance intéressante à un niveau local) [4][START_REF] Yannou | Capturing the relevant problems leading to pain-and usage-driven innovations: The Dependency Structure Modeling Value Bucket algorithm[END_REF][START_REF] Keppler | Absorption chillers as a contribution to a climate-friendly refrigeration supply regime: Factors of influence on their further diffusion[END_REF]. -Les études ayant pour objectif la modélisation et simulation de systèmes entiers avec plusieurs composants et configurations par des séries d'équations mathématiques pour montrer la faisabilité de la solution technique. Selon l'objectif de l'étude, les modèles développés vont être plus ou moins complexes et juste par rapport à la réalité (évaluation de performances uniquement thermodynamiques par exemple). Les études utilisent soit des logiciels existants (comme SuperSim, Dymola), soit des modèles basés sur des équations plus ou moins complexes/détaillées et nécessaires à l'étude mais qui ne rentrent pas dans un processus de conception (c'est-à-dire qu'on évalue une performance à un instant t avec des hypothèses particulières) [START_REF] Sun | Experimental investigation on twin screw refrigeration compressor with different capacity control methods[END_REF][START_REF] Ben-Abdallah | Experimental investigation of the use of PCM in an open display cabinet for energy management purposes[END_REF][START_REF] Gullo | Energy and environmental performance assessment of R744 booster supermarket refrigeration systems operating in warm climates[END_REF][START_REF] Delventura | Secondary loop systems for the supermarket industry[END_REF][START_REF] Kolokotroni | Supermarket Energy Use in the UK[END_REF]. Ce sont les articles les plus fréquemment trouvés dans la littérature.

-

Les études ayant pour objectif la définition d'un langage avec des composants, des connexions, des variables comme des bibliothèques de langages à intégrer dans une étude (ex : Modelica) [12-14] -Les études ayant pour objectif le développement d'une ontologie d'une partie des systèmes de réfrigération, ou inclue dans une ontologie plus générale, comme CheBi [START_REF] Hastings | The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013[END_REF], une ontologie définissant les entités chimiques avec un sous-concept dédié aux réfrigérants.

Dans toutes ces études, il manque certaines notions industrielles pour la simulation et la modélisation de performances réelles en usage comme la maintenance, la sécurité, la facilité d'installation, le confort sonore... Ces manques sont un frein à une évolution optimale des systèmes afin de répondre aux nouvelles règlementations. En effet, les plateformes de simulation et modélisation existantes pour les systèmes de réfrigération ne traduisent pas toute la complexité des systèmes au niveau de la diversité des performances obtenues ou la diversité des architectures possibles. Elles ne permettent donc pas une exploration optimale de l'espace de conception. De plus, le diagnostic souligne une approche de conception de système de réfrigération descendante et basée sur l'évaluation d'un nombre très limité de solutions dimensionnées (on parle de point-based design). En effet, une limitation mise en avant par plusieurs travaux [START_REF] Sobek | Toyota's principles of set-based concurrent engineering[END_REF] est la restriction de l'espace de conception à une solution acceptable mais pas optimale au vue des spécifications sans tester l'ensemble des alternatives possibles, faute du savoir-faire pour les énumérer. La conception basée sur des ensembles (set-based design) [START_REF] Canbaz | Improving Process Performance of Distributed Set-Based Design Systems by Controlling Wellbeing Indicators of Design Actors[END_REF][START_REF] Abi Akle | Visualisation d'information pour une décision informée en exploration d'espace de conception par shopping[END_REF] commence par l'analyse d'un ensemble de solutions possibles puis réduit progressivement l'ensemble des possibilités pour tendre vers le meilleur compromis. Nous retrouvons dans les travaux de A. Abi Akle [START_REF] Abi Akle | Visualisation d'information pour une décision informée en exploration d'espace de conception par shopping[END_REF] une notion similaire appelé le « Design by Shopping ». Dans cette approche, le processus est itératif en permettant aux acteurs de la conception d'interagir en simultané avec l'espace de performance et l'espace de décision.

Dans ses travaux de recherche, V.Holley développe une approche [START_REF] Holley | A method to envision highly constrained architectural zones in the design of multi-physics systems in severe conditions[END_REF] basée sur les concepts listés précédemment et développe l'outil de modélisation et de simulation Geeglee. En générant un espace de conception avec toutes les alternatives possibles, l'approche permet de découvrir des conceptions pertinentes et parfois hors normes répondant aux objectifs des acteurs impliqués. En se basant sur des critères de performances du système, les acteurs peuvent ainsi trouver des solutions intéressantes auxquelles ils n'auraient pas pensé ou qu'ils auraient difficilement identifiées. Geeglee est divisé en deux outils complémentaires : Geeglee Engineering Patterns (GEP) et Geeglee Engineering Intelligence (GEI). GEP est l'outil utilisé pour modéliser le système d'intérêt (system of interest), tandis que GEI est celui utilisé pour visualiser et analyser les données de l'espace de conception qui sont générées à partir du modèle créé sous GEP, puis pour permettre au concepteur de jouer avec l'espace de conception en réduisant l'incertitude jusqu'à une solution optimale, ceci de manière opportuniste (design by shopping) en sur-contraignant le cahier des charges et en effectuant des choix de conception particuliers.

Afin de répondre aux limites identifiées, nous avons cherché à développer une plateforme d'exploration d'espace de conception basée sur ces approches avec les outils Geeglee. Dans la plateforme, le comportement

Mise en place d'une plateforme de simulation et d'exploration

Dans cette dernière partie et afin de répondre au dernier objectif, le modèle de connaissance validé est ensuite exploité à l'aide de l'outil GEI configuré pour permettre de retrouver le modèle de connaissance et d'explorer l'ensemble de l'espace de conception selon les objectifs et les besoins des utilisateurs. Des performances qualitatives et quantitatives sont analysées et évaluées. Les objectifs de cette plateforme sont non seulement de tester plusieurs alternatives de composants ou d'architecture possible en phase amont de conception et d'explorer en temps réel l'espace de conception mais aussi d'apporter un outil d'aide à la décision pour les industriels et pour aider à explorer des champs de recherche. Il est intéressant de noter que les études de cas proposées sont représentatives de la complexité industrielle liée à la conception et exploitation de système de réfrigération dans un supermarché [START_REF] Canbaz | Improving Process Performance of Distributed Set-Based Design Systems by Controlling Wellbeing Indicators of Design Actors[END_REF][START_REF] Abi Akle | Visualisation d'information pour une décision informée en exploration d'espace de conception par shopping[END_REF]. Ces scénarios d'usage de la plateforme sont présentés en détail dans le rapport et sont validés par expérimentation avec des experts (test de la plateforme). La qualité de la plateforme est évaluée selon les mêmes critères de qualité cités dans le paragraphe précédent : la pertinence du vocabulaire employé, sa clarté, le choix des relations, la cohérence et l'extensibilité, la fidélité et l'usage. En s'appuyant sur les limites des plateformes digitales actuelles pour évaluer les performances de systèmes de réfrigération et les concevoir, un nouvel outil est développé. Cette plateforme offre pour la première fois la possibilité aux acteurs du domaine d'explorer un large espace de conception et de décision (plus de 10 000 alternatives considérées). Quatre apports majeurs obtenus avec cette nouvelle plateforme peuvent être considérés : (1)intégration de plusieurs performances dont des performances « industrielles » (maintenance, coûts totaux, maturité technologique) ;

(2) modularité des composants techniques, c'est-à-dire la possibilité pour tous les acteurs du processus de pouvoir ajouter un ou plusieurs composants qui leur sont propres ; (3) modularité pour l'intégration de nouvelles briques technologiques et leur évaluation dans un cas industriel ; (4) accessibilité de la plateforme à tous les acteurs impliqués dans le processus afin de permettre une conception itérative entre utilisation finale et conception. Plusieurs expérimentations de la plateforme sont mises en place pour valider les critères listés. Six experts venant du domaine académique ou industriel de la réfrigération sont désignés pour tester et évaluer la pertinence de la plateforme et les résultats obtenus. Des scénarios provenant de cas d'études réel sont testés. Il est montré comment l'espace de conception est différent selon les spécifications de l'utilisateur. Par exemple, un concepteur en charge de concevoir un système de réfrigération avec des composants matures et le plus efficace énergétiquement n'aura pas les mêmes résultats qu'un concepteur voulant concevoir un système avec le moindre coût d'investissement. Une réévaluation par la méthode RID permet de valider que cette plateforme répond aux poches de valeurs identifiées pendant le diagnostic. Des recommandations d'utilisation de la plateforme et du modèle de connaissances sont proposées afin de permettre aux acteurs académiques et industriels de tendre vers le développement de nouvelles briques technologiques et de systèmes optimaux.

Conclusion

L'objectif principal de cette thèse est d'améliorer la conception globale d'un système de réfrigération (RS) pour l'industrie alimentaire (tel qu'on le trouve généralement dans un supermarché). Les domaines d'amélioration des étapes du cycle de vie du système sociotechnique de la réfrigération ont été analysés pour répondre à la question suivante : "Les causes des mauvaises performances du système sont-elles dues à un manque d'outils, à un manque de savoir-faire en matière de conception, ou à tout un ensemble de problèmes organisationnels causés par un manque de concertation entre les acteurs de la chaîne de valeur ?". Une étude de terrain a permis de recueillir les données nécessaires à une analyse par le RID. Lors des entretiens, les questions ont révélé l'importance de la première étape du cycle de vie d'un système de réfrigération : la conception, la fabrication et l'installation. Cette analyse nous montre comment partir de ces valeurs identifiées pour développer une solution innovante. Une revue de littérature extensive et la collection de données auprès d'experts a permis de mettre en place un modèle de connaissance structurée pour la conception de système de réfrigération. Ensuite nous avons mis en place ce modèle de connaissance dans une plateforme de simulation et d'exploration basée sur des modèles pour modéliser et simuler le système réfrigérant en cours de conception. L'utilisateur peut trouver la meilleure combinaison de solutions pour un scénario de système de réfrigération en supermarché ou explorer en temps réel les effets d'un changement de composants physiques sur les performances du système. Cette plateforme de simulation et d'exploration répond à une lacune liée au processus de conception perturbé qui entraîne une exploration sous-optimale de l'espace de conception. Cela est principalement dû à un processus de conception descendant et l'évaluation d'un nombre très limité de solutions dimensionnées pour répondre aux contraintes de l'utilisateur final (budget, état d'esprit, image de l'entreprise, délai et conditions d'exploitation et d'installation) et à son objectif (par exemple, minimiser la consommation d'énergie et les coûts, maximiser les performances). Au début de la phase de conception, nous avons cherché à mieux intégrer les contraintes et les objectifs des parties prenantes dans une plateforme de conception partagée afin d'assurer une conception collaborative et de surmonter le manque d'interaction entre les parties prenantes. Des modèles simplifiés de la performance du système de réfrigération non pris en compte par les plateformes existantes sont utilisés pour l'évaluer plus tôt dans le processus de conception, assurant ainsi une meilleure appréhension

  of contents ............................................................................................................... List of figures ......................................................................................................................

Figure 2 - 1 .Figure 2 - 2 .

 2122 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 2 - 4 .

 24 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 2 - 5 .

 25 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 2 - 6 .Figure 2 - 7 .Figure 2 - 8 .Figure 2 - 9 .

 26272829 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 2 - 11 .

 211 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 2 - 12 .

 212 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 2 -

 2 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 2 - 15 .

 215 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 3 - 1 .

 31 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 3 - 2 .

 32 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 3 - 3 .

 33 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 4 - 1 .

 41 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 4 - 5 .

 45 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 4 - 6 .

 46 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 4 - 7 .

 47 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 4 - 8 .

 48 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 4 - 9 .

 49 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 4 - 10 .

 410 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 4 - 11 .

 411 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram .......Figure 2-2. Simplified scheme of a centralized direct expansion system architecture Figure 2-3. Simplified scheme of a plug-in cabinet ..........................................................Figure 2-4. Simplified scheme of secondary loop refrigeration system .........................Figure 2-5. Simplified scheme of a transcritical architecture ...........................................Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and Zhong and Zhou (2019) ........................................................................................................Figure 2-7. Scheme of a system structure from Pahl and Beitz (2013) (left) and a simplified example for a refrigeration system structure (right) .......................................Figure 2-8. Steps of Pahl and Beitz design process (Pahl and Beitz 2013) ....................Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company ...............Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat, Bertoluci, and Froelich 2007) ............................................................................Figure 2-11. Steps of an LCA study from (ISO 14044 2006) ............................................Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines. ...............................................................................................................Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right) ..Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009) ................Figure 2-15. Issues identified by (Evans et al. 2014) .........................................................Figure 3-1. Overview of the research gaps, questions and objectives. ..........................Figure 3-2. DRM framework from(Blessing and Chakrabarti 2009) ................................Figure 3-3. Positioning of our research in the industry and academic research ...........Figure 4-1. The three main stages of the Radical Innovation Design process ..............Figure 4-2. The Radical Innovation Design process in detail ...........................................Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process ....Figure 4-4. Retail refrigeration system lifecycle process ..................................................Figure 4-5. Influence/Dependency graph ...........................................................................Figure 4-6. The 12 categories of user-profiles and their interactions ............................Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase. ...................................................................................Figure 4-8. Causality graph for the problems occurring during the exploitation phase. ..................................................................................................................................................Figure 4-9. Causality graph for problems occurring during the EOL treatment phase. ..................................................................................................................................................Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of quantity of pains, irrespective of usage situations and user profiles) .Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems) ....................................................................................................

Figure 4 - 12 .Figure 5 - 1 .Figure 5 - 2 .Figure 5 - 3 .

 412515253 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 5 - 4 .

 54 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 5 - 5 .Figure 5 - 8 .Figure 5 - 9 .

 555859 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 5 -

 5 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 1 .

 61 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 2 .

 62 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 3 .

 63 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 4 .

 64 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 9 .

 69 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 10 .

 610 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 11 .

 611 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 12 .

 612 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 13 .

 613 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 15 .

 615 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 16 .

 616 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 18 .

 618 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 -

 6 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified. .....Figure 5-1. Construction of the ontology and implementation into a design platform ................................................................................................................................................Figure 5-2. ChEBI ontology for refrigerant ....................................................................... Figure 5-3. PhysSys ontology organisation of the model library from (Borst, Akkermans, and Top 1997) .................................................................................................Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition. ..............................................................................Figure 5-5. The three fields of BIM activity from (Succar 2009) ....................................Figure 5-6. FBS framework from (J. S. Gero and Kannengiesser 2014) ........................Figure 5-7. UML diagram of the scenario for supermarket RS ......................................Figure 5-8. Example of a scenario composed of a stakeholder, an objective and a system environment. ...........................................................................................................Figure 5-9. Decomposition of an RS design solution .....................................................Figure 5-10. UML diagram of some classes that can be comprise in the properties .Figure 5-11. UML diagram of the global performance classes, their intra-and extrarelations with other classes................................................................................................. Figure 5-12. UML diagram of the knowledge model and the relations between the concepts ................................................................................................................................Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms ............................................................................................................................Figure 6-2. Our strategy for design space exploration based on a brute-force enumeration and "design by shopping" inspired by (Neema et al., 2003). .................Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects. ....................................................................Figure 6-4. Presentation of the implementation of the scenario concept in the GEP Figure 6-5. Presentation of the design space implementation in the GEP ..................Figure 6-6. Compressor characteristics implemented in the exploration platform ....Figure 6-7. Incompatibility table implemented in the design platform .......................Figure 6-8. Environmental impacts class implementation in the design and exploration platform ............................................................................................................Figure 6-9. Simplified scheme of a design point computation algorithm ...................Figure 6-10. Steps to follow using the design and exploration platform ....................Figure 6-11. Reference supermarket for verification of the models .............................Figure 6-12. Platform interface ..........................................................................................Figure 6-13. System environment: store characteristics interface.................................Figure 6-14. System environment: food conservation characteristics interface .........Figure 6-15. Selection of the components of the existing system ................................Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant. ........................................................................................................................ Figure 6-17. Pareto space displayed in the platform ......................................................Figure 6-18. Cost table and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right) ...........................................................................Figure 6-19. Environmental 1D interval visualization and selection of constraints ....

Figure 6 - 20 .Figure 7 - 1 .Figure 7 - 2 .

