Carlos Juiz
email: cjuiz@uib.es

Belen Bermejo

Alexander Nikolov

Silvia Rus

Andrea Carboni

Dario Russo

Davide Moroni

Efstathios Karanastasis

Vassiliki Andronikou

Christina Samuelsson

Frederic Lievens

Ad Van Berlo

Willeke Van Staalduinen

Maria Fernanda Cabrera-Umpierrez

Measuring the uptaking of digital health platforms on AAL/AHA domain

Keywords: Uptaking, Digital Health Platforms, AAL, AHA KPIs

This paper presents a method to determine the metrics to assess the uptake of Ambient Assisted Living (AAL) platforms. The different platforms are offering various resources to construct digital health products oriented to Active and Healthy Ageing (AHA) and social health care. This research work is addressed to identify and define which metrics could be Key Performance Indicators (KPIs) to be tracked for successful uptake, interoperability, synergies, and cost-benefit analysis of open platforms.

Introduction

Aging presents one of the greatest socio-economic challenges in the 21st century. Our research project aims at analyzing open service platforms in the AHA and AAL fields and the measurement of their performance. To do this, a set of KPIs needs to be defined to perform effective analysis of the platforms' success. As the platforms' success involves more than one factor for more than one stakeholder group to make it measurable in detail, a set of KPIs is defined and analyzed according to a specific methodology. Key European domain-related platforms are relevant, such as universAAL IoT [START_REF]UNIVERsal Open Platform and Reference Specification for Ambient Assisted Living | UniversAAL Project | FP7 | CORDIS | European Commission[END_REF] and FIWARE [START_REF]The Open Source Platform for Our Smart Digital Future[END_REF]. Besides the two mentioned platforms, a considerable number of platforms have been created and are operational on similar services within the similar domain, either competing or complementing each other, which would urge for more in-teroperability among them. Responding to the numerous critical voices, lack of interoperability of the various solutions deployed, or difficulties with the large-scale uptake of the platforms by their targeted users, the main objective of this research is to determine the platforms' uptake by their user communities as well as their continuous improvement and possibility of market expansion. Consequently, the research question of this work attempt to answer is how this platform uptake can be measured and quantified? The response to this question should serve to measure the uptake in current and future AAL platforms.

State of the Art

Problem Statement

A Key Performance Indicator (KPI) is a measurable value that represents the possibility of a process or action of a company or organization, to achieve a specific objective (mainly strategic).While a metric measures a raw value on a process, a KPI is directly associated with a goal set by the organization and allows to assess whether the strategies followed to achieve it are working. Since this work aims to analyze the uptake of the existing platforms in the AHA/AAL field, a set of KPIs needs to be defined to perform an effective analysis of the platforms' success.

Fig. 1. Overview of problem statement.

Thus, it seems that the number of users of these platforms may partially define the platform uptake. However, considering the market strategy and the use of the platforms, a set of KPIs can be defined to determine the platforms' uptake (see Figure 1).

Methodology for the definition of potential KPIs

In our research, we propose different stakeholders types around any platform, such as: Primary end-users (the single individual intended as the main beneficiary of a service or set of services provided by the considered platform), End-user customer (the person or organization in direct contact with a primary end-user, such as formal and informal care persons, family members, care organizations, and their representatives), Technology providers (typically hired by customer end-users that follow and implement the entire life cycle of the applications), and Government (the public sector service organizers, social security systems, insurance companies). In this work, we propose a methodology to determine the platforms' uptake. This methodology is divided into four steps, and it aims to obtain a list of representative KPIs for platform uptake:

1. The first step regards the ecosystem observation by reviewing whether some platform is publicly showing a set of metrics and/or even KPIs of its uptake. This direct information should be crawled in the sites of the different platforms. This first step also includes indirect information coming from research papers related to this uptake. Thus, a literature review is necessary. In any case, if positive, recapture all these metrics or KPIs from reviews and jump to step 3. 2. In case of not having metrics or KPIs from step 1, proceed with different benchmarking procedures with other platform sectors as the software platforms or e-commerce platforms to determine potential metrics or KPIs. Any potential metric or KPIs should be translated to the AHA/AAL domain. Additionally, the authors as researchers in the domain add other potential metrics or KPIs not provided by benchmarking. 3. Make a proposal of KPIs (classified by stakeholder types and pointing out which are generic vs. AHA/AAL specialized). 4. Propose the potential list of KPIs and metrics to stakeholders through different instruments. Then, considering the platform's stakeholders' experience and intention, the list of uptake KPIs shall be obtained as a result of the application of the proposed methodology.