 6207172 Figure 6-20. Visualization of all the feasible solutions ...................................................Figure 6-21. Selection of non-dominated solutions ....................................................... Figure 6-22. Workshops with INRAE experts for step-by-step verification .................Figure 7-1. Positioning of the chapter in the thesis ........................................................Figure 7-2. Hierarchy of criteria for design artefact evaluation from (Prat et al., 2014). ................................................................................................................................................Figure 7-3. Timeline of the validation session .................................................................Figure 7-4. Simplified script given to the testers .............................................................Figure 7-5. Environmental impacts reduction using the sliders ....................................Figure 7-6. Example of 2D representation to compare design solutions ....................Figure 7. Méthodologie mise en place pour construire le livre de connaissance .......Figure 8. Résumé de la méthodologie RID et des résultats obtenus pour évaluer le système socio-technique existant...................................................................................... Figure 9. Présentation des macro-concepts du modèle de connaissance ...................Figure 10. Présentation de la mise en place du concept de scénario dans le GEP .....Figure 11. Présentation de la mise en place de l'espace de décision dans le GEP .....Figure 12. Exemple d'une analyse multi-objectifs entre le coût total et les impacts environnementaux sur la plateforme de simulation et d'exploration ..........................Figure 13. Exemple de l'évaluation des impacts environnementaux de toutes les alternatives possibles ..........................................................................................................

Figure 7 - 3 .

 73 Figure 6-20. Visualization of all the feasible solutions ...................................................Figure 6-21. Selection of non-dominated solutions ....................................................... Figure 6-22. Workshops with INRAE experts for step-by-step verification .................Figure 7-1. Positioning of the chapter in the thesis ........................................................Figure 7-2. Hierarchy of criteria for design artefact evaluation from (Prat et al., 2014). ................................................................................................................................................Figure 7-3. Timeline of the validation session .................................................................Figure 7-4. Simplified script given to the testers .............................................................Figure 7-5. Environmental impacts reduction using the sliders ....................................Figure 7-6. Example of 2D representation to compare design solutions ....................Figure 7. Méthodologie mise en place pour construire le livre de connaissance .......Figure 8. Résumé de la méthodologie RID et des résultats obtenus pour évaluer le système socio-technique existant...................................................................................... Figure 9. Présentation des macro-concepts du modèle de connaissance ...................Figure 10. Présentation de la mise en place du concept de scénario dans le GEP .....Figure 11. Présentation de la mise en place de l'espace de décision dans le GEP .....Figure 12. Exemple d'une analyse multi-objectifs entre le coût total et les impacts environnementaux sur la plateforme de simulation et d'exploration ..........................Figure 13. Exemple de l'évaluation des impacts environnementaux de toutes les alternatives possibles ..........................................................................................................

Figure 7 - 4 .

 74 Figure 6-20. Visualization of all the feasible solutions ...................................................Figure 6-21. Selection of non-dominated solutions ....................................................... Figure 6-22. Workshops with INRAE experts for step-by-step verification .................Figure 7-1. Positioning of the chapter in the thesis ........................................................Figure 7-2. Hierarchy of criteria for design artefact evaluation from (Prat et al., 2014). ................................................................................................................................................Figure 7-3. Timeline of the validation session .................................................................Figure 7-4. Simplified script given to the testers .............................................................Figure 7-5. Environmental impacts reduction using the sliders ....................................Figure 7-6. Example of 2D representation to compare design solutions ....................Figure 7. Méthodologie mise en place pour construire le livre de connaissance .......Figure 8. Résumé de la méthodologie RID et des résultats obtenus pour évaluer le système socio-technique existant...................................................................................... Figure 9. Présentation des macro-concepts du modèle de connaissance ...................Figure 10. Présentation de la mise en place du concept de scénario dans le GEP .....Figure 11. Présentation de la mise en place de l'espace de décision dans le GEP .....Figure 12. Exemple d'une analyse multi-objectifs entre le coût total et les impacts environnementaux sur la plateforme de simulation et d'exploration ..........................Figure 13. Exemple de l'évaluation des impacts environnementaux de toutes les alternatives possibles ..........................................................................................................

Figure 7 - 5 .

 75 Figure 6-20. Visualization of all the feasible solutions ...................................................Figure 6-21. Selection of non-dominated solutions ....................................................... Figure 6-22. Workshops with INRAE experts for step-by-step verification .................Figure 7-1. Positioning of the chapter in the thesis ........................................................Figure 7-2. Hierarchy of criteria for design artefact evaluation from (Prat et al., 2014). ................................................................................................................................................Figure 7-3. Timeline of the validation session .................................................................Figure 7-4. Simplified script given to the testers .............................................................Figure 7-5. Environmental impacts reduction using the sliders ....................................Figure 7-6. Example of 2D representation to compare design solutions ....................Figure 7. Méthodologie mise en place pour construire le livre de connaissance .......Figure 8. Résumé de la méthodologie RID et des résultats obtenus pour évaluer le système socio-technique existant...................................................................................... Figure 9. Présentation des macro-concepts du modèle de connaissance ...................Figure 10. Présentation de la mise en place du concept de scénario dans le GEP .....Figure 11. Présentation de la mise en place de l'espace de décision dans le GEP .....Figure 12. Exemple d'une analyse multi-objectifs entre le coût total et les impacts environnementaux sur la plateforme de simulation et d'exploration ..........................Figure 13. Exemple de l'évaluation des impacts environnementaux de toutes les alternatives possibles ..........................................................................................................

Figure 7 - 6 .Figure 7 .Figure 8 .Figure 9 .

 76789 Figure 6-20. Visualization of all the feasible solutions ...................................................Figure 6-21. Selection of non-dominated solutions ....................................................... Figure 6-22. Workshops with INRAE experts for step-by-step verification .................Figure 7-1. Positioning of the chapter in the thesis ........................................................Figure 7-2. Hierarchy of criteria for design artefact evaluation from (Prat et al., 2014). ................................................................................................................................................Figure 7-3. Timeline of the validation session .................................................................Figure 7-4. Simplified script given to the testers .............................................................Figure 7-5. Environmental impacts reduction using the sliders ....................................Figure 7-6. Example of 2D representation to compare design solutions ....................Figure 7. Méthodologie mise en place pour construire le livre de connaissance .......Figure 8. Résumé de la méthodologie RID et des résultats obtenus pour évaluer le système socio-technique existant...................................................................................... Figure 9. Présentation des macro-concepts du modèle de connaissance ...................Figure 10. Présentation de la mise en place du concept de scénario dans le GEP .....Figure 11. Présentation de la mise en place de l'espace de décision dans le GEP .....Figure 12. Exemple d'une analyse multi-objectifs entre le coût total et les impacts environnementaux sur la plateforme de simulation et d'exploration ..........................Figure 13. Exemple de l'évaluation des impacts environnementaux de toutes les alternatives possibles ..........................................................................................................

  used to transfer heat from cold to heat sources. A refrigerant moves inside the components and absorbs or resorbs heat by its phase changes when the fluid is condensed or evaporated. In the evaporator, the refrigerant is submitted to low pressure. The refrigerant evaporates to cool the environment by absorbing heat, and the temperature along the evaporator is almost constant. The vaporous refrigerant then enters the compressor. High pressure and high-temperature gas at the compressor exit enter the condenser, where the cumulated heat is released. The thermodynamic cycle finishes by the return of the fluid to low pressure by passing through the expansion valve (see Figure2-1).

Figure 2 - 1 .

 21 Figure 2-1. Basic refrigeration system and its pressure -enthalpy (p-h) diagram

Figure 2 - 2 .

 22 Figure 2-2. Simplified scheme of a centralized direct expansion system architecture

Figure 2 - 3 .

 23 Figure 2-3. Simplified scheme of a plug-in cabinet

Figure 2 - 4 .

 24 Figure 2-4. Simplified scheme of secondary loop refrigeration system

Figure 2 - 5 .

 25 Figure 2-5. Simplified scheme of a transcritical architecture

  organisation management, human factor engineering and control (Kosky et al. 2021), as presented in Figure 2-6.

Figure 2 - 6 .

 26 Figure 2-6. Industrial engineering field overview inspired by Kosky et al. (2021) and[START_REF] Zhong | Inquiry and Experiential Mixed Teaching Method Is Effective Way to Cultivate High-Quality Innovative Talents[END_REF]

Figure 2 - 8 .

 28 Figure 2-8. Steps of Pahl and Beitz design process(Pahl and Beitz 2013)

Figure 2 - 9 .

 29 Figure 2-9. Types of Design for eXcellence (DfX) from Geometric Company

Figure 2 -

 2 Figure 2-10. Eco-design tools and their use in the product development process from (Le Pochat,

- 11 :

 11  Context, goal, boundaries, method and functional unit definition  Life Cycle Inventory (LCI)  Impacts assessment (mid-points or end-points indicators)  Interpretation and results analysing

Figure

   Steps of an LCA study from(ISO 14044 2006)

Figure 2 - 12 .

 212 Figure 2-12. Example of a Design structure Matrix (DSM) to model the interactions between disciplines.

Figure 2 -

 2 Figure 2-13. Disciple interactions: sequential (left), parallel (centre), coupled (right)

Figure 2 -

 2 Figure 2-14. Two-dimensional-(2D) Pareto front (red line and dark green circles). The light green dots are feasible points, whereas the yellow ones indicate infeasible solutions with respect to the constraints.(Gollub and de Vivie-Riedle 2009)

Figure 2 -

 2 Figure 2-15 presents all the issues encountered in the audit. Evans and al. identified energy savings of between 8 and 82% if appropriate changes were made.

Figure 2 - 15 .

 215 Figure 2-15. Issues identified by(Evans et al. 2014)

  [START_REF] Shahrestani | Decision-Making on HVAC&R Systems Selection: A Critical Review[END_REF] provide a multi-disciplinary decision-making tool based on three main criteria: technical (energy consumption, thermal comfort and indoor air quality), economic (initial and operational cost), and environmental (CO2 emissions).

Figure 3 -

 3 Figure 3-1 offers an overview of the thesis outline in relation to the research gaps, questions and associated objectives.

Figure 3 - 1 .

 31 Figure 3-1. Overview of the research gaps, questions and objectives.

  have adapted our research framework on the Design Research Methodology (DRM) (Blessing and Chakrabarti 2009). It consists of four main steps: (1) research clarification; (2) descriptive study; (3) prescriptive study; (4) second descriptive study as shown in Figure 3-2.

Figure 3 - 2 .

 32 Figure 3-2. DRM framework from[START_REF] Blessing | DRM: A Design Reseach Methodology[END_REF]

  1. The followed steps are iterative to adapt this process from the results obtained at each step. The first literature review allows us to define the research gaps. The first descriptive study consists of assessing the state of the existing (objective 1.1 and 1.2). The prescriptive studies are the main scientific contribution of this thesis (objective 2.1 and 2.2). Finally, the second descriptive study assesses our work with the field actors (research and industry).

Table 3 - 1 .

 31 Proposed research methodology for the present work adapted from the DRM explore the research fields in order to identify the research gaps, acquire knowledge on the domains and select the adapted design artefacts, i.e. methods, tools, models etc. based on surveys and observation of the field to complete the literature review and confront the research gaps found in the literature to the industrial ground. Chapter 4 Prescriptive study Proposition and development of a knowledge model to provide a clear way of posing a problem and understanding the userExperimentation to assess the quality and collect feedbacks on the proposed design artefacts, i.e. model and tool Chapter 7 Discussion to interpret the results and provide eventually recommendations of implementation in the domain Chapter 8

  involved in the activities linked to the RS throughout its lifecycle.[START_REF] Minetto | Non-technological barriers to the diffusion of energy-efficient HVAC&R solutions in the food retail sector[END_REF] have conducted interviews with food retail sector stakeholders in Europe. Despite a positive attitude toward energyefficient solutions, the authors identify non-technical barriers to adopting low-carbon solutions such as CO2-based systems and heat recovery: poor awareness of technological opportunities and expertise in operational optimization, and lack of social, organizational and legislative knowledge. A recently published article[START_REF] Paurine | Understanding the market need for skills in alternative refrigerants with low global warming potential in the EU region -A comprehensive survey on Refrigerant Emissions And Leakage (REAL) alternatives programme[END_REF]) also highlights a lack of knowledge, skills and awareness in the industrial use of low-GWP alternative refrigerants. For this purpose, a series of surveys on available training was conducted with several actors in the field. The results showed the usefulness of these fluids and described the existing practices and the skill needs related to them. To our knowledge, no adequate description or analysis of the socio-technical aspects of an RS in France have yet been undertaken.

  decision-aid tools: RID Comparator to first analyze the effectiveness of the existing solutions, and then RID Compass to decide on the improvement areas. In this light, the improvement areas are finally discussed to envisage design specifications of a more integrated model-based design platform for an RS. Paragraph 2 of this chapter describes the RID methodology and our research process. The first contribution is presented in paragraph 3. The four dimensions are detailed and justified. The results from the two RID decision-aid tools are shown in paragraph 4. The effectiveness of the solutions from RID Comparator is first analyzed, and RID Compass then finds improvement paths. Finally, a general conclusion and perspectives are given, with a description of the proposed solution.

Figure 4 - 1 .

 41 Figure 4-1. The three main stages of the Radical Innovation Design process

Figure 4 - 2 .

 42 Figure 4-2. The Radical Innovation Design process in detail

Figure 4 - 3 .

 43 Figure 4-3. Cognitive perspective and algorithmic workflow of the "Observe and Learn" and "Explore and Decide" stages of the Radical Innovation Design process

  Background/demographics -General information about the actor and company, including company name, activity, size and geographical location, background, business organization, suppliers and clients, existing collaborations Positioning of the actor interviewed on the value chain Clarification of the actors, their position and role in Identify and clarify the existing technologies: maps of existing solutions  most common ones, why, by who Identify the ways for developing a disrupting technology Design of a refrigeration system, what are the intermediate design objects Maps of tools and numerical supports Clarify the needs of design teams for a global approachMaintenance of refrigeration systemsClarification of the types of maintenance and the actors involved;From KPIs identified, confront with experts on which are relevant and usedSustainability issues positioningIdentify the social, economic, environmental concerns and ecological transition positioning of the actorsWe obtained the two contributions to this "Observe and Learn" (Stage 1 in Figure4-1) study. First was a knowledge design part (gathering the knowledge) on socio-technical RS in France. The knowledge was then computed during the problem design (how to build the cognitive model).

  : design, manufacture, installation, exploitation, and end-of-life (EOL) treatment, as depicted in Figure 4-4. In our study, we consider three main stages for the usage situations:  Design, manufacture and installation: This includes the stages of the engineering design process (Pahl and Beitz, 2013), manufacturing of the RS (including raw material extraction), transport of the components to the installation site, and installation. We aggregated this first usage situation because of the difficulty in clearly distinguishing the boundaries of each stakeholder's activities in the initial stages.  Exploitation: This is the longest stage. It includes the regular use of the RS and the attendant maintenance, repair and upgrades.  End-of-life (EOL): This is the shortest stage of the process. It consists of the dismantling, transportation, and EOL management for recycling, reuse, or disposal of the RS.

Figure 4 - 4 .

 44 Figure 4-4. Retail refrigeration system lifecycle process

  French directives on frigorific equipment and write French regulation, standards and laws on frigorific system design Professional organism Organization that helps understand and implement regulation to industrial firms (ex: International Institute of Refrigeration) Socio-technical diagnosis of refrigeration systems in France 90R&D researcherPerson or industrial team who creates prototypes of systems to validate technical solutions. These experimental systems are intended to test and validate new components and integrate the knowledge learnt from feedback. They optimise and implement the solutions to evolve with the final user expectations (ex: Axima R&D team)

  StructureMatrix (DSM) to evaluate the interactions between all the stakeholders. The results are represented in an influence/dependency graph presented in Figure4-5. Dependency structure Matrix (DSM) is a square matrix used to represent the project dependencies. The interactions are graded between 0 and 4:

Figure 4

 4 Figure 4-5. Influence/Dependency graph

  regulations, standards, and laws, grouped in the Environmental Code in France. The certifications, regulation watch and guidelines are provided by training facilities, unions and associations such as the International Institute of Refrigeration grouped in professional organisms. Academic researchers are also external parties that anticipate new technologies through theoretical and experimental projects. They can directly relate to R&D teams or through technology transfer centres.

Figure 4 - 6 .

 46 Figure 4-6. The 12 categories of user-profiles and their interactions

Figure 4 -

 4 Figure 4-8 shows the causal graph for the exploitation phase. Finally, Figure 4-9 shows the causal graph for the problems occurring during the EOL treatment phase.

Figure 4 - 7 .

 47 Figure 4-7. Causality graph for the problems occurring during the design, manufacture and installation phase.

Figure 4 - 8 .Figure 4 - 9 .

 4849 Figure 4-8. Causality graph for the problems occurring during the exploitation phase.

Figure 4 -

 4 Figure 4-10 shows one of the effectiveness indicators: the relative effectiveness of existing solutions in responding to the problems of a socio-technical RS. It indicates the percentage of the possible quantity of pains that each existing solution removes overall in a problem. The most effective solutions are those with the broadest coverage on the radar plot.

Figure 4 -

 4 Figure 4-10 shows that the most effective solutions are knowledge-based (training, best practicesguides, skills analysis table) represented in red. Knowledge management (KM) solutions include understanding new technical solutions such as the use of CO2 as a refrigerant and allow users to engage an effective transition toward regulations[START_REF] Paurine | Understanding the market need for skills in alternative refrigerants with low global warming potential in the EU region -A comprehensive survey on Refrigerant Emissions And Leakage (REAL) alternatives programme[END_REF]. The diagram also highlights that the most damaging problems are mainly solved because they represent very high risks for the actors. For example, the risk of high danger for humans is 100% solved by awareness guides and risk analysis, as is the risk of high operational costs. However, interviewees emphasized that the training courses arrived only after a stakeholder was already in a critical situation (such as working with a CO2-based system).

Figure 4 -Figure 4 -

 44 Figure 4-10. Effectiveness of the existing solutions for the problems (as percentage eradication of

Figure 4 -

 4 Figure 4-11. Effectiveness of the existing solutions for the user profiles or stakeholders (as percentage eradication of quantity of pains, irrespective of the usage situations and problems)

Figure 4 -

 4 Figure 4-12. Results of the DSM value bucket tool with the three VBs identified.

  that explain failures in design. In the last decades, research in refrigeration has developed promising technologies. The stakeholders meet difficulties investing in these new technologies because of a lack of long-term visibility. RSs are robust and costly to develop. Technical change toward more sustainable systems should be more usagedriven in the future, with more systematic consideration of the users and the usage situations of the whole socio-technical RS.Following the present study, we intend to address the gaps identified with the main value buckets by two novel solutions to improve the performance of the activities of the socio-technical RS. First, by developing first a new model-based integrated design platform and approach, and second a new organization linking all the stakeholders involved in the early stages of an RS design.

  of the ontology Define the level of formality Find the concepts and the relations Build the ontology: choose one of four approaches (1. Skip the previous steps and use an ontology editor to define terms and axioms; 2. Do the previous steps and then begin a formal encoding; 3. Produce an intermediate document that can be the final result, or a specification/documentation of the formal code; 4. Identify formal terms from the set of informal terms) (purpose of the ontology, the level of formality and the scope of the ontology) Acquire knowledge: brainstorming, structured and unstructured interviews, formal and informal analysis of texts, the implemented ontology Evaluate technically Document what was done, how and why it was doneIn a recent study,[START_REF] Yang | Ontology-Based Systems Engineering: A State-ofthe-Art Review[END_REF] propose a review of ontology-based system engineering. They offer a roadmap to create a helpful ontology for system engineering (SE). It consists of four steps that are: (1) choose a particular knowledge area where a gap exists and has not been solved;(2) define a clear objective of use of the ontology; (3) use ontology development methodologies and tools to support the development; (4) address SE challenges into the proposed ontology.For the development of the proposed ontology, we use a merging of different methods. The typical first steps of the METHONTOLOGY, Ontology Development 101 and the methodology from[START_REF] Pinto | Ontologies: How Can They Be Built?[END_REF] described in Table5-1 are used:

Figure 5 - 1 .

 51 Figure 5-1. Construction of the ontology and implementation into a design platform

Figure 5 - 2 .

 52 Figure 5-2. ChEBI ontology for refrigerant

Figure 5 - 3 .

 53 Figure 5-3. PhysSys ontology organisation of the model library from[START_REF] Borst | Engineering Ontologies[END_REF]

2. 3 . 2 .

 32 Domain, task and domain-task ontologies After exploring general ontologies, we are interested in existing work in the specific domain. As refrigeration systems are energy-consuming devices, our investigation field is extended to energyconsuming devices in buildings. However, the topic of energy consumption devices and refrigeration systems is not much explored in the literature. Ontologies on the energy domain provide common representation and thus includes refrigeration devices as a sub-system (Cuenca, Larrinaga, and Curry 2017). 'Refrigeration Devices' module of the Domain Analysis-Based Global Energy ontology (DABGEO) (Cuenca, Larrinaga, and Curry 2020) is a formal taxonomy of refrigeration devices. It defines the different concepts of existing devices for refrigeration, their hierarchy relations, as shown in Figure 5-4.

Figure 5 - 4 .

 54 Figure 5-4. Existing classes in the "refrigeration device" module of DABGEO and an example of Chiller concept definition.

Figure 5 -

 5 6 displays this FBS framework. The designer is supposed to compare the two behaviours to adapt and validate the structure. The structure is modelled by a set of variables selected by experts. They give important characteristics of the systems such as dimension, materials, decision variables, geometry. Derived from the FBS ontology, the Observation Interpretation Aggregation is a framework developed as part of two thesis projects (Collignan 2011; Quirante 2012). It integrates three models around the FBS ontology: the observation (O) model corresponding to the system behaviour; the interpretation (I) and aggregation (A) models, which formulate designers' preferences and the optimization, which allows the exploration of the design space and study different design solutions.

Figure 5 - 6 .

 56 Figure5-6. FBS framework from (J. S.[START_REF] Gero | The Function-Behaviour-Structure Ontology of Design[END_REF]

3. 3 . 1 . 1 .

 311 Context related Context-related concepts are based on the definition of a scenario in Design Science. We have based our concept on the description proposed by McKay (2013): a scenario is composed of a particular user who wants to reach a specific objective or execute a specific activity in a specific context. Context related concept participates in the simulation of the system physical behaviour as described in[START_REF] Cheong | Physics-Based Simulation Ontology: An Ontology to Support Modelling and Reuse of Data for Physics-Based Simulation[END_REF] and the components choices.

Figure 5 -

 5 Figure 5-7 shows the UML diagram of the classes composing the scenario. An example of the possible attributes for the user identity for our case study is shown in the enumeration box.

Figure 5 - 7 .Figure 5 - 8 .

 5758 Figure 5-7. UML diagram of the scenario for supermarket RS

Figure 5 - 9 .

 59 Figure 5-9. Decomposition of an RS design solution A system or product architecture is defined in design science as: -« the scheme by which the functional elements of the product are arranged into physical chunks and by which the chunks interact. » (Ulrich and Eppinger 2016) -« a scheme showing the relationship between the function structure of a product and its physical consideration » (Pahl and Beitz 2013)-« how a single product satisfies its function by being divided or not into modules »(Otto and Wood 2001)

Figure 5 -

 5 Figure 5-10. UML diagram of some classes that can be comprise in the properties

  Global performances class regroups the global performances involved in the design objectives. They correspond to a macro level of performance that the stakeholder can interpret to make decisions further. In the refrigeration domain, the global performances usually assessed are the thermal behaviour and energy consumption(Ben-Abdallah et al. 2019; Ge and Tassou 2011), the environmental impacts[START_REF] Makhnatch | The Role of Environmental Metrics (GWP, TEWI, LCCP) in the Selection of Low GWP Refrigerant[END_REF]) and total costs. In this knowledge model for the design and simulation of refrigeration systems, the global performances are instantiated by five main elements: the energy consumption, the environmental impacts, the total cost, the maintenance, and the technological readiness level of the RS solution. They can be expressed from the aggregation of local performances.-Local performances class is composed of variables that are observed or calculated with mathematical models or empirical models (field observations and experts' knowledge). They simulate the system behaviour, called the observation model by[START_REF] Quirante | A Trade-off Function to Tackle Robust Design Problems in Engineering[END_REF], in the OIA ontology or Behaviour in the FBS ontology.

Figure 5 -

 5 Figure 5-11 shows the UML diagram of the performance classes. The global performances can depend on each other for the calculation. For example, the environmental impact assessment depends on the calculation of the energy consumption for the calculation of indirect emissions.

Figure 5 -

 5 Figure 5-11. UML diagram of the global performance classes, their intra-and extra-relations with other classes

Figure 5 -

 5 [START_REF] Mitsopoulos | Annual performance of a supermarket refrigeration system using different configurations with CO2 refrigerant[END_REF] shows the UML diagram of the concepts and classes related to the design and simulation of refrigeration systems for a typical supermarket. Some of the inter-relations are displayed to understand how the

Figure 5 -

 5 Figure 5-12. UML diagram of the knowledge model and the relations between the concepts

5 .

 5 Refrigeration system solution classes, extended sub-classes and instances for the application to retail refrigeration systems

Figure 6 - 1 .

 61 Figure 6-1. Influence Explorer visualisation tool: performances and parameters histograms

( 4 )

 4 ) Technical Portfolio where the intermediate values related to each technical option are entered. Engineering Patterns (EP) where the performance and physical behaviour of the system are explained by means of equations.

Figure 6 - 3 .

 63 Figure 6-3. Correspondence between the design problem represented in our knowledge model and the Geeglee objects.

Figure 6 -

 6 Figure 6-4 partially shows the definition of a scenario of the use of the refrigeration system in a supermarket (number of cold rooms, sales cabinets, the mass of food in each element, set-point temperature, location of the supermarket...) as listed in Chapter 5. The set of possible values for the variable are given in blue. This is its discrete domain of definition.

Figure 6 - 4 .

 64 Figure 6-4. Presentation of the implementation of the scenario concept in the GEP

Figure 6 - 5 .Figure 6 -

 656 Figure 6-5. Presentation of the design space implementation in the GEP

Figure 6 - 6 .

 66 Figure 6-6. Compressor characteristics implemented in the exploration platform

Figure 6 - 7 .

 67 Figure 6-7. Incompatibility table implemented in the design platform

Figure 6 - 8 .

 68 Figure 6-8. Environmental impacts class implementation in the design and exploration platform

Figure 6 - 10 .

 610 Figure 6-10. Steps to follow using the design and exploration platform

Figure 6 - 11 .

 611 Figure 6-11. Reference supermarket for verification of the models

Figure 6 -

 6 Figure 6-12. Platform interface

Figure 6 -

 6 [13][START_REF] Wetter | Modelica-based modelling and simulation to support research and development in building energy and control systems[END_REF] show the interface to fulfil. It comprises the store characteristics (Figure6-13) and the food conservation characteristics.

Figure 6 - 13 .Figure 6 - 14 .

 613614 Figure 6-13. System environment: store characteristics interface

- 15 .

 15 He chooses the type of architecture and then the compressor, condenser, refrigerant, and pipeline material. The choice of evaporator and expansion valve is by default because the model does not yet select different alternatives, and only the evaporator cooling capacity is calculated. The pipeline diameters are constraints by the type of compressor and condenser chosen.

Figure 6 - 15 .

 615 Figure 6-15. Selection of the components of the existing system

Figure 6 - 16 .

 616 Figure 6-16. Comparison of the performances evolution before and after the change of refrigerant.

Figure 6 - 17 .

 617 Figure 6-17. Pareto space displayed in the platform

Figure 6 -

 6 Figure 6-18. Costtable and 1D interval visualization and selection of constraints using either the table (left) or 1D slider (right)

Figure 6 -

 6 Figure 6-19. Environmental 1D interval visualization and selection of constraints

Figure 6 - 20 .Figure 6 - 21 .

 620621 Figure 6-20. Visualization of all the feasible solutions

A-

  verification step-by-step was conducted throughout the whole process of knowledge model construction and its implementation with Geeglee supporting tool. Eight workshops of discussions with researchers or technicians from INRAE and the Geeglee development team were piloted to discuss: The definition of the concepts of the ontology, its intended use -The platform used for the implementation of the ontology -The content of the knowledge model, i.e., the classes, the relations, the equations, the variables, intermediary data and the implementation into the platform The verification workshops consist of one or more hour sessions of discussions to collect feedbacks on the knowledge model and its implementation on the platform as illustrated in Figure 6-22.

Figure 6 -

 6 Figure 6-22. Workshops with INRAE experts for step-by-step verification

  What characteristics define the context of refrigeration system used? Description of the intended use of a knowledge model, the scenario specification, the knowhow of stakeholders for the design and simulation of refrigeration systems Which components are necessary for designing a retail refrigeration system? Description of the design process, the tools, the information exchanged, etc. Does the proposed knowledge model satisfy the basic knowledge capitalization for the design and exploration of refrigeration systems decision space? Discussion on the concepts, classes, relations, and the correctness of mathematical models Does the ontology describe the reality of the domain? Describe a refrigeration system as it is in reality Describe the real performances of a system Describe the whole life cycle of a refrigeration system

  is detailed. The results are presented in Section 4. The validation results and the answers to our research objective are discussed in Section 5. Two main outcomes are discussed based on the work of (Silva and Winckler, 2017): (1) technical validation, (2) usefulness validation of the proposed artefacts.

Figure 7 -

 7 Figure 7-1 shows the positioning of the chapter in relation to the others.

Figure 7 - 1 .

 71 Figure 7-1. Positioning of the chapter in the thesis

1 .

 1 Criteria and their definition for our validation strategy which the platform covers all the appropriate aspects of the problem: Is the detail level sufficient and are all the aspects of the design problem considered?ConsistencyThe repeatability of the results: Do the results vary if the inputs are the same?

Figure 7 - 4 .

 74 Figure 7-4. Simplified script given to the testers

Figure 7 - 5 .

 75 Figure 7-5. Environmental impacts reduction using the sliders Then, a first assessment of the possible solutions is observed. Depending on the reduction of the environmental impacts' interval, the size of the design space varies between 8,700 and 2,200 solutions. A first design space is assessed in a few minutes for each participant. They can either choose a design solution based on the first results or choose to reiterate the filtering actions.

4 .

 4 Performances order of decision for design space reduction depending on the participants(industrial stakeholders)

5 .

 5 Design solutions obtained with the different uncertainty reduction

Figure 7 - 6 .

 76 Figure 7-6. Example of 2D representation to compare design solutions

Matrix

  𝑈𝑆𝑠𝑖𝑧𝑒 crosses User Profiles with Usage Situations. The users may spend different times in a given usage situation. The question asked to fill the matrix is: "How much time is spent in this usage situation for this user?"