Platform review and literature review

After the review of the complete list of platforms above, we did not find any metric or KPI to assist with the platform uptake assessment. Thus, we proceeded with a partial systematic literature review. To determine the platforms' uptake, a revision of out-comes from the literature was performed for the two more popular platforms (one generic and one specialized), aiming to get information on potential KPIs that will nurture our analysis. Some keywords were identified to perform a Google Scholar search (i.e. "KPI", "Key performance indicator", "metric", "measure", "benchmarking", "AHA", "AAL" and "platform"). Then, these logical expressions (LE) were used for the search:  LE1: "FIWARE" AND (("KPI" OR "Key performance indicator" OR "metric" OR "measure" OR "benchmarking") AND ("AHA" OR "AAL" OR "platform"))  LE2: "UNIVERSAAL" AND (("KPI" OR "Key performance indicator" OR "metric" OR "measure" OR "benchmarking") AND ("AHA" OR "AAL" OR "platform")) Taking into account the whole metadata from Google Scholar and with any timeline restrictions, we obtained 983 results from LE1 and 293 from LE2. In a second stage, we obtained from the abstract information that the platform's performance is the main aim of the current literature. Therefore, we modified the LE1 and LE2 to go in-depth on the performance study, evaluating the modified logical expressions in the same previous conditions:  LE1.1: "FIWARE" AND (("KPI" OR "Key performance indicator" OR "metric" OR "measure" OR "benchmarking") AND ("AHA" OR "AAL" OR "platform") AND "performance")  LE2.1: "universAAL" AND (("KPI" OR "Key performance indicator" OR "metric" OR "measure" OR "benchmarking") AND ("AHA" OR "AAL" OR "platform") AND "performance") From LE1.1 we obtained 833 results and 205 from LE2.1. From this literature review and our knowledge, there is no paper about metrics of uptaking in AHA/AAL domains. Then we proceeded with reviewing the whole list of platforms. However, we were not able to establish any consequent and consistent list of metrics or KPIs for any platform publishing its uptake measurements as there is no research paper about this. To solve this problem, we proceed with step two of our methodology.

Benchmarking

As we denoted in the previous section, it is necessary to proceed with the benchmarking process to obtain a potential list of metrics and KPIs for uptake. We followed the benchmarking methodology proposed by Kaiser Associates [START_REF]Beating the competition: a practical guide to Benchmarking[END_REF]. The obtained list of metrics and KPI is not specialized in AAL/AHA domain, and we should adapt it to our domain.

Proposal of metrics and KPIs (results)

In the following, we present a set of KPIs from different stakeholders' perspectives to determine the degree of platform uptake. There are 12 KPIs that can be specialized for the digital health domain marked with (S).