3. Appendix 3 :

 3 Local and global performances assessment implemented in the Geeglee knowledge model

(

  "Refrigerant"."coefficient 1 of refrigerant density in evaporator [kg/m^3.°C^3]"*("T_c [°C]"^3)+"Refrigerant"."coefficient 2 of refrigerant density in evaporator [kg/m^3.°C^2]"*("T_c [°C]"^2)+"Refrigerant"."coefficient 3 of refrigerant density in evaporator [kg/m^3.°C]"*("T_c [°C]")+"Refrigerant"."coefficient 4 of refrigerant density in evaporator [kg/m^3]") Refrigerant characteristics mass of refrigerant in heat exchanger [kg] "volume of primary refrigerant in primary refrigerant pipe heat exchanger [m^3]"*"density of refrigerant in evaporator [kg/m^3]" Refrigerant characteristics mass of secondary refrigerant in evaporator [kg] "Evaporator"."V_ref [m3]"*"secondary refrigerant"."density of refrigerant in pipe [kg/m^3]" Refrigerant characteristics mass of secondary refrigerant in heat exchanger [kg] "volume of secondary refrigerant in secondary refrigerant pipe heat exchanger [m^3]"*"secondary refrigerant"."density of refrigerant in pipe [kg/m^3]" Refrigerant characteristics mass of secondary refrigerant in pipe [kg] "v_secondary ref_pipeline_total [m^3]"*"secondary refrigerant"."density of refrigerant in pipe [kg/m^3]" Secondary pipe characteristics Secondary refrigerant pipeline TRL "secondary refrigerant pipeline"."Technology Readiness Level [#]" Secondary pipe characteristics Volume of material in entire secondary refrigerant pipeline [m^3] "Volume of material in secondary refrigerant discharge pipe [m^3]"+"Volume of material in secondary refrigerant suction pipe [m^3]" Transcritical Emsys_man_TC pipeline [kg CO2 eq]

  TC_mass flow [kg/m^3]"/"TC_rho_pr [kg/m^3]"*("TC_Delta_H [kJ/kg]"/"Conversion from kW to kJ [kW/kJ]")*"Mass of food inside each CS [kg]"*(1/"no of hours in a day [hrs/day]")TranscriticalTC_Hourly Q_dot_food for DC_hor[kW] "TC_mass flow [kg/m^3]"/"TC_rho_pr [kg/m^3]"*("TC_Delta_H [kJ/kg]"/"Conversion from kW to kJ [kW/kJ]")*"Mass of food inside each DC horizontal [kg]"*(1/"no of hours in a day [hrs/day]") Transcritical TC_Hourly Q_dot_food for Dc_ver [kW] "TC_mass flow [kg/m^3]"/"TC_rho_pr [kg/m^3]"*("TC_Delta_H [kJ/kg]"/"Conversion from kW to kJ [kW/kJ]")*"Mass of food inside each DC vertical [kg]"*(1/"no of hours in a day [hrs/day]") Transcritical TC_Hourly Q_dot_light [kW] ("Transcritical storage"."Heat_light [W/m^2]"/"Conversion from kW to W [W/kW]")*("Transcritical storage"."A_floor [m^2]") Transcritical TC_Hourly Q_dot_people [kW] "TC_Hourly Q_dot_people during working time [kW]"+"TC_Hourly Q_dot_people during opening time [kW]" Transcritical TC_Hourly Q_dot_people during opening time [kW] "Nb of people coming during opening time [#]"*("People heat during opening time [W]")/"Conversion from kW to W [W/kW]" Transcritical TC_Hourly Q_dot_people during working time [kW] "Nb of people coming during working time [#]"*("People heat during working time [W]"/"Conversion from kW to W [W/kW]") Transcritical TC_Hourly Q_dot_trans [kW] (("Transcritical storage"."U_sidewall [W/m^2.K]"/"Conversion from kW to W [W/kW]"*"Transcritical storage"."A_sidewall [m^2]")+("Transcritical storage"."U_floor [W/m^2.K]"/"Conversion from kW to W [W/kW]"*"Transcritical storage"."A_floor [m^2]"))*("Supermarket"."Ambient temperature [°C]"-"TC_setpoint temp [°C]") Transcritical TC_Hourly Q_infiltration_day [kW] "Transcritical storage"."X_(air_curtain) [#]"*"Transcritical storage"."V_(air_curtain) [kg/s]"*("Supermarket"."h_ambience [kJ/kg]"-"TC_h_inside [kJ/kg ]") Transcritical TC_Hourly Q_infiltration_night [kW] "Transcritical storage"."U_air_curtainDC [W/m².K]"*"conversion factor W to kW [kW/W]"*"Transcritical storage"."A_air curtainDC [m²]"*("Supermarket"."Ambient temperature [°C]"-"TC_setpoint temp [°C]") Transcritical TC_Hourly Q_night [kW] "TC_Hourly Q_wall [kW]"+"Transcritical storage"."Q_hw [w]"*"conversion factor W to kW [kW/W]"+"TC_Hourly Q_infiltration_night [kW]"+"Transcritical storage"."Q_fan [W]"*"conversion factor W to kW [kW/W]"+(if('Transcritical Horizontal display cabinet'="Transcritical storage"."name") then ("TC_Hourly Q_dot_food for DC_hor [kW]") else ("TC_Hourly Q_dot_food for Dc_ver [kW]"))

Figure 7 .

 7 Figure 7. Méthodologie mise en place pour construire le livre de connaissance

Figure 8 .

 8 Figure 8. Résumé de la méthodologie RID et des résultats obtenus pour évaluer le système sociotechnique existant

Figure 9 .

 9 Figure 9. Présentation des macro-concepts du modèle de connaissance L'outil GEP est composé de quatre sections principales : -High Level Requirement (HLR) où sont décrits les spécifications (entrées, contraintes, objectifs…). -Product Breakdown Structure (PBS) où est décrit l'espace de décision (les options techniques pour le système d'intérêt et les incompatibilités entre solutions techniques). -Technical Portfolio où sont entrées les valeurs intermédiaires liées à chaque option technique. -Engineering Patterns où sont explicités les performances et le comportement physique du système au moyen d'équations.

Figure 10 .

 10 Figure 10. Présentation de la mise en place du concept de scénario dans le GEP Sur la Figure 11, l'exemple de l'espace de décision est montré sur le GEP. Plusieurs architectures de systèmes de réfrigération sont mises en place (primaire, secondaire, logé, transcritique) dans l'espace de décision. Tous les composants d'un système de réfrigération dans un supermarché sont inclus dans la modélisation avec une ou plusieurs alternatives.

Figure 11 .

 11 Figure 11. Présentation de la mise en place de l'espace de décision dans le GEP La qualité de notre modèle de connaissance est évaluée sur plusieurs points : la pertinence du vocabulaire employé, sa clarté, le choix des relations, la cohérence et l'extensibilité, la fidélité par rapport au domaine et l'usage de l'ontologie. Une vérification ligne par ligne du modèle est faite afin d'assurer la cohérence et la fidélité du modèle.

  En reprenant le modèle de connaissance pour la conception de système de réfrigération, la simulation commence par la définition du scénario d'utilisation du système de réfrigération. Ensuite, plusieurs possibilités d'exploitation de la plateforme d'exploration sont proposées à l'utilisateur. Elles sont regroupées en deux catégories :-Évaluerun système de réfrigération existant : o Simuler ses performances globales o Simuler l'impact d'un changement de composant technique sur ses performances -Identifier les meilleures combinaisons de composants selon une analyse mono-ou multi-objectifs. Cette simulation peut être utilisée soit sur un système existant, soit pour un nouveau système. Les objectifs correspondent à une combinaison de performances globales. Plusieurs scénarios d'analyse sont proposés sur la plateforme : o Identifier les composants avec le plus faible coût total (évalué selon les coûts d'installation et opérationnels sur toute la durée de vie du système) o Identifier les composants avec le moins de consommation énergétique (évalué par année selon le type et le nombre d'équipement, i.e. meuble de vente et chambre froide) o Identifier les composants avec le plus faible impact environnemental (évalué sur tout le cycle de vie du système) o Identifier les composants et le système le plus mature (évalué par l'échelle de Technological Readiness Level (TRL)) o Identifier les composants avec le meilleur score de maintenance (évalué par une échelle de critère de maintenance) o Identifier les composants ayant le coût total et les impacts environnementaux les plus faibles Ce dernier exemple d'utilisation listé précédemment est montré dans la Figure 12. On cherche à connaitre l'ensemble des meilleurs compromis de combinaisons au sens de Pareto.

Figure 12 .

 12 Figure 12. Exemple d'une analyse multi-objectifs entre le coût total et les impacts environnementaux sur la plateforme de simulation et d'exploration Pour chaque scénario d'usage, les performances locales et globales présentées dans le modèle de connaissance sont évaluées et peuvent être explorées plus en détail afin de répondre aux spécifications de l'utilisateur. Un exemple de l'étude détaillé exploré avec l'impact environnemental est présenté dans la Figure 13. La performance globale d'impacts environnementaux est présentée en premier puis une répartition des impacts directs (liés aux fuites de réfrigérant) et indirects (liés à la consommation d'électricité) est définie.

Figure 13 .

 13 Figure 13. Exemple de l'évaluation des impacts environnementaux de toutes les alternatives possibles

Table

Table 2 -

 2 1. Application, definitions and equipment of the refrigeration domain inspired by (IEA 2020) ............................................................................................................Table 2-2. Summary table of commonly used refrigerants classified by their chemical type, R-number, GWP, ODP and safety group ................................................................... Table 2-3. Related work on refrigeration systems and buildings maintenance assessment .............................................................................................................................. Table 2-4. Maintenance qualitative assessment table adapted from (Geng et al. 2013). .................................................................................................................................................. Table 2-5. Summary of some related works on environmental impact assessment of refrigeration systems. ............................................................................................................ Table 2-6. Table of the different platform specifications. ................................................ Table 2-7. Table of the different outputs assessed on the platforms. ............................

Table 3 -

 3

1. Proposed research methodology for the present work adapted from the DRM .........................................................................................................................................

Table 4-1. Summary of the interviews (role of the interviewees and their organizations) ......................................................................................................................... Table

4-2

. Detail questions of the interviews ..................................................................... Table 4-3. Table of the users and their role in the process. ............................................. Table 4-4. List of problems identified in in the socio-technical RS and classified in four main categories. ..................................................................................................................... Table 4-5. UsEs matrix: To what extent does this existing solution facilitate the usage situation? (scale from 0 = never to 5 = frequently) .......................................................... Table 5-1. Literature review summary of the main methodologies for ontology development ......................................................................................................................... Table 5-2. Summary of the knowledge acquisition (KA) ................................................ Table 5-3. List of concepts for the refrigeration system design level, their definitions, and their classes associated with retail refrigeration ...................................................... Table 5-4. Scenario related classes, extended sub-classes and instances for the application to retail refrigeration systems ........................................................................

Table 5 -

 5 5. Refrigeration system solution classes, extended sub-classes and instances for the application to retail refrigeration systems ........................................................... Table 5-6. Global performance: environmental impact class, its extended sub-classes and instances for the application to retail refrigeration systems ..................................

Table 7 .

 7 Verification investigation: questions and outcomes of the workshops .........

Table 8 .

 8 Results of the total energy consumption in kWh calculated with our model and the data from the case study (last column) .............................................................. Table 7-1. Criteria and their definition for our validation strategy ............................... Table 7-2. Criteria and the associated evaluation category and tool. ..........................

Table 7 -

 7

3. Table of target users and their activity ........................................................... Table 7-4. Performances order of decision for design space reduction depending on the participants (industrial stakeholders) ......................................................................... Table 7-5. Design solutions obtained with the different uncertainty reduction .........

Table 7 -

 7 6. Comparison of the performances implemented in our platform and in the existing refrigeration system design platforms ............................................................... 194

Table 2 -

 2

	1. Application, definitions and equipment of the refrigeration domain inspired by (IEA
			2020)
		Commercial and industrial	Cold-chain	Thermal	Domestic
		cooling		preservation	comfort	product
				during		conservation
	Application			transportation
		Commercial	Industrial	Refrigerated	Space and	Domestic
		refrigeration	refrigeration	transports	mobile air	refrigeration
					conditioning
		Small	Large volume	Critical link in	Removing	Short term
		volume of	storage for the	the food chain	heat and	preservation
		storage and	agri-food,	to maintain the	moisture	of domestic
		display	pharmaceutica	temperature	from the	food to
		chilled or	l and	integrity of	interior of	preserve its
		frozen goods	healthcare	perishable	an occupied	quality and
	Definition	for	products, floral	products from	space to	maintain the
		commercial	products etc.	the producers	improve the	food chain.
		sale in	facilities.	to the retailers.	comfort of
		stores,			occupants in
		restaurants,			cars,
		supermarket,			buildings,
		etc.			trains etc.
		Display	Large	Transport	Heat pumps,
		cabinets and	refrigeration	refrigeration	air
		cold rooms,	equipment for	units	conditioning
	Equipment	commercial equipment	industrial usage		units (unitary or
					mobiles for
					transport
					AC)

Table 2 -

 2 2. Summary table of commonly used refrigerants classified by their chemical type, Rnumber, 100 years-Global Warming Potential (GWP), Ozone Depletion Potential (ODP) and safety group

	Type	Refrigerant	Refrigerant name	100 years-GWP	ODP	Safety
		R-number				group
	CFC	12 502	Dichlorodifluoromethane 10900 CFC blend 4750	0.82 1	A1 A1
	HCFC	22	Chlorodifluoromethane	1810	0.055 A1
		134a	Tetrafluoroethane	1430	0	A1
	HFC	404A	HFC blend	3922	0	A1
		407C	HFC blend	1770	0	A1
	HC	290	Propane	3	0	A3
	Natural	717 744	Ammonia Carbon dioxide	0 1	0 0	B2L A1
	HFO	1234yf	Tetrafluoropropene	4	0	A2L

Table 2 -

 2 3. Related work on refrigeration systems and buildings maintenance assessment

	Authors -date Domain -Subject		Maintenance assessment method
	(Alrwashdeh	Refrigeration -cost analysis	Maintenance cost is calculated as a
	and	Ammari	of two different refrigeration	percentage of initial cost (ex: 10% which
	2019)			systems			corresponds to 60$/yr)
	(Amrina, Kamil,	Buildings -Multi Criteria	Maintenance is assessed with 3 criteria:
	and Aridharma	Approach for Sustainable	1. Economic (Maintenance cost; Availability
	2020)			Maintenance Performance	rate; Product quality; Failure rate; Ease of
				Evaluation			maintenance;	Technology	applied;
							Breakdown	rate)
							2. Social (Working environment; Occupational
							health and safety; Employee involvement;
							Lighting and ventilation; Training and
							education)
							3.	Environmental	(Emission;	Energy
							consumption; Material consumption; Water
							usage)
	(Kumar Singh,	Refrigeration -Comparative	Maintenance cost is included in the
	Kumar,		and	energy,	exergy	and	operational costs, by using a maintenance
	Gupta 2020)	economic analysis of a	factor (ϕ) of 1.06 (rate of parts changed per
				cascade refrigeration system	year)
	(Imamura,		Refrigeration -evaluation on	Qualitative evaluation of the maintenance:
	Kamiya,	and	A2L refrigerants in situations	observed flammability; temperature; flame
	Sugawa 2015)	of service and maintenance	propagation
	By extending our field of research to ergonomics, another field of industrial engineering,

Table 2 -

 2 4. Maintenance qualitative assessment table adapted from

Table 2 -

 2 5. Summary of some related works on environmental impact assessment of refrigeration systems.

	Authors

and date Location Description Environmental impact assessment method (Maykot, Weber, and Maciel 2004)

			Brazil	Evaluation of typical refrigeration	TEWI metric
				equipment for household and light
				commercial applications in North
				America, Europe and Asia with different
				refrigerant
	(Youbi-		France	Comparative study of direct and indirect	SimaPro
	Idrissi 2006)			systems in France and in Germany for	Eco-indicator 99 (E)
				MT an LT
	(Bovea,		Spain	Comparative study of environmental	SimaPro
	Cabello, and		impacts of commonly used refrigerants	1) CML
	Querol 2007)		(HCFCs, HFCs and natural ones), with	2) Eco Indicators 95,
				different commercial refrigeration	99 and EPS 00
				systems for MT and LT
	(Beshr et al.	USA	Analyse of the LCCP for supermarket	LCCP metric
	2015)			systems with different refrigerants in six
				regions with climate differences
	(Cascini et al.	Italy	Evaluation of refrigeration systems under	SimaPro
	2016)			different configurations for MT	IPCC 2007 GWP 100a
	(Islam et al.	Japan	Environmental impact assessment of	TEWI metric
	2017)			supermarket refrigerating systems using
				different refrigerant
	2.4.	Design problem-solving methods
	In section 2.3.1, different activities in the design processes were described: definition of
	requirements, solutions research, detailed design, embodiment design with simulations,
	prototyping, dimensioning, etc. During these steps, decisions are taken toward multiple "best"
	solutions. In a complex system design problem, as for the design of a refrigeration system,
	multiple actors with different expertises and objectives collaborate. The association of different
	actors in the same design problem is called collaborative design. It can be centralized, or in the
	most frequent case, decentralized, i.e. the decision is distributed, and the design problem is
	divided into sub-problems

Table 2

 2

	-6, the tools compared are the following: CoolPack, CoolTool, PackCalculationPro,
	CoolSelector, CyberMart, Manufacturer software, EnergyPlus, SuperSmart and other types of
	digital tools. They are chosen based on their main purpose to support the design of
	refrigeration systems. It includes the support to choose components, architecture and
	refrigeration, simulate the system performances, optimize the system based on different
	criteria, and compare solutions on one or more criteria.
	Six criteria are used to compare existing tools for the design and simulation of refrigeration
	systems:
	 Language: programming language or tools used for the development of the software
	and the models
	 Modularity: possibility of a tool to assess the user's specific system and to add a new
	model into the software
	 Decision aid tool: different levels are specified to assess the capacity of the platform to
	help decision making: it can help the dimensioning of the components, it can simulate
	and optimize the system in operating conditions, it can compare solutions, it can help
	build an operational strategy.
	 Required knowledge: the level of knowledge required to use the platform (* = minimum
	level; + = medium level; ++= expert level)

Table 2

 2

	-6 presents a comparison of different platforms for the design and assessment of
	refrigeration systems based on the eight criteria.

Table 2 -

 2 6. Table of the different platform specifications.

	Software specifications

Table 2 -

 2 7. Table of the different outputs assessed on the platforms.

		Quantative performances		Qualitative performances
		consumption	Energy	impacts	Environmental	Costs	Space used	conditions	Installation	conditions	Maintenance	TRL Technology	Lifetime
	CoolPack	Yes	No	Yes	Yes	Through costs	Through costs Through costs	No	No
	SuperSim	Yes	No	Yes	No	Through costs	Through costs	No	Yes
	*Instructions from the manufacturer

Table 4 -

 4 1. Summary of the interviews (role of the interviewees and their organizations)

		Role	Organization
	1,2,3	Research engineer, PhD	Research lab
	4	Research engineer	Research lab
	5	Refrigeration engineer	Research lab
	6	CEO	Small company
	7	Technical expert in charge of innovation	Large company
	8	R&D project leader & CAD manager	Large company
	9	Eco-design expert	Recycling body

Table 4 -

 4 3 presents a summary of the different user profiles involved in the lifecycle of a refrigeration system

Table 4 -

 4 3. Table of the users and their role in the process.

	Actor

Role Academic researcher Person or group of people who keep pace with the changing regulations and anticipate them through a combination of theoretical and experimental work. (ex: researchers at INRAE) Technological transfer centre Person or group of people which transfers the results of research to businesses by leveraging and managing laboratories' portfolio of technologies via agreements with industrial firms; promotes the creation of innovative companies and assist them with development; supports the set-up, negotiation and management of collaborative research projects at the European and national level by bringing to bear its expertise in management project engineering; organises, leads and manages major research and development programmes; makes resources available to the environmental technologies sector by creating a porous interface between public research laboratories and the business world. (ex: INRAE transfert)

Table 4 -

 4 4. List of problems identified in in the socio-technical RS and classified in four main categories.

Table 4 -

 4

[START_REF] Yannou | Capturing the relevant problems leading to pain-and usage-driven innovations: The Dependency Structure Modeling Value Bucket algorithm[END_REF]

gives an example of matrix filling. From the data collected during the interviews and from observations, it quantifies the adaptation of the existing solution categories to usage situations.

Table 4 -

 4

	5. UsEs matrix: To what extent does this existing solution facilitate the usage situation? (scale
			from 0 = never to 5 = frequently)
		Software so-	KM solu-	EN 378 gui-	Methodologi-	Technologi-	Tool so-
		lutions	tions	delines	cal solutions	cal solutions	lutions
	Design,
	manufac-ture and	4	4	3	3	2	2
	installation
	Exploitation 4	5	4	4	4	5

  As shown in the diagnosis of the refrigeration domain, designing a refrigeration system (RS) is a highly collaborative process involving many actors, tools, knowledge and disciplines. Digital tools are now essential during the RS design process. They are used to calculate the cooling load, dimension the system, optimize the components, simulate the machine's efficiency, manage the installation and the End-of-Life (EOL), and calculate the environmental impacts. They use different models, which are complementary for most of them but also redundant, contradictory or too far from reality. Moreover, different actors may use different software, making communication difficult. The literature review highlights a lack of an integrated approach regarding the design of refrigeration systems (Research gap 2). We have shown from the diagnosis of the refrigeration domain that the most important value buckets (VBs) are the lack of understanding of new technologies, the lack of common tools and knowledge.

	addition to the literature review (150 articles and documents) and field observations. Ontologies are
	used for capitalizing knowledge in different domains by defining meta-classes, relationships between

them, vocabulary, instances. Thus, this chapter presents a knowledge model for the design and simulation of a refrigeration system applied to retail refrigeration. The data acquisition results allow us to establish the design process of a refrigeration system architecture in different usage scenarios. The use of ontology and a design and exploration platform in the decision-making process has many benefits. It reduces industry segmentation and helps design optimal systems. The objective of such knowledge model construction is to support the design process in the refrigeration domain for different types of actors involved in the process. Four concepts and their associated classes and relations are described as related to the context, system design solution, and performance.

1. Introduction: Why an ontology?

  1998;[START_REF] Kharbat | Building Ontology from Knowledge Base Systems[END_REF][START_REF] Van Heijst | Using Explicit Ontologies in KBS Development[END_REF]: knowledge representation, general, meta-, domain, task, domain-task, application, index, tell and ask. To

Table 5 -

 5

1. Literature review summary of the main methodologies for ontology development

Table 5 -

 5 2 summarizes the different knowledge acquisition processes. Fifteen interviews with experts in the refrigeration domain were conducted. The interviewees are six researchers, one technician and one frigorific engineer or industrial expert (suppliers and designers), two from large companies of supply and manufacture, and five from small or medium companies. Documentation,

standards, various projects were also investigated and analysed. An extensive literature review of refrigeration (around 120 articles from conference proceedings and journals) systems research, and similar systems ontology such as building, electricity energy supports the knowledge acquisition. The research keywords are listed in the summary table for the data collection of case study in the literature.

Table 5 -

 5 2. Summary of the knowledge acquisition (KA)

	CATEGORY OF KA	KNOWLEDGE ACQUIRED		QUANTITY
	INTERVIEWS	Researchers, technician, frigorific engineers,	15 interviewees
		suppliers and designers expertise on the
		process, the vocabulary, the data
	WORKSHOPS	Researchers, technician, frigorific engineers,	8 workshops
		suppliers and designers' opinion for validation
		and verification
	OBSERVATIONS	Documentation
		Regulations
		Standards
		Industrial projects			~150	articles	and
	LITERATURE REVIEW	Key	words:	REFRIGERATION	SYSTEM	documents
		ONTOLOGY; ENERGY SYSTEM ONTOLOGY;
		REFRIGERATION SYSTEM; REFRIGERATION
		SYSTEM DESIGN; ONTOLOGY OF ENERGY
		SYSTEM; BUILDING ONTOLOGY

Table 5 -

 5 4. Scenario related classes, extended sub-classes and instances for the application to retail refrigeration systems

	Class	Extended sub-class	Description
	System	Locstore	Location of the store. It determines the external
	environment		temperature during the year
		Text	External temperature. It determines the cooling
			power necessary to maintain a constant cold
			production during the year.
		Astore	Store surface area
		OpeningHour	Opening hour of the store
		ClosingHour	Closing hour of the store
		Vfpos	Volume of food stored in refrigeration (positive
			temperature)
		Tpos_setpoint	Range of positive temperature for food
			refrigeration storage
		PosDisplayCabinet	Number of positive temp display cabinets
		PosColdRoom	Number of refrigerated cold storage room
		Vfpos	Volume of frozen food stored (negative
			temperature)
		Tneg_setpoint	Range of negative temperature for food
			refrigeration storage
		NegDisplayCabinet	Number of negative temp display cabinets
		NegColdRoom	Number of frozen cold storage room
		Nd	Number of doors opening
	Stakeholder	Researcher; designer;	Identity of the stakeholder that uses the tool: its
		Refrigeration engineer; final	identity as an actor of the domain, its expertise, and
		user; EOL treatment parties	its mindset
	Objective	Problem or performances
		analysis; solution design;
		decision space exploration;
		technological change analysis

Description of the outcome of the problem analysis.

Table 8 .

 8 The yearly consumption calculated with the model is 252,696.3 kWh. The data from the real case study is approximately 291,600 kWh per year. The calculated yearly consumption is lower than the reference case study by 12 %, which is acceptable considering different factors impact the result. The difference in monthly consumption shown in the table can be explained mainly by the weather data in the model and that of 2010. Indeed, according to Meteo-France data, in 2010, the temperature in Paris varied between -1°C in December and 36°C in July so that the consumption data can go up to 700 kWh for the hottest months. The data taken into account in the model are average data that vary over the year between 2.7°C and 25.2°C, so the highest data is 650 kWh.

Table 8 .

 8 Results of the total energy consumption in kWh calculated with our model and the data from the case study(last column)

		MODEL RESULTS (KWH)			ADEME	CASE
						STUDY	RESULTS
						(KWH)
		Day	Night	Daily	Monthly	Monthly
	JANUARY	24.2	22.0	554.4	18016.9	Between 400 kWh/
						day in winter and
	FEBRUARY	24.8	22.0	561.4	18415.1	790 kWh/ day in
	MARCH	26.6	23.3	598.8	19802.7	summer
	APRIL	28.2	24.3	629.6	20982.9
	MAY	30.0	26.0	672.3	22337.4
	JUNE	31.4	27.4	705.2	23362.8
	JULY	32.5	28.3	729.5	24174.8
	AUGUST	32.4	28.2	727.9	24110.3
	SEPTEMBER 30.7	26.9	690.6	22836.2
	OCTOBER	28.5	25.4	646.8	21222.6
	NOVEMBER 26.0	23.5	593.8	19308.8
	DECEMBER	24.4	22.3	560.4	18125.8
	TOTAL	339.6	299.6	7670.6	252696.3	291600
	YEARLY

Table 7 -

 7 2. Criteria and the associated evaluation category and tool.

	Criteria	Evaluation category	Evaluation tool
			Observation of the design space result. We
	Efficacy	Observations	consider the platform efficacy if a result is
			provided (0, 1 or more design solutions obtained)
	Validity	measurement Quantitative	Response to a questionnaire and measurement

Table 7 -

 7 3. Table of target users and their activity

	Stakeholder	Task
		Simulate a different technical module's alternative of an existing system:
	Supermarket manager

  Matrix 𝑈𝑠𝐸𝑠 crosses Usage Situations and Existing Solutions. The question asked to fill the matrix is: "To what extent does this existing solution facilitate this usage situation?"

	Design,
	manufac-ture and	4	4	3	3	2	2
	installation
	Exploita-tion	4	5	4	4	4	5
	End-of-life 3	4	4	3	2	5
		Software solutions	KM solu-tions	EN guidelines 378	Methodologi-cal solutions	Technolog-ical solu-tions	Tool so-lutions

  Matrix 𝑈𝑃𝑠𝑖𝑧𝑒 is the vector assessing the relative size of User Profiles segments. The question asked to fill the matrix is: "What is the relative size of user segments?"

	Academic researcher	1%
	Technological transfer centre	1%
	Professional organism	1%
	R&D researcher	3%
	Design office	8%
	Consulting firms	5%
	Refrigeration engineer	30%
	Supplier	20%
	Components designer (drawing)	5%
	Manufacturer	20%
	Final user	5%
	EOL treatment parties	1%
	From SNEFCCA (Syndicat National des Entreprises du Froid et des équipements en Cuisines profes-
	sionnelles et Conditionnement de l'Air), 2600 companies for design and installation, maintenance
	activities (includes supplier, refrigeration engineers/technicians and design offices) are registered.

  Refrigerant"."coefficient 1 of refrigerant density in condenser [kg/m^3.°C^2]"*("Max T_h [°C]"^2)+"Refrigerant"."coefficient 2 of refrigerant density in condenser [kg/m^3.°C]"*("Max T_h [°C]")+"Refrigerant"."coefficient 3 of refrigerant density in condenser [kg/m^3]")