KPIs for primary end-users

 Reaching user goals, needs, and preferences: The platform matches the user's goals, needs, and preferences, which may be specific to the context of use and are subject to change.  Accessibility: The platform is accessible for persons with disabilities and older persons is a prerequisite for personal user experience.  User-driven design: The platform has been developed with user involvement.  User empowerment: The platform significantly contributes to the individual user's empowerment, by supporting and training the individual user to better understand and express their current and future wishes, needs, and preferences.  Adaptability: The user should be able to adjust the look & feel of the platform and overwrite the system's choices and interface settings.  Non-stigmatization: The platform looks attractive and is meaningful to everyone. It does not exclude any sub-group of users.  Easy-to-use: The platform is easy-to-use.  Responsive: The platform adapts itself to the user's selected preferences, behavior, and devices.  Modular: It allows the user to extend the solution by adding additional modules, if needed.  Privacy and data governance: The platform provides information on data collection, access, usage, control, sharing, and benefit to the user.  Ethics compliance: The platform ensures that complies with de facto ethical issues such as the Declaration of Helsinki.  Autonomy: The level of autonomy of users reached using the platform.  Hospitalizations (S): The percentage of hospitalizations of end-users using the platform compared with current clinical practices without the usage of the platform.  Quality of Life: Quality of Life: impact on QoL of the platform services.  Learnability: The platform and its functions are learnable for both older persons, relatives, and staff  Interest and enjoyment: The platform is interesting and enjoyable to end-users; older adults, relatives, and staff.  Costs for informal care (S): The costs decrease/increase of informal care for end-user using the platform.  K-factor for attracting other end-users: used to describe the growth rate of platform users, the platform-developed apps, or membership of the platform.  Adherence-health+app/solution (S): The end-users are improving due to the app/solution developed based on platform contents.  Affordability-treatment+app/solution (S): Treatment became more affordable end-users because of the developed app/solution based on platform contents.  Efficiency-treatment+app/solution (S): Treatment is more efficient because the developed app/solution based on platform contents was used/solution.

 Effectiveness-treatment+app/solution (S): Treatment process is more effective because the developed app/solution based on platform contents was used (except for clinical effectiveness).  Empowerment-app/solution (S): The app/solution developed based on platform contents empowers the end-users and health professionals to know more about their conditions or perform their tasks better.  Safety-app/solution: The developed app/solution based on platform contents itself is safe or makes the treatment process safer.  Trustability-treatment+app/solution (S): The developed app/solution based on platform contents improves the trust of the end-user in the treatment.  Customer Success Stories Submitted: The amount of Customer Success Stories Submitted.

KPIs for secondary end-users

 Compliance/Adherence to standards: These would make the platform directly compatible with hardware (e.g., medical or other IoT devices), software (e.g., services or tools), or another kind of protocols (e.g. compliance to legal/ethics/security requirements via standards).  The wideness of adoption of the platform: Number of users of the platform.  Availability/Level of support: Number of available support channels.  Maintenance difficulty level: How easy or difficult is it to locate and fix problems with the platform.  Mean frequency of updates: How often are existing problems tackled.  Monitoring capabilities (or other capabilities of high importance for IoT): Performance issues can be quickly understood and resolved.  Compliance/ Adherence to standards: These would make the platform directly compatible with hardware (e.g., medical or other IoT devices), software (e.g., services or tools), or another kind of protocols (e.g. compliance to legal/ethics/security requirements via standards).  Minimal fixed cost: Initial cost for purchasing the required platform components.  Readability of platform documentation: How much time did it take to go through all platform documentation.  Platform deployment: How much time did it take to deploy the platform.  Platform configuration: How much time did it take to configure the different tools/components of the platform.  Platform support documentation: How good are the platform installation and configuration documentation.  Platform support services: How good are the provided support services (e.g. phone/email/chat support) for the platform.  Awareness (TOMA) (S): Top of mind: The first platform that comes to mind when a stakeholder is asked an unprompted question about AHA/AAL.  Acquisition (registration/membership…): registration on the platform.

 Revenue (gross receipts, support, contributions, etc., gains, gross income…):

Economic revenue of platform.  Average producer/developer/... lifespan: It is the average number of years that a service producer/developer continues to produce through the platform's components/items/...  Retention (active producers/developers/…): It is the percentage of active producers/developers from total (registered, known).  K-factor for attracting other producers/developers/registrations/donations/…: the K-factor can be used to describe the growth rate of platform users, the platform-developed apps, or membership of the platform.  Productivity: The number of solutions based on platform contents over time.  Robustness: The number of use-cases based on platform contents over time.  Certifications of apps/solutions: The number of Certifications of apps/solutions based on platform contents.

KPIs for platform developers

 Initial investment: The costs related to the setup of the platform (e.g. hardware, software royalties, installation, and configuration).  Cost per year: The costs to maintain the platform actively.  Cost per user: The mean costs for each user.  Reduction of home care costs (S): The decrease of hospitalization of end-user using the platform.