	"+"Horizontal DC"."Q_hw [w]"*"conversion factor W to kW [kW/W]"+"Horizontal DC"."Q_lighting [W]"*"conversion factor W to kW [kW/W]"+"Hourly Q_infiltration_day_hor [kW]"+"Hourly Q_radiation_day_hor [kW]"+"Horizontal DC"."Q_fan [W]"*"conversion factor W to kW [kW/W]"+"Hourly Q_dot_food for DC_hor [kW]" "Hourly Q_wall_ver [kW]"+"Vertivcal DC"."Q_hw [w]"*"conversion factor W to kW [kW/W]"+"Vertivcal DC"."Q_lighting [W]"*"conversion factor W to kW [kW/W]"+"Hourly Q_infiltration_day_ver [kW]"+"Hourly Q_radia-tion_day_ver[kW]"+"Vertivcal DC"."Q_fan [W]"*"conversion factor W to kW [kW/W]"+"Hourly Q_dot_food for Dc_ver [kW]" if('NO Horizontal Display Cabinet'="Horizontal DC"."name") then 0 else "Stored food in display cabinet"."mass flow [kg/m^3]"/"Stored food in display cabi-net"."rho_pr [kg/m^3]"*("Stored food in display cabinet"."Delta_H [kJ/kg]"/"Conversion from kW to kJ [kW/kJ]")*"Mass of food inside each DC horizontal [kg]"*(1/"no of hours in a day [hrs/day]") "Horizontal DC"."X_(air_curtain) [#]"*"Horizontal DC"."V_(air_curtain) [kg/s]"*("Supermarket"."h_ambience [kJ/kg]"-"Setpoint temperature"."h_in-side [kJ/kg ]") "Vertivcal DC"."X_(air_curtain) [#]"*"Vertivcal DC"."V_(air_curtain) [kg/s]"*("Supermarket"."h_ambience [kJ/kg]"-"Setpoint temperature"."h_in-side [kJ/kg ]") "Horizontal DC"."U_air_curtainDC [W/m².K]"*"conversion factor W to kW [kW/W]"*"Horizontal DC"."A_air curtainDC [m²]"*("Supermarket"."Ambient temperature [°C]"-"Setpoint temperature"."setpoint temperature [°C]") "Vertivcal DC"."U_air_curtainDC [W/m².K]"*"conversion factor W to kW [kW/W]"*"Vertivcal DC"."A_air curtainDC [m²]"*("Supermarket"."Ambient temperature [°C]"-"Setpoint temperature"."setpoint temperature [°C]") "Hourly Q_wall_hor [kW]"+"Horizontal DC"."Q_hw [w]"*"conversion factor W to kW [kW/W]"+"Hourly Q_infiltration_night_hor [kW]"+"Horizontal DC"."Q_fan [W]"*"conversion factor W to kW [kW/W]"+"Hourly Q_dot_food for DC_hor [kW]" "Hourly Q_wall_ver [kW]"+"Vertivcal DC"."Q_hw [w]"*"conversion factor W to kW [kW/W]"+"Hourly Q_infiltration_night_ver [kW]"+"Vertivcal DC"."Q_fan [W]"*"conversion factor W to kW [kW/W]"+"Hourly Q_dot_food for Dc_ver [kW]" "Horizontal DC"."hro [W/m²K]"*"conversion factor W to kW [kW/W]"*"Hori-zontal DC"."A_door [m^2]"*("Horizontal DC"."T_wall [°C]"-"Setpoint tempera-ture"."setpoint temperature [°C]")*"Horizontal DC"."phi_1 [#]"*"Horizontal DC"."phi_2 [#]" "Vertivcal DC"."phi_2 [#]" "Horizontal DC"."U_wallDC [W/m².K]"*"conversion factor W to kW [kW/W]"*"Horizontal DC"."A_wallDC [m²]"*("Supermarket"."Ambient temper-ature [°C]"-"Setpoint temperature"."setpoint temperature [°C]") "Vertivcal DC"."U_wallDC [W/m².K]"*"conversion factor W to kW [kW/W]"*"Vertivcal DC"."A_wallDC [m²]"*("Supermarket"."Ambient tempera-ture [°C]"-"Setpoint temperature"."setpoint temperature [°C]") Energy_consumption_DC("Hourly Q_night_ver [kW]","pinch_h [°C]","eta [#]","T_c [°C]","No of days used in a month (31) [days]","No of days used in a month (28) [days]","No of days used in a month (30) [days]","Supermarket's Closed time [hrs]","time for each defrost [hrs]","number of defrosts in closed time [#]","Location_data"."01 Jan_Min_temp [°C]","Location_data"."02 Feb_Min_temp [°C]","Location_data"."03 Mar_Min_temp [°C]","Loca-tion_data"."04 April_Min_temp [°C]","Location_data"."05 May_Min_temp [°C]","Location_data"."06 Jun_Min_temp [°C]","Location_data"."07 Jul_Min_temp [°C]","Location_data"."08 Aug_Min_temp [°C]","Loca-tion_data"."09 Sep_Min_temp [°C]","Location_data"."10 Oct_Min_temp [°C]","Location_data"."11 Nov_Min_temp [°C]","Location_data"."12 Dec_Min_temp [°C]") in closed time [#]","Supermarket"."Ambient temperature [°C]",0,0,0,0,0,0,0,0,0,0,0) (("GWP Recycling steel [kg CO2 eq/kg]"*"mass of steel in the components ex-cept pipelines [kg]")+("GWP Recycling copper [kg CO2 eq/kg]"*"mass of cop-per in the components except pipelines [kg]")+("GWP Recycling aluminium [kg CO2 eq/kg]"*"mass of Aluminium in the components except pipelines [kg]"))+((if('Copper'="pipeline discharge material"."name") then ("GWP Recy-cling copper [kg CO2 eq/kg]") elseif('Plastic'="pipeline discharge mate-rial"."name") then ("GWP Recycling plastic [kg CO2 eq/kg]") else ("GWP Recy-cling steel [kg CO2 eq/kg]"))*"Mass of all discharge pipes [kg]")+((if('Cop-per'="pipeline suction material"."name") then ("GWP Recycling copper [kg CO2 eq/kg]") elseif('Plastic'="pipeline suction material"."name") then ("GWP Recycling plastic [kg CO2 eq/kg]") else ("GWP Recycling steel [kg CO2 eq/kg]"))*"Mass of all suction pipe [kg]") (("GWP Recycling steel [kg CO2 eq/kg]"*"mass of steel in the components ex-cept pipelines [kg]")+("GWP Recycling copper [kg CO2 eq/kg]"*"mass of cop-per in the components except pipelines [kg]")+("GWP Recycling aluminium [kg CO2 eq/kg]"*"mass of Aluminium in the components except pipelines [kg]"))+((if('Copper'="pipeline discharge material"."name") then ("GWP Recy-cling copper [kg CO2 eq/kg]") elseif('Plastic'="pipeline discharge mate-rial"."name") then ("GWP Recycling plastic [kg CO2 eq/kg]") else ("GWP Recy-cling steel [kg CO2 eq/kg]"))*("Mass of all discharge pipes [kg]"+"mass of sec-ondary refrigerant discharge pipe [kg]"))+((if('Copper'="pipeline suction mate-rial"."name") then ("GWP Recycling copper [kg CO2 eq/kg]") elseif('Plas-tic'="pipeline suction material"."name") then ("GWP Recycling plastic [kg CO2 eq/kg]") else ("GWP Recycling steel [kg CO2 eq/kg]"))*("Mass of all suction pipe [kg]"+"mass of secondary refrigerant suction pipe [kg]")) ((("GWP Recycling steel [kg CO2 eq/kg]"*"mass of steel in the components ex-cept pipelines [kg]")+("GWP Recycling copper [kg CO2 eq/kg]"*"mass of cop-per in the components except pipelines [kg]")+("GWP Recycling aluminium [kg CO2 eq/kg]"*"mass of Aluminium in the components except pipelines [kg]"))+((if('Copper'="pipeline discharge material"."name") then ("GWP Recy-cling copper [kg CO2 eq/kg]") elseif('Plastic'="pipeline discharge mate-rial"."name") then ("GWP Recycling plastic [kg CO2 eq/kg]") else ("GWP Recy-cling steel [kg CO2 eq/kg]"))*("Mass of all discharge pipes [kg]"))+((if('Cop-per'="pipeline suction material"."name") then ("GWP Recycling copper [kg CO2 eq/kg]") elseif('Plastic'="pipeline suction material"."name") then ("GWP Recycling plastic [kg CO2 eq/kg]") else ("GWP Recycling steel [kg CO2 eq/kg]"))*("Mass of all suction pipe [kg]")))*("Number of Vertical Display Cabi-net [#]"+"Number of horizontal Display Cabinets [#]") (("GWP Recycling steel [kg CO2 eq/kg]"*"mass of steel in the components ex-cept pipelines [kg]")+("GWP Recycling copper [kg CO2 eq/kg]"*"mass of cop-per in the components except pipelines [kg]")+("GWP Recycling aluminium [kg CO2 eq/kg]"*"mass of Aluminium in the components except pipelines [kg]"))+((if('Copper'="pipeline discharge material"."name") then ("GWP Recy-cling copper [kg CO2 eq/kg]") elseif('Plastic'="pipeline discharge mate-rial"."name") then ("GWP Recycling plastic [kg CO2 eq/kg]") else ("GWP Recy-cling steel [kg CO2 eq/kg]"))*("Mass of all discharge pipes [kg]"+"Mass of all TC discharge pipes [kg]"))+((if('Copper'="pipeline suction material"."name") then ("GWP Recycling copper [kg CO2 eq/kg]") elseif('Plastic'="pipeline suction ma-terial"."name") then ("GWP Recycling plastic [kg CO2 eq/kg]") else ("GWP Re-cycling steel [kg CO2 eq/kg]"))*("Mass of all suction pipe [kg]"+"Mass of all TC suction pipe [kg]")) "mass of entire heat exchanger [kg]"*(("Heat Exchanger"."Steel in component [%]"*"GWP steel [kg CO2 eq/kg]")+("Heat Exchanger"."Copper in component [%]"*"GWP copper [kg CO2 eq/kg]")+("Heat Exchanger"."Aluminium in compo-nent [%]"*"GWP aluminium [kg CO2 eq/kg]")+("Heat Exchanger"."Lubricating oil in component [%]"*"GWP lubricating oil [kg CO2 eq/kg]")+("Heat Ex-changer"."Casting/Forging [%]"*"GWP Casting/Forging [kg CO2 eq/kg]")+("Heat Exchanger"."Termoforming/Injection moulding [%]"*"GWP Termoforming/Injection moulding [kg CO2 eq/kg]")+("Heat Ex-changer"."Cut/bending/deep drawing [%]"*"GWP Cut/bending/deep drawing [kg CO2 eq/kg]")+("Heat Exchanger"."Wire drawing/extrusion/lamination [%]"*"GWP Wire drawing/extrusion/lamination [kg CO2 eq/kg]")+("Heat Ex-changer"."Welding/soldering [%]"*"GWP Welding/soldering [kg CO2 eq/kg]")+("Heat Exchanger"."Powder coating/Zinc coating/cataphoresis/ano-dizing [%]"*"GWP Powder coating/Zinc coating/cataphoresis/anodizing [kg CO2 eq/kg]")+("Heat Exchanger"."Other machining [%]"*"GWP other machin-ing [kg CO2 eq/kg]")) "Number of circulation pumps [#]"*"Circulation Pump"."Mass [kg]"*(("Circula-tion Pump"."Steel in component [%]"*"GWP steel [kg CO2 eq/kg]")+("Circula-tion Pump"."Copper in component [%]"*"GWP copper [kg CO2 eq/kg]")+("Cir-culation Pump"."Aluminium in component [%]"*"GWP aluminium [kg CO2 eq/kg]")+("Circulation Pump"."Lubricating oil in component [%]"*"GWP lubri-cating oil [kg CO2 eq/kg]")+("Circulation Pump"."Casting/Forging [%]"*"GWP Casting/Forging [kg CO2 eq/kg]")+("Circulation Pump"."Termoforming/Injec-tion moulding [%]"*"GWP Termoforming/Injection moulding [kg CO2 eq/kg]")+("Circulation Pump"."Cut/bending/deep drawing [%]"*"GWP Cut/bending/deep drawing [kg CO2 eq/kg]")+("Circulation Pump"."Wire draw-ing/extrusion/lamination [%]"*"GWP Wire drawing/extrusion/lamination [kg CO2 eq/kg]")+("Circulation Pump"."Welding/soldering [%]"*"GWP Weld-ing/soldering [kg CO2 eq/kg]")+("Circulation Pump"."Powder coating/Zinc coating/cataphoresis/anodizing [%]"*"GWP Powder coating/Zinc coating/cata-phoresis/anodizing [kg CO2 eq/kg]")+("Circulation Pump"."Other machining [%]"*"GWP other machining [kg CO2 eq/kg]")) "Number of condensers [#]"*"Condenser"."Mass [kg]"*(("Condenser"."Steel in component [%]"*"GWP steel [kg CO2 eq/kg]")+("Condenser"."Copper in com-ponent [%]"*"GWP copper [kg CO2 eq/kg]")+("Condenser"."Aluminium in component [%]"*"GWP aluminium [kg CO2 eq/kg]")+("Condenser"."Lubricat-ing oil in component [%]"*"GWP lubricating oil [kg CO2 eq/kg]")+("Conden-ser"."Casting/Forging [%]"*"GWP Casting/Forging [kg CO2 eq/kg]")+("Conden-ser"."Termoforming/Injection moulding [%]"*"GWP Termoforming/Injection moulding [kg CO2 eq/kg]")+("Condenser"."Cut/bending/deep drawing [%]"*"GWP Cut/bending/deep drawing [kg CO2 eq/kg]")+("Condenser"."Wire drawing/extrusion/lamination [%]"*"GWP Wire drawing/extrusion/lamination [kg CO2 eq/kg]")+("Condenser"."Welding/soldering [%]"*"GWP Welding/sol-dering [kg CO2 eq/kg]")+("Condenser"."Powder coating/Zinc coating/catapho-resis/anodizing [%]"*"GWP Powder coating/Zinc coating/cataphoresis/anodiz-ing [kg CO2 eq/kg]")+("Condenser"."Other machining [%]"*"GWP other ma-chining [kg CO2 eq/kg]")) "Number of evaporators [#]"*"Evaporator"."Mass [kg]"*(("Evaporator"."Steel in component [%]"*"GWP steel [kg CO2 eq/kg]")+("Evaporator"."Copper in component [%]"*"GWP copper [kg CO2 eq/kg]")+("Evaporator"."Aluminium in component [%]"*"GWP aluminium [kg CO2 eq/kg]")+("Evaporator"."Lubricat-ing oil in component [%]"*"GWP lubricating oil [kg CO2 eq/kg]")+("Evapora-tor"."Casting/Forging [%]"*"GWP Casting/Forging [kg CO2 eq/kg]")+("Evapora-tor"."Termoforming/Injection moulding [%]"*"GWP Termoforming/Injection moulding [kg CO2 eq/kg]")+("Evaporator"."Cut/bending/deep drawing [%]"*"GWP Cut/bending/deep drawing [kg CO2 eq/kg]")+("Evaporator"."Wire drawing/extrusion/lamination [%]"*"GWP Wire drawing/extrusion/lamination [kg CO2 eq/kg]")+("Evaporator"."Welding/soldering [%]"*"GWP Welding/sol-dering [kg CO2 eq/kg]")+("Evaporator"."Powder coating/Zinc coating/catapho-resis/anodizing [%]"*"GWP Powder coating/Zinc coating/cataphoresis/anodiz-ing [kg CO2 eq/kg]")+("Evaporator"."Other machining [%]"*"GWP other ma-chining [kg CO2 eq/kg]")) ((if('Copper'="pipeline discharge material"."name") then ("GWP copper [kg CO2 eq/kg]") elseif('Plastic'="pipeline discharge material"."name") then ("GWP plastic [kg CO2 eq/kg]") else ("GWP steel [kg CO2 eq/kg]"))*"mass of second-ary refrigerant suction pipe [kg]")+((if('Copper'="pipeline suction mate-rial"."name") then ("GWP copper [kg CO2 eq/kg]") elseif('Plastic'="pipeline "Number of Vertical Display Cabinet [#]"+"Number of horizontal Display Cabi-nets [#]"+"Number of Cold Storage units [#]"+(if('Transcritical Horizontal dis-play cabinet'="Transcritical storage"."name") then "Number of Transcritical Horizontal Display cabinet [#]" elseif('Transcritical Vertical display cabi-net'="Transcritical storage"."name") then "Number of Transcritical Vertical dis-play cabinets [#]" else "Number of Transcritical CS units [#]") "cooling capacity of Heat exchanger in secondary loop required by the evapo-rator [kW]"/("secondary refrigerant"."specific heat of secondary refrigerant [kJ/kg.°C]"*"pinch_c [°C]") "mass of primary refrigerant pipe in heat exchanger [kg]"+"mass of secondary refrigerant pipe in heat exchanger [kg]" if("Heat Exchanger"."nb of plates [#]"<>0) then (("Area of heat transfer re-"Pipeline Suction"."pipe diameter [in]" sqrt("Supermarket"."Area of roof available [m^2]") "Supermarket"."Distance between machine room and CS [m]" pi*(("Pipeline Suction"."pipe diameter [m]"/2)^2-(("Pipeline Suction"."pipe di-ameter [m]"/2)-("pipe thickness [mm]"*"Conversion from mm to m "secondary refrigerant"."Technology Readiness Level [#]" [m/mm]"))^2)*"length of suction pipe [m]" ("Length of total pipe in siphons [m]"+"Length of pipe running vertical to the "mass of secondary refrigerant in pipe [kg]"+"mass of secondary refrigerant in heat exchanger [kg]"+"mass of secondary refrigerant in evaporator [kg]" roof [m]"+"Length of pipe in supermarket roof [m]")*"number of pipes [#]"+"Length of pipe inside the supermarket [m]" 100 suction material"."name") then ("GWP plastic [kg CO2 eq/kg]") else ("GWP steel [kg CO2 eq/kg]"))*"mass of secondary refrigerant suction pipe [kg]") "Emsys_man [kg CO2 eq]"+"Emref_man [kg CO2 eq]"+"Emsys_EOL [kg CO2 eq]"+"Emelec [kg CO2 eq]" "ref_leak_primary_BOL [%]"+(("ref_leak_primary_EOL [%]"-"ref_leak_pri-mary_BOL [%]")/10)*"System life time [yrs]" "ref_leak_secondary_BOL [%]"+(("ref_leak_secondary_EOL [%]"-"ref_leak_sec-ondary_BOL [%]")/10)*"System life time [yrs]" "ref_leak_plugin_BOL [%]"+(("ref_leak_plugin_EOL [%]"-"ref_leak_plugin_BOL [%]")/10)*"System life time [yrs]" "Total CO2 emissions of refrigeration system for entire lifetime [kg CO2 eq]"*"Conversion from kg to Ton [Ton/kg]" "Indirect CO2 emissions [kg CO2 eq]"+"Direct CO2 emissions due to refrigerant leakage [kg CO2 eq]" "Leak_ref [% of mass of refrigerant]"/"conversion of % to %/100 [#]"/"System life time [yrs]" "Evaporator"."Technology Readiness Level [#]" "Number of Vertical Display Cabinet [#]"+"Number of horizontal Display Cabi-nets [#]"+"Number of Cold Storage units [#]" "Expansion Valve"."Technology Readiness Level [#]" "Number of evaporators [#]" "cooling capacity of Heat exchanger in primary loop required by the evapora-tor [kW]"/("overall heat transfer coefficient for Heat exchanger [kJ/kg.°C]"*"pinch_h [°C]") [m^2]"/(2*pi*"Heat Exchanger"."Diameter of heat exchanger [m]"/2)) else ("Heat Exchanger"."nb of plates [#]"*("Heat Exchanger"."Separation distance between plate [m]"+"Heat Exchanger"."Width of each plate [m]"+2*"heat ex-changer plate/pipe thickness [m]")) "Frigorific power for all CS and DC [kW]" "Frigorific power for all CS and DC [kW]" if("Heat Exchanger"."e [m]"<>0) then ("Heat Exchanger"."e [m]") else (("Pipe-line Discharge"."pipe diameter [m]"-"heat exchanger plate/pipe thickness [m]")*(ln("Pipeline Discharge"."pipe diameter [m]")-ln("Pipeline Dis-charge"."pipe diameter [m]"-"heat exchanger plate/pipe thickness [m]"))) terial [kg/m^3]" ("Pipeline Discharge"."pipe diameter [m]"/("Refrigerant"."h_refrigerant [kW/m^2.K]"*("Pipeline Discharge"."pipe diameter [m]"+"heat exchanger plate/pipe thickness [m]")))+("e (thickness of heat exchanger) [m]"/"conductiv-ity of HE material [kW/m.K]")+(1/"secondary refrigerant"."h_secondary refrig-erant [kW/m^2.K]") if("Heat Exchanger"."nb of plates [#]"<>0) then (("Area of heat transfer re-quired [m^2]"/2)*"Heat Exchanger"."Width of each plate [m]") else (pi*("Pipe-line Discharge"."pipe diameter [m]"/2)^2*"Length of the heat exchanger [m]") "pipeline discharge material"."Technology Readiness Level [#]" [#]"*3)) to m [m/mm]"))^2)*"length of discharge pipe [m]" charge"."pipe diameter [m]"/2)-("pipe thickness [mm]"*"Conversion from mm pi*(("Pipeline Discharge"."pipe diameter [m]"/2)^2-(("Pipeline Dis-[#]")*"Mass of refrigerant in the refrigeration system [kg]" (1+"Leak_ref [% of mass of refrigerant]"/"conversion of % to %/100 "Pipeline Suction"."Technology Readiness Level [#]" [m3]"*"Number of evaporators [#]" "density of refrigerant in evaporator [kg/m^3]"*"Evaporator"."V_ref "Pipeline Discharge"."Technology Readiness Level [#]" "v_ref_pipeline_discharge [m^3]"+"v_ref_pipeline_suction [m^3]" floor("Length of pipe running vertical to the roof [m]"/("number of pipes version from mm to m [m/mm]"))^2*"length of suction pipe [m]" pi*(("Pipeline Suction"."pipe diameter [m]"/2)-("pipe thickness [mm]"*"Con-"pipeline suction material"."Technology Readiness Level [#]" version from mm to m [m/mm]"))^2*"length of discharge pipe [m]" pi*(("Pipeline Discharge"."pipe diameter [m]"/2)-("pipe thickness [mm]"*"Con-thickness [m]"))^2))*"Length of the heat exchanger [m]")))*"density of HE ma-if("Heat Exchanger"."nb of plates [#]"=0) then ("Area of heat transfer required Ex-changer"."Diameter of heat exchanger [m]"/2)-("heat exchanger plate/pipe "Mass of all discharge pipes [kg]"+"Mass of all suction pipe [kg]" 2 "Heat Exchanger"."Technology Readiness Level [#]" quired [m^2]"/2)*2*"heat exchanger plate/pipe thickness [m]")*"density of HE material [kg/m^3]" else (((pi*((("Pipeline Discharge"."pipe diameter [m]"/2)^2)-(("Pipeline Discharge"."pipe diameter [m]"/2)-("pipe thickness [mm]"*"Conversion from mm to m [m/mm]"))^2))*"Length of the heat ex-changer [m]"))*"pipeline discharge material"."Density of pipe material [kg/m^3]" Length of pipe running vertical to the roof [m] "Supermarket"."Height of supermarket [m]" Hourly Q_day_ver [kW] Hourly Q_dot_food for DC_hor [kW] Hourly Q_dot_food for Dc_ver [kW] Hourly Q_infiltration_day_hor [kW] Hourly Q_infiltration_day_ver [kW] Hourly Q_infiltration_night_hor [kW] Hourly Q_infiltration_night_ver [kW] Hourly Q_night_hor [kW] Hourly Q_night_ver [kW] Hourly Q_radiation_day_hor [kW] Hourly Q_radiation_day_ver[kW] Hourly Q_wall_hor [kW] Hourly Q_wall_ver [kW] Yearly energy consumption of vertical DC in closed time [kWh] Yearly energy consumption of vertical DC in closed time [kWh] Environment impact Emsys_EOL [kg CO2 eq] DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion DC Energy consump-tion Environment impact Emsys_EOL [kg CO2 eq] Environment impact Emsys_EOL [kg CO2 eq] Environment impact Emsys_EOL [kg CO2 eq] Environment impact Emsys_man_Heat exchanger [kg CO2 eq] Environment impact Emsys_man_circulation pump [kg CO2 eq] Environment impact Emsys_man_condenser [kg CO2 eq] Environment impact Emsys_man_evaporator [kg CO2 eq] Environment impact Emsys_man_secondary refrigerant pipeline [kg CO2 eq] Evaporator characte-ristics Number of evaporators [#] Heat exchanger cha-racteristics mass flow of secondary refrigerant [kg/s] Heat exchanger cha-racteristics mass of entire heat exchanger [kg] Primary pipe charac-teristics Diameter of the suction pipeline [in] Primary pipe charac-teristics Length of pipe in supermarket roof [m] Primary pipe charac-teristics Length of pipe inside the supermarket [m] Primary pipe charac-Volume of material in suction pipe [m^3] Refrigerant characte-Secondary refrigerant TRL ristics teristics Refrigerant characte-ristics Total mass of secondary refrigerant Primary pipe charac-teristics length of discharge pipe [m] Refrigerant characte-conversion of % to %/100 [#] ristics Environment impact Indirect CO2 emissions [kg CO2 eq] Environment impact Leak_ref [% of mass of refrigerant] Environment impact Leak_ref [% of mass of refrigerant] Environment impact Leak_ref [% of mass of refrigerant] Environment impact Total CO2 emissions of refrigeration system for entire lifetime [Ton CO2 eq] Environment impact Total CO2 emissions of refrigeration system for entire lifetime [kg CO2 eq] Environment impact leak_ref_year [%] Evaporator characte-ristics Evaporator TRL Evaporator characte-ristics Number of evaporators [#] Expansion valve cha-racteristics Expansion valve TRL Expansion valve cha-racteristics Number of expansion valves [#] Heat exchanger cha-racteristics Area of heat transfer required [m^2] Heat exchanger cha-racteristics Heat exchanger TRL Heat exchanger cha-racteristics Length of the heat exchanger [m] Heat exchanger cha-racteristics cooling capacity of Heat exchanger in primary loop required by the evaporator [kW] Heat exchanger cha-racteristics cooling capacity of Heat exchanger in second-ary loop required by the evaporator [kW] Heat exchanger cha-racteristics e (thickness of heat exchanger) [m] Heat exchanger cha-racteristics overall heat transfer coefficient for Heat ex-changer [kJ/kg.°C] Heat exchanger cha-racteristics volume of primary refrigerant in primary re-frigerant pipe heat exchanger [m^3] Primary pipe charac-teristics Material of discharge pipeline TRL teristics Material of suction pipeline TRL Primary pipe charac-teristics Primary pipe charac-teristics Pipeline discharge TRL Primary pipe charac-teristics Pipeline suction TRL teristics Volume of material in discharge pipe [m^3] Primary pipe charac-Refrigerant characte-ristics Mass of refrigerant in the overall lifecycle [kg] Refrigerant characte-ristics Mass of refrigerant in all evaporators [kg] teristics v_ref_pipeline_total [m^3] Primary pipe charac-Number of siphons required [#] Primary pipe charac-teristics v_ref_pipeline_suction [m^3] Primary pipe charac-Primary pipe charac-teristics v_ref_pipeline_discharge [m^3] Heat exchanger cha-racteristics mass of secondary refrigerant pipe in heat ex-changer [kg] Primary pipe charac-teristics Mass of entire piping (discharge +suction) for the refrigeration system teristics number of pipes [#] Primary pipe charac-Heat exchanger cha-racteristics mass of primary refrigerant pipe in heat ex-changer [kg] Primary pipe charac-teristics Primary pipe charac-teristics Length of total pipe in siphons [m] ("Number of siphons required [#]"*"Pipeline Discharge"."Siphon length [m]") Primary pipe charac-teristics Mass of all discharge pipes [kg] "Volume of material in discharge pipe [m^3]"*"pipeline discharge mate-rial"."Density of pipe material [kg/m^3]" Primary pipe charac-teristics Mass of all suction pipe [kg] "Volume of material in suction pipe [m^3]"*"pipeline suction material"."Den-sity of pipe material [kg/m^3]" Primary pipe charac-teristics length of discharge pipe [m] 0.5 Primary pipe charac-teristics length of suction pipe [m] "Length of pipe inside the supermarket [m]" Primary pipe charac-teristics length of suction pipe [m] 0 Primary pipe charac-teristics length of suction pipe [m] 0.5 Refrigerant characte-ristics density of refrigerant in condenser [kg/m^3] ("Refrigerant characte-ristics density of refrigerant in evaporator [kg/m^3]

if('NO Vertical Display Cabinet'="Vertivcal DC"."name") then 0 else "Stored food in cold storage"."mass flow [kg/m^3]"/"Stored food in cold storage"."rho_pr [kg/m^3]"*("Stored food in cold storage"."Delta_H [kJ/kg]"/"Conversion from kW to kJ [kW/kJ]")*"Mass of food inside each DC vertical [kg]"*(1/"no of hours in a day [hrs/day]") "Vertivcal DC"."hro [W/m²K]"*"conversion factor W to kW [kW/W]"*"Vertivcal DC"."A_door [m^2]"*("Vertivcal DC"."T_wall [°C]"-"Setpoint temperature"."setpoint temperature [°C]")*"Vertivcal DC"."phi_1 [#]"*Energy_consumption_DC("Hourly Q_night_ver [kW]","pinch_h [°C]","eta [#]","T_c [°C]","No of days used in a month (31) [days]","No of days used in a month (28) [days]","No of days used in a month (30) [days]","Supermarket's Closed time [hrs]","time for each defrost [hrs]","number of defrosts (if("Heat Exchanger"."nb of plates [#]"<>0) then (("Area of heat transfer required [m^2]"/2)*2*"heat exchanger plate/pipe thickness [m]") else (((pi*(("Heat Exchanger"."Diameter of heat exchanger [m]"/2)^2-(("Heat

  Number of transcritical compressors [#]"*"Transcritical compressor"."Mass [kg]"*(("Transcritical compressor"."Steel in component [%]"*"GWP steel [kg CO2 eq/kg]")+("Transcritical compressor"."Copper in component [%]"*"GWP copper [kg CO2 eq/kg]")+("Transcritical compressor"."Aluminium in component [%]"*"GWP aluminium [kg CO2 eq/kg]")+("Transcritical compressor"."Lubricating oil in component [%]"*"GWP lubricating oil [kg CO2 eq/kg]")+("Transcritical compressor"."Casting/Forging [%]"*"GWP Casting/Forging [kg CO2 eq/kg]")+("Transcritical compressor"."Termoforming/Injection moulding [%]"*"GWP Termoforming/Injection moulding [kg CO2 eq/kg]")+("Transcritical compressor"."Cut/bending/deep drawing [%]"*"GWP Cut/bending/deep drawing [kg CO2 eq/kg]")+("Transcritical compressor"."Wire drawing/extrusion/lamination [%]"*"GWP Wire drawing/extrusion/lamination [kg CO2 eq/kg]")+("Transcritical compressor"."Welding/soldering [%]"*"GWP Welding/soldering [kg CO2 eq/kg]")+("Transcritical compressor"."Powder coating/Zinc coating/cataphoresis/anodizing [%]"*"GWP Powder coating/Zinc coating/cataphoresis/anodizing [kg CO2 eq/kg]")+("Transcritical compressor"."Other machining [%]"*"GWP other machining [kg CO2 eq/kg]")) Transcritical Mass of all TC discharge pipes [kg] "Volume of material in TC discharge pipe [m^3]"*"pipeline discharge material"."Density of pipe material [kg/m^3]" Transcritical Mass of all TC suction pipe [kg] "Volume of material in TC suction pipe [m^3]"*"pipeline suction material"."Density of pipe material [kg/m^3]"Transcritical Mass of entire TC piping (discharge +suction) for the refrigeration system "Mass of all TC discharge pipes [kg]"+"Mass of all TC suction pipe [kg]" Transcritical Number of transcritical compressors [#] ceil("TC_Frigorific power for all CS and DC [kW]"/"Transcritical compressor"."Cooling capacity [kW]") TC_Hourly Q_dot_ColdStorage [kW]","pinch_h [°C]","eta [#]","TC_T_c [°C]","Location_data"."01 Jan_Min_temp [°C]","Loca-tion_data"."02 Feb_Min_temp [°C]","Location_data"."03 Mar_Min_temp [°C]","Location_data"."04 April_Min_temp [°C]","Location_data"."05 May_Min_temp [°C]","Location_data"."06 Jun_Min_temp [°C]","Loca-tion_data"."07 Jul_Min_temp [°C]","Location_data"."08 Aug_Min_temp [°C]","Location_data"."09 Sep_Min_temp [°C]","Location_data"."10 Oct_Min_temp [°C]","Location_data"."11 Nov_Min_temp [°C]","Loca-tion_data"."12 Dec_Min_temp [°C]")

	TC_Frigorific power for CS [kW] "Transcritical "TC_Frigorific power for CS during closed time [kW]"+"TC_Frigorific power for one CS during working time [kW]" frigorific_power("
	Transcritical	TC_Frigorific power for CS during closed time [kW]
	Transcritical	Emsys_man_Transcritical compressor [kg CO2 eq]

  "*(if('Transcritical Horizontal display cabinet'="Transcritical storage"."name") then "Number of Transcritical Horizontal Display cabinet [#]" else "Number of Transcritical Vertical display cabinets [#]") else 0TranscriticalTC_Frigorific power for one DC during working time[kW] frigorific_power("TC_Hourly Q_day[kW]","pinch_h [°C]","eta [#]","TC_T_c [°C]","Location_data"."01Jan_Max_temp [°C]","Location_data"."02 Feb_Max_temp [°C]","Location_data"."03 Mar_Max_temp [°C]","Loca-tion_data"."04 April_Max_temp [°C]","Location_data"."05 May_Max_temp [°C]","Location_data"."06 Jun_Max_temp [°C]","Location_data"."07 Jul_Max_temp [°C]","Location_data"."08 Aug_Max_temp [°C]","Loca-tion_data"."09 Sep_Max_temp [°C]","Location_data"."10 Oct_Max_temp [°C]","Location_data"."11 Nov_Max_temp [°C]","Location_data"."12 Dec_Max_temp [°C]") Transcritical TC_Hourly Q_day [kW] "TC_Hourly Q_wall [kW]"+"Transcritical storage"."Q_hw [w]"*"conversion factor W to kW [kW/W]"+"Transcritical storage"."Q_lighting [W]"*"conversion factor W to kW [kW/W]"+"TC_Hourly Q_infiltration_day [kW]"+"TC_Hourly Q_radiation_day [kW]"+"Transcritical storage"."Q_fan [W]"*"conversion factor W to kW [kW/W]"+(if('Transcritical Horizontal display cabinet'="Transcritical storage"."name") then "Hourly Q_dot_food for DC_hor [kW]" else "TC_Hourly Q_dot_food for Dc_ver [kW]")

	Transcritical	TC_Hourly Q_dot_Airex [kW]	"TC_V_airex [m^3/hrs]"*"TC_delta_h x phi [Wh/m^3]"/"Conversion from kW to W [W/kW]"
	Transcritical	TC_Hourly Q_dot_ColdStorage [kW]

  Afin de répondre à notre première question de recherche, nous avons cherché à diagnostiquer le système socio-technique de la réfrigération en représentant et évaluant l'état de l'existant. La recherche vise à développer des innovations pour répondre à la réglementation croissante et aux impacts environnementaux critiques des systèmes de réfrigération. Un système de réfrigération sociotechnique est un système complexe faisant intervenir de multiples disciplines, environnements et parties prenantes tout au long de son cycle de vie (utilisation, maintenance, mise à jour technique et fin de vie). Sa complexité peut soulever des problèmes et provoquer des malentendus lors des premières étapes de la conception. Cela peut conduire à des performances sous-optimales. La méthodologie Radical Innovation Design (RID) est bien adaptée à l'identification des principaux domaines d'amélioration des activités des systèmes complexes. À cette fin, des données provenant d'entretiens et de pratiques existantes fournies par différentes entreprises ayant une expérience et des connaissances variées ont été recueillies pour alimenter la méthodologie RID. Notre champ d'investigation est la réfrigération dans le secteur de la vente au détail. Deux séries de résultats sont présentées répondant aux deux objectifs de recherche (1.1 et 1.2) Premièrement, un livre de connaissances du système socio-technique est décrit. La Figure 7présente la méthodologie suivie pour construire le livre de connaissance et classer les données collectées. Une quinzaine d'acteurs du domaine de la réfrigération en France ont été interviewés. Les personnes interrogées ont été choisies pour être représentatives des acteurs impliqués dans le cycle de vie des systèmes de réfrigération. Les données collectées sont divisées en quatre catégories correspondant aux quatre dimensions ontologiques de RID (situations d'usage, profils des utilisateurs, problèmes actuels et solutions existantes) : -Trois situations d'usages sont identifiées : Conception, fabrication et installation ; Exploitation ; Fin de vie. Six catégories de solutions existantes sont présentées : Solutions basées sur les logiciels ; Solutions basées sur la gestion des connaissances ; Solutions méthodologiques ; Solutions techniques ; Solutions outils ; Directives EN 378.

	2.1.	Objectif 1.1 : Livre de connaissance

-Douze profils d'utilisateurs sont identifiés : Chercheur académique ; Centre de transfert technologique ; Organisme professionnel ; Chercheur en R&D ; Bureau d'études ; Cabinet de conseil ; Ingénieur frigoriste ; Fournisseur ; Concepteur de composants ; Fabricant ; Utilisateur final ; Parties de traitement de la fin de vie -Quinze problèmes sont mis en avant : Risque d'obsolescence précoce; Risque de coûts d'investissement imprévus ; Risque de panne; Risque de coûts opérationnels élevés (OPEX); Diminution de la qualité du recrutement; Manque de techniciens et d'ingénieurs en réfrigération; Manque de compréhension des nouvelles grappes technologiques; Perturbation du processus de conseil à l'utilisation finale du système; Manque de flexibilité pour les installateurs; Risque d'inconfort pour les clients de l'utilisateur final; Risque de danger élevé pour la santé humaine; Mauvaise exploration de l'espace de conception; Manque d'outils communs efficaces; Risque d'impacts environnementaux élevés; Faible taux de recyclage -

List of tables

General introduction

(June): 100716. https://doi.org/10.1016/j.seta.2020.100716. Law, Jonathan. 2009. Socio-Technical System. Oxford University Press.