KPIs for customer end-users

 The efficiency of service providers (S): The service providers (including caregivers) find it easy to use the platform, and make fewer errors.  One Business Model (BM) per user group: The platform offers a BM for each of the user groups addressed.  Purchasing and usage expectations: The platform considers all stakeholders, including buyer, payer, user, prescriber, and service provider, and their expectations in the purchase and later also in the usage processes.  Affordability (S): The platform considers its affordability for each of the user groups (who may not be able to pay for it) and supports them in the application of some financial support.  Sustainability: The platform uses mainstream technologies as much as possible for economic sustainability and easy replacement and updates  Usability and acceptance: The usability and acceptance of the services offered by the platform by stakeholders.  Integration: The platform permits the integration of different devices and integrate / can be integrated with other systems.  Scalability: The platform can be adapted according to the number of services/users.  Solve real needs: The platform solves the real needs of end-users.

 Openness: The platform is openly accessible for everyone.  Churn rate (contributors/members/registrations/…): It is the proportion of subscribers/members/registered/ who leave a platform during a given period.  Engagement per visit (downloads): the visits to the website platforms and the number of downloads of items on the platform.  Engagement per visit (time spent): the time visiting the website platforms.  Net Promoter Score (NPS): Scoring of the platform from any stakeholder.  Literature rate: The number of papers or projects reporting usage.

Discussion, conclusion and open problems

The work started with an analysis of current literature with a focus on two widely-used reference platforms (i.e. FIWARE and universAAL), where we did not find any relevant KPI that serves to measure the uptake and success of these platforms. According to the authors' knowledge, there is a clear absence of KPIs that help to assess open service platforms aiming to support its success and uptake. This could mean that there is an apparent lack of strategy for platform uptake. KPIs have been analyzed, clustered, and prioritized according to four different perspectives defined by 4 groups of stakeholders. However, having a KPI does not mean that the stakeholder of this group must provide the necessary information to calculate such KPI. As shown on the measurement instrument and unit fields, in most of the cases, information to measure the KPIs will be provided by others (e.g. platform owners according to the characteristics of the platform or technical features, service providers, etc.). From this research outcome, we cluster the most important KPIs for the different stakeholders. The list of KPIs was developed with a holistic perspective to be applicable for any platform. Moreover, we will consider other commercial platforms different from European projects.

Acknowledgment

This research work was co-funded within the project PlatformUptake.EU from European Union's Horizon 2020 research and innovation program under the grant agreement No 875452.


Davide Moroni
email: davide.moroni@isti.cnr.it

Gabriele Pieri
email: gabriele.pieri@isti.cnr.it

Marco Tampucci
email: marco.tampucci@isti.cnr.it

Daniele Masini
email: daniele.masini@sysdat.it

ARTiCo -AR in Tissue Converting

Keywords: Augmented reality, mobile app, predictive maintenance, pervasive vision, smart cameras, tissue converting, industry 4.0

For fully up-taking the advances achieved in pervasive monitoring systems within Industry 4.0, novel intelligent interfaces are needed to ease interactions with human personnel and enable them to access the available data and services ergonomically. To this end, we present a mobile app based on augmented reality that allows operators to receive location-aware notifications and access and visualize real-time information and guidance for troubleshooting and plant maintenance. The app has been tailored to respond to the needs of a specific scenario, i.e. tissue converting, where it has provided encouraging results.

I. INTRODUCTION

Industry 4.0 is demanding new solutions to manage the multiple resources available in a production plant and integrate and orchestrate all the processes that should be performed. Besides the general production -which is often assumed to be the default mode-maintenance is of key importance since, in principle, it has to be performed so as not to interfere with production, keeping productivity at optimal levels and guaranteeing the expected quantities of delivered product [START_REF] Cigolini | Improving productivity of automated tissue converting lines: an empirical model and a case study[END_REF].

In this context, technological solutions which can improve these aspects are always sought. Considering the rapid advancements in Information Technologies, particularly those concerning Augmented Reality (AR), what we present in this paper is a prototype demonstrator developed to support the maintenance of industrial tissue production lines. The paper is structured as follows: in section II, the reference scenario for the factory under investigation is presented; the subsequent section III describes the technical implementation and novelties of the app; while the final section IV shows the results and outcomes of the application of the demonstrator into a working factory environment.

II. THE TISSUE CONVERTING SCENARIO

Tissue converting lines represent one of the vital plants in the paper production field. In such plants, paper tissue is converted from jumbo rolls, weighting tons into their final form for domestic and sanitary use (e.g. towels, napkins, toilet paper). The process is achieved through a line of machines that cover unwinding the jumbo rolls, pairing multiple plies of tissue, embossing and printing them and finally rewinding the product on logs of the desired radius to be cut into rolls of the requested length.

A highly efficient, strongly coordinated and robust process is key to making converting profitable. Therefore, the machines in the line must be orchestrated to work at a fixed speed, reaching 800 meters per minute while minimizing possible downtime. This goal is achieved through a mixture of advanced automation and Industrial Internet of Things (IoT) in modern converting lines. Several sensors are used for fine control and auto-regulation of each machine through a pervasive system. For instance, cameras might be used to control the quality of the tissue plies and identify defects that might lead to a paper jam. This way, faults can be predicted, and adaptive maintenance can be performed. Further, the information collected by each machine is gathered and shared with other devices. In such a way, a global orchestration of the processes on the converting lines is achieved. Notably, resulting in the definition of working speed (in terms of meters per second of tissue) and the optimization of cleaning and maintenance.

To be fully exploited such pervasive information requires the development of novel intelligent interfaces for the operators to enable them to monitor the line in real-time and be timely informed about events requiring their intervention [START_REF] Coscetti | Augmented reality for tissue converting maintenance[END_REF], [START_REF] Benassi | Augmented reality and intelligent systems in industry 4.0[END_REF].

For these reasons, tissue converting is a scenario where the integration of solutions for Industrial IoT, pervasive computing and intelligent user interfaces may have a great impact and pave the way to applications in other domains linked to the Factory of the Future. The prototype ARTiCo has been designed and developed to act as an AR useful tool during the tissue converting line inspections and during ordinary and extraordinary technical maintenance operations. ARTiCo exploits Wikitude framework [START_REF] Gmbh | Wikitude[END_REF] to recognize one or more target objects of the observed scene in order to understand the position of the operator. Thanks to this framework, the prototype is able to place inside the scene a set of AR content that operators use to assess the working conditions of the line and, in case of assistance is required, the prototype can display a set of documentation and step by step procedures that support the operator during intervention operation. Despite the Wikitude framework being a powerful and easy to use tool to recognize the scene and surrounding environment, it has some limitations that prevent its use in recognizing the entire tissue line due to the line size. Therefore, a set of Bluetooth beacons are placed all along the line to perform a coarse recognition of the examined portion of the line and retrieve the right feature-points cloud used to perform the precise position recognition via Wikitude.

The feature-points clouds are obtained by exploiting Wikitude Studio; Wikitude studio is able to generate a threedimensional point cloud from a set of images of the scene. Thus, the prototype has to pass through two phases of training. During the first phase, each portion of the tissue converting line is photographed, and the relatives feature-points clouds are obtained. The second phase is devoted to associating each point cloud to the proper beacon displaced along the tissue line.

Fig. 1 shows a simple diagram of a typical ARTiCo use case. Once the operator is within the range of a beacon, the app recognizes the explored portion of the line and queries the server in order to retrieve the feature-points cloud relative to the visited area. With this point cloud, the recognition engine is able to locate the scene target object used to understand the displayed portion and correctly displace visible AR content. In the meanwhile, another component of the prototype is in charge to retrieve, from the server, real-time data and multimedia content to be displayed. Indeed, the app can provide three different types of tag: (i) real-time data, (ii) multimedia content (e.g. video guides, component documentation, 3d schema), (iii) interactive tags (e.g. step by step guides for assistance intervention). Real-time data, that is gathered by the server from the Supervisory Control And Data Acquisition (SCADA) for the converting line, are displayed as AR tags in the scene, which are automatically updated at predetermined intervals of time; multimedia contents vary from documents that can be opened through an external link to images and video that are displayed and can be played directly on the scene; interactive tags are a set of tags that operators can use in order to perform dedicated actions, such as proceeding to the next step of an intervention guide, retrieving detailed information about a complex component (such as shown in Fig. 2) or analyzing the abnormal status of a component of the line.

IV. RESULTS

ARTiCo has been tested and demonstrated using two devices: a Xiaomi Mi 5S Plus smart-phone and a Samsung Galaxy Tab S5e tablet. The camera characteristics of the two devices are reported in Table 1. At the same time the portability over a smart-glass model (i.e. the Epson Moverio BT-350) is under development. The results, as also mentioned in [START_REF] Coscetti | Factory maintenance application using augmented reality[END_REF], are promising both on recognition of single targets and on complete scenes in simulated environments (Fig. 4) and in the real factory (Fig. 5). The analysis performed were based both on the number of images used for the point clouds generation and the efficiency, in terms of recognized target occurring within a predefined slot of time over the total amount of targets. The demonstrator results show a very promising trend in terms of usability and robustness. A refinement in the acquisition and reconstruction steps is under development as well as an extension of the AR content.

Fig. 1 .

 1 Fig. 1. Diagram of ARTiCo operational flow, position identification steps are depicted in blue

Fig. 2 .

 2 Fig. 2. An AR interactive tag relative to a complex component of the tissue converting line.

Fig. 3 .

 3 Fig. 3. ARTiCo interface in landscape mode after fine position is recognized. Displayed targets contain dynamic data gathered from the server

Fig 3

 3 shows an example of the interface, in detail, a set of real-time values gathered from the server are displayed; the warning tag reports an error code and indicates an abnormal status in the operation of the line.

Fig. 4 .

 4 Fig. 4. An example of the use of ARTiCo app in a simulated lab environment, with the item under examination highlighted in AR.

TABLE I CAMERA

 I CHARACTERISTICS OF THE TWO DEVICES.

	Device	Xiaomi	Samsung
		Mi5s Plus Galaxy Tab 5Se
	Resolution (MP)	13	13
	Aperture	f/2.2	f/2.0
	Focal Length (mm)	34.88	26
	Sensor format	1/4.3"	1/3.4"


Davide Moroni
email: davide.moroni@isti.cnr.it

Gabriele Pieri
email: gabriele.pieri@isti.cnr.it

Marco Reggiannini
email: marco.reggiannini@isti.cnr.it

Marco Tampucci
email: marco.tampucci@isti.cnr.it

A mobile crowdsensing app for improved maritime security and awareness

Keywords: Crowd-sensing, Volunteered Geographic Information (VGI), Citizen Science, Oil spill, Pollution, Maritime security and safety

The marine and maritime domain is well represented in the Sustainable Development Goals (SDG) envisaged by the United Nations, which aim at conserving and using the oceans, seas and their resources for sustainable development. At the same time, there is a need for improved safety in navigation, especially in coastal areas. Up to date, there exist operational services based on advanced technologies, including remote sensing and in situ monitoring networks which provide aid to the navigation and control over the environment for its preservation. Yet, the possibilities offered by crowdsensing have not yet been fully explored. This paper addresses this issue by presenting an app based on a crowdsensing approach for improved safety and awareness at sea. The app can be integrated into more comprehensive systems and frameworks for environmental monitoring as envisaged in our future work.

I. INTRODUCTION

Since the dawn of civilisation, the sea has always represented a source of wealth but also a dangerous environment and a gateway for threats. There are, therefore, two deeply connected needs: from one side, it is crucial to be able to protect the sea and make sustainable use of its resources, form the other it is necessary to be able to navigate through the environment safely and enhance the security against possible illegal and malicious actions and traffics, including piracy, trespassing, smuggling of immigrants and poaching. The first step in these directions is represented by environmental monitoring systems encompassing both marine and maritime aspects. Several approaches have been proposed, showing that solutions based on the integration and cross-correlation of multimodal data and disparate resources are the most promising [START_REF] Moroni | Environmental decision support systems for monitoring small scale oil spills: Existing solutions, best practices and current challenges[END_REF]. Sensorized buoys, Autonomous Underwater Vehicles

The work was partially supported by the Italian National Project PON "Smart Cities and Communities" S4E and by the EU H2020-BG-2020-1 Project Nautilos (Grant 101000825).

(AUVs), water-column samplers, ships of opportunity have been considered as in situ monitoring resources while airborne and space-borne imaging and sensing offer great opportunities for remotely assessing different aspects, including vessel traffic and water quality at different scales, ranging from global coverage data to the detailed assessment of small areas.

In situ resources can be linked together in order to create a distributed and pervasive network with the aim of creating an integrated infrastructure capable of characterising the open sea and the coastal environment in the marine boundary layer [START_REF] D'acunto | Optimized dislocation of mobile sensor networks on large marine environments using voronoi partitions[END_REF]. The network can be able to analyse different analytes (e.g. sulphates and nitrates) in the water, to measure the temperature profile of the water column, to estimate the parameters that define the state of the sea (significant wave height, direction, period and wavelength of the dominant waves), the field of surface currents and to detect the presence of contaminants such as hydrocarbons and heavy metals [START_REF] Moroni | Sensorized buoy for oil spill early detection[END_REF]. Buoys can also be equipped with submerged hydrophones capable of identifying activities related to poaching or signaling the presence of vessels in protected marine areas, or to generate earlywarnings for offshore seismic waves of significant intensity. The pervasive network on site can be completed by additional sensors such as ground or vessel-borne radar, including passive radar technologies taking advantage of opportunistic sources [START_REF] Li | Multi-frame fractional fourier transform technique for moving target detection with space-based passive radar[END_REF]. Indeed, with reference to coastal marine areas where it is not possible to install active radar sensors due either to radio-frequency pollution constraints or to the coast profile, passive sensors have demonstrated to have good capabilities for localisation, tracking and re-identification of vessels.

Among such disparate sources, a valuable contribution might be represented by Volunteered Geographical Information (VGI), that since the seminal work [START_REF] Goodchild | Neogeography and the nature of geographic expertise[END_REF] has proven to provide helpful information in case of events like hurricanes, earthquakes and pandemics. A VGI approach to the monitoring of oil spills has been proposed in [START_REF] Martinelli | Volunteered geographic information for enhanced marine environment monitoring[END_REF] in the framework of a more general and full-fledged Marine Information Sys-tem (MIS), featuring proactive decision support services [START_REF] Moroni | A proactive system for maritime environment monitoring[END_REF]. However, the possibilities of using VGI and, more generally, crowdsensing information have not be fully exploited to treat more general events related to safety and security.

In this paper, we present in Section II a demonstration of our proposed crowdsensing app that allows for the collection of extensive data for a wide range of observations. As mentioned in Section III, the collected data represent a wealth to be analysed to improve our knowledge of the sea. Finally, Section IV concludes the paper with directions for future work.

II. APP PROTOTYPE

Aiming at promoting its wide usage, the application has been developed exploiting the software framework React Native [START_REF]React native[END_REF]. Thanks to this, the application has been easily exported and built for both Android and iOS systems. It has been designed as simple as possible in order to promote its usage among volunteers, that could be discouraged by an excessive complexity. Thus, the application offers only a few essential functionalities with a basic and straightforward interface. As shown in Fig. 1 the application relies on a data server to store and retrieve the submitted reports. The data server is composed by a set of REST APIs, in charge of satisfying application requests, plus a PostGIS [START_REF]PostGis[END_REF] database. PostGIS database allows providing complex queries that take into account spatial position and distances, such as retrieving reports in the nearby of the volunteer position.

III. DATA COLLECTION AND USAGE

The application can be used to report the presence of anomalous situations discovered by any volunteer at sea or coastal areas (e.g. presence of plastics or oil spill), or to share particular and useful observations retrieved during divers campaigns. When the application is loaded, users can choose, from the opening screen to submit a new report to the data server, as shown in Fig. 2, or to refer to previously stored reports near the actual location.

Once the typology of the report is chosen, a second screen of the app is loaded. In this screen, volunteers can specify a set of details relative to the report; for instance, as shown in Fig. 3 for a plastic report, the user can specify the distance where the plastics