Socio-technical diagnosis of refrigeration systems in France

Validation of the knowledge model by scenarios and by experts

General conclusion

Controller for Transcritical CO2 Refrigeration Cycle with an Adjustable Ejector'. Energy Conversion and Management 142: 466-76. He, Yang, Jianqiang Deng, and Z Zhang. 2014. Thermodynamic Study on a New Transcritical CO2 Ejector Expansion Refrigeration System with Two-Stage Evaporation and Vapor Feedback. Vol. 20. https://doi.org/10.1080/10789669.2014.929422. Horton, William Travis. 2004. 'Modeling of Secondary Loop Refrigeration Systems in Supermarket Applications.' IEA. 2020. 'Cooling Emissions and Policy Synthesis Report'. Paris. https://www.iea.org/reports/cooling-emissions-and-policy-synthesis-report. Imamura, Tomohiko, Kyoko Kamiya, and Osami Sugawa. 2015. 'Ignition Hazard Evaluation on A2L Refrigerants in Situations of Service and Maintenance'. Journal of Loss Prevention in the Process Industries 36 (July): 553-61. https://doi.org/10.1016/j.jlp.2014.12.018. IPU. n.d. PackCalculationPro. https://www.ipu.dk/products/pack-calculation-pro/. Islam, Md. Amirul, K. Srinivasan, Kyaw Thu, and Bidyut Baran Saha. 2017. 'Assessment of Total Equivalent Warming Impact (TEWI) of Supermarket Refrigeration Systems'. International Journal of Hydrogen Energy 42 (43): 26973-83. https://doi.org/10.1016/j.ijhydene.2017.07.035. ISO 14044. 2006. 'Environmental Management --Life Cycle Assessment --Requirements and Guidelines'. ISO 14062. 2002. 'Management Environnemental --Intégration Des Aspects Environnementaux Dans La Conception et Le Développement de Produit'. Jakobsen, Arne, Bjarne D. Rasmussen, and Simon Engedal Andersen. 1999. 'CoolPack-Simulation Tools for Refrigeration Systems'. Scan Ref 28 (4): 7-10. Jiang, J., M. Shiu, and M. Tu. 2007. 'DFX and DFSS: How QFD Integrates Them'. Quality Progress 40 (10): 45. Kang, Eunsuk, Ethan Jackson, and Wolfram Schulte. 2010. 'An Approach for Effective Design Space Exploration'. In Monterey Workshop, 33-54. Springer. Kazachki, Georgi S., and David K. Hinde. 2006. 'Secondary Coolant Systems for Supermarkets'. ASHRAE Journal 48 (9): 34-47. Korhonen, Pekka, Herbert Moskowitz, and Jyrki Wallenius. 1992. 'Multiple Criteria Decision Support -A Review'. European Journal of Operational Research 63 (3): 361-75. https://doi.org/10.1016/0377-2217(92)90155-3. Kosky, Philip, Robert Balmer, William Keat, and George Wise. 2021. 'Chapter 11 -Industrial Engineering'. In Exploring Engineering (Fifth Edition), edited by Philip Kosky, Robert Balmer, William Keat, and George Wise, 229-57. Academic Press. https://doi.org/10.1016/B978-0-12-815073-3.00011-9. Kumar Singh, Kaushalendra, Rajesh Kumar, and Anjana Gupta. 2020. 'Comparative Energy, Exergy and Economic Analysis of a Cascade Refrigeration System Incorporated with Flash Tank (HTC) and a Flash Intercooler with Indirect Subcooler (LTC) Using Natural Refrigerant Couples'. Sustainable Energy Technologies and Assessments

7 discusses the validity of the design exploration platform with several criteria.

Chapter 8 provides a general discussion, limitations and perspectives for future works. A general conclusion ends this dissertation.

Environmental impacts simulation

The environmental impact in this example consists of the calculation of the Life Cycle Climate Performance (LCCP) that characterises the global warming potential of a frigorific installation through the whole lifecycle of the system. 𝐿𝐶𝐶𝑃 = 𝐷𝑖𝑟𝑒𝑐𝑡 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 + 𝐼𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 + 𝐸𝑚𝑏𝑜𝑑𝑖𝑒𝑑 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 The direct emissions are the emissions related to the refrigerant leakage occurring during the exploitation and the end-of-life (EOL) treatment phases, respectively Eq ( 6) and [START_REF] Sun | Experimental investigation on twin screw refrigeration compressor with different capacity control methods[END_REF], where 𝑚 𝑟𝑒𝑓 is the mass of refrigerant in the whole system, 𝑛 is the system lifetime, 𝑙𝑒𝑎𝑘 𝑟𝑒𝑓 𝑦𝑒𝑎𝑟 is the percentage of leakage depending on the system architecture, the components' age and the life cycle phase, 𝐶𝑂 2 𝑒𝑞_𝑟𝑒𝑓 is the global warming potential (GWP) of the refrigerant.

𝐸𝑚 𝑟𝑒𝑓𝑙𝑒𝑎𝑘 = 𝑚 𝑟𝑒𝑓 * 𝑛 * 𝑙𝑒𝑎𝑘 𝑟𝑒𝑓 𝑦𝑒𝑎𝑟 * 𝐶𝑂 2𝑒𝑞_𝑟𝑒𝑓 [START_REF] Yannou | Capturing the relevant problems leading to pain-and usage-driven innovations: The Dependency Structure Modeling Value Bucket algorithm[END_REF][START_REF] Keppler | Absorption chillers as a contribution to a climate-friendly refrigeration supply regime: Factors of influence on their further diffusion[END_REF] 𝐸𝑚 𝑟𝑒𝑓𝑙𝑒𝑎𝑘,𝐸𝑂𝐿 = 𝑚 𝑟𝑒𝑓 * 𝑙𝑒𝑎𝑘 𝑟𝑒𝑓 𝐸𝑂𝐿 * 𝐶𝑂 2𝑒𝑞_𝑟𝑒𝑓 [START_REF] Yannou | Capturing the relevant problems leading to pain-and usage-driven innovations: The Dependency Structure Modeling Value Bucket algorithm[END_REF][START_REF] Keppler | Absorption chillers as a contribution to a climate-friendly refrigeration supply regime: Factors of influence on their further diffusion[END_REF][START_REF] Sun | Experimental investigation on twin screw refrigeration compressor with different capacity control methods[END_REF] The embodied and indirect emissions are the emissions related to the electric consumption of the system during the whole life cycle: manufacture, exploitation and EOL treatment, respectively shown in Eq [START_REF] Ben-Abdallah | Experimental investigation of the use of PCM in an open display cabinet for energy management purposes[END_REF] and [START_REF] Gullo | Energy and environmental performance assessment of R744 booster supermarket refrigeration systems operating in warm climates[END_REF], Eq [START_REF] Delventura | Secondary loop systems for the supermarket industry[END_REF], and [START_REF] Kolokotroni | Supermarket Energy Use in the UK[END_REF], where 𝑚 𝑚𝑎𝑡 is the mass of material substance in each technical components, except the refrigerant, 𝑅 is the percentage of refrigerant recycled at the EOL, 𝐸 𝑟𝑒𝑐𝑦𝑐𝑙_𝑚𝑎𝑡 is the electric consumption for recycling the components, ∑ 𝐸 is the yearly energy consumption of the whole refrigeration system, 𝐶𝑂 2𝑒𝑞_𝑋 is the GWP for the components manufacture or EOL treatment, for the refrigerant manufacture, for the production of 1kWh of electricity, depending on the energy mix of the country.

𝐸𝑚 𝑠𝑦𝑠,𝑚𝑎𝑛 = ∑ 𝑚 𝑚𝑎𝑡 * 𝐶𝑂 2𝑒𝑞_𝑚𝑎𝑛𝑢𝑓 [START_REF] Yannou | Capturing the relevant problems leading to pain-and usage-driven innovations: The Dependency Structure Modeling Value Bucket algorithm[END_REF][START_REF] Keppler | Absorption chillers as a contribution to a climate-friendly refrigeration supply regime: Factors of influence on their further diffusion[END_REF][START_REF] Sun | Experimental investigation on twin screw refrigeration compressor with different capacity control methods[END_REF][START_REF] Ben-Abdallah | Experimental investigation of the use of PCM in an open display cabinet for energy management purposes[END_REF] 𝐸𝑚 𝑟𝑒𝑓,𝑚𝑎𝑛 = 𝑚 𝑟𝑒𝑓 * (1 + 𝑛 * 𝑙𝑒𝑎𝑘 𝑟𝑒𝑓 𝑦𝑒𝑎𝑟 -𝑅) 𝐶𝑂 2𝑒𝑞_𝑟𝑒𝑓 [START_REF] Yannou | Capturing the relevant problems leading to pain-and usage-driven innovations: The Dependency Structure Modeling Value Bucket algorithm[END_REF][START_REF] Keppler | Absorption chillers as a contribution to a climate-friendly refrigeration supply regime: Factors of influence on their further diffusion[END_REF][START_REF] Sun | Experimental investigation on twin screw refrigeration compressor with different capacity control methods[END_REF][START_REF] Ben-Abdallah | Experimental investigation of the use of PCM in an open display cabinet for energy management purposes[END_REF][START_REF] Gullo | Energy and environmental performance assessment of R744 booster supermarket refrigeration systems operating in warm climates[END_REF] 𝐸𝑚 𝑠𝑦𝑠,𝐸𝑂𝐿 = ∑ 𝐸 𝑟𝑒𝑐𝑦𝑐𝑙_𝑚𝑎𝑡 * 𝑚 𝑚𝑎𝑡 * 𝐶𝑂 2𝑒𝑞_𝑟𝑒𝑐𝑦𝑐𝑙𝑖𝑛𝑔 [START_REF] Yannou | Capturing the relevant problems leading to pain-and usage-driven innovations: The Dependency Structure Modeling Value Bucket algorithm[END_REF][START_REF] Keppler | Absorption chillers as a contribution to a climate-friendly refrigeration supply regime: Factors of influence on their further diffusion[END_REF][START_REF] Sun | Experimental investigation on twin screw refrigeration compressor with different capacity control methods[END_REF][START_REF] Ben-Abdallah | Experimental investigation of the use of PCM in an open display cabinet for energy management purposes[END_REF][START_REF] Gullo | Energy and environmental performance assessment of R744 booster supermarket refrigeration systems operating in warm climates[END_REF][START_REF] Delventura | Secondary loop systems for the supermarket industry[END_REF] 𝐸𝑚 𝑠𝑦𝑠,𝑒𝑥𝑝𝑙𝑜𝑖𝑡 = 𝑛 * ∑ 𝐸 * 𝐶𝑂 2𝑒𝑞_𝑒𝑙𝑒𝑐 [START_REF] Yannou | Capturing the relevant problems leading to pain-and usage-driven innovations: The Dependency Structure Modeling Value Bucket algorithm[END_REF][START_REF] Keppler | Absorption chillers as a contribution to a climate-friendly refrigeration supply regime: Factors of influence on their further diffusion[END_REF][START_REF] Sun | Experimental investigation on twin screw refrigeration compressor with different capacity control methods[END_REF][START_REF] Ben-Abdallah | Experimental investigation of the use of PCM in an open display cabinet for energy management purposes[END_REF][START_REF] Gullo | Energy and environmental performance assessment of R744 booster supermarket refrigeration systems operating in warm climates[END_REF][START_REF] Delventura | Secondary loop systems for the supermarket industry[END_REF][START_REF] Kolokotroni | Supermarket Energy Use in the UK[END_REF] To obtain the different variables of the equations, i.e., 𝑚 𝑟𝑒𝑓 , 𝑙𝑒𝑎𝑘 𝑟𝑒𝑓 𝑦𝑒𝑎𝑟 , 𝑚 𝑚𝑎𝑡 , ... we need to first know the necessary frigorific power, depending on the system environment (type of conservation, opening and closing hours, and working hours, doors opening...), as shown in the energy consumption simulation section.

The properties necessary to calculate the environmental impacts of the refrigeration systems are the global warming potential of each material manufactured, the electricity..., that were found in EcoInvent database.

Table 5-6 shows how the problem modelled with the knowledge model is mapped to correspond to the elements of the equation. In state of the art, we have noted that design platforms do not take all the performances of a refrigeration system into account. Moreover, digital platforms do not allow designers to navigate the space of possibilities optimally because the user is not involved in the digital design process. On the contrary, in practice, the user generally poses a design problem and is provided with a single solution.

The artefacts developed in this work provide an approach for the design space exploration for commercial refrigeration systems. It could be used to implement multiple performances relevant to the design of refrigeration systems. One participant noted that: having such a tool to calculate several performances including CO2 emissions and energy consumption according to design choices is essential.

In the literature, this approach for refrigeration systems has not been studied, as shown in The platform has been tested with one use case. Some changes were tested by adding more or fewer display cabinets and cold rooms. The change of location, store area, were tested and the design Discussion, limitations, and perspectives

Discussion

Our research work aimed to improve the design process of refrigeration systems, with a focus on commercial refrigeration applications. Three research gaps were highlighted by a literature review, which led us to define two research questions.

Research question 1: How to represent and assess the refrigeration socio-technical system?

The first research question stems from the research gap on the lack of a holistic representation of the refrigeration socio-technical system (research gap 1).

We proposed a book of knowledge on the current refrigeration socio-technical system to address this issue. The first step was to conduct a diagnosis and knowledge investigation on the refrigeration socio-technical system. Different forms of data acquisition were implemented: interviews with interlocutors of different roles, companies, knowledge; studies of the laws, standards, and regulations specific to the field as well as the guides of good practices; a collection of literature data on refrigeration technologies, evaluation of the performances, observation.

From these data, and in order to represent the socio-technical system and assess the opportunities of development in the domain, the Radical Innovation Design (RID) methodology was used. The four RID objects allowed us to structure the knowledge in four categories: stakeholders (called user profiles), who encounter issues (called problems) during their activities, themselves segmented into life cycle phases (called use situations) and finally that of existing solutions that stakeholders can apply to reduce or eliminate a problem in a given activity. Based on the RID methodology, several categories were identified:

 Three life-cycle phases: design, manufacture and installation; exploitation and end-of-life.

 Twelve stakeholders.

 Fifteen problems classified into four categories: cost-related, knowledge management (KM)related, stakeholders' interaction-related and tool-related.

 Six categories of existing solutions: software solutions, KM solutions, EN 378 guidelines, methodological solutions, technological solutions, tool solutions.

The main shortcomings that emerge from this analysis are: (1) a lack of understanding of the technologies, (2) a weak exploration of the design, (3) language and tool gap between the stakeholders resulting once again in an underperforming refrigeration system.

Appendix 2: Modelling of the input matrices for the application of the RID methodology

The present thesis used RID methodology version 2. This RID version uses 6 input matrices (1D or 2D). These 6 matrices answer precise questions given in the Figure below.

The 4 matrices 𝑊𝑊, 𝑈𝑝𝐸𝑠, 𝑈𝑠𝐸𝑠, 𝐸𝑠𝑃 have a qualitative scale spanning from 0 to 5 with: 0= No/Never; 1=Very few; 2=Rarely; 3=Some/sometimes; 4=most/a lot; 5 =frequently.

WW matrix

Matrix 𝑊𝑊 is the matrix "What's Wrong in this activity?" and crosses Usage Situations with Problems.

In RID versions 1 and 2, all users have the same Problems when they are in a given Usage Situation.

The question asked to fill the matrix is: "How often this problem (of a given level / intensity, to define precisely) may occur during this usage situation?"

Let us recall below the definition of the 15 problems considered in our RID study.

Cost

Risk of early obsolescence

The evolution of regulation and the predicted laws and taxes has proven the need of a quick change in the systems to respond to the change in regulation. In

Tool

Cost

Interactions between stakeholders Knowledge management A few laboratories or companies do tests to measurement of the noise, vibration of components to reduce the discomfort of the machines.

AutoCAD is frequently used by design office to dimension the system and draw the structure during the design phase. Bitzer can help supplier to find components that meet the design office calculation of frigorific power need. It is a combination of solutions that are used by the design actors.

The software CoolTool is an example of a software that has been developed as part of a PhD project to calculate the energy consumption needed for operational conditions. It is unowned by most stakeholders and is used by very few users in France. "Accessibility design"+"Physical hurt preventing design"+"Ergonomic design"+"Error proofing design"+"Marking design" Maintenance Visibility design (sum(."Visibility design")+"Architecture -Visibility design")/6

Secondary pipe characteristics Volume of material in secondary refrigerant discharge pipe [m^3] pi*(("secondary refrigerant pipeline"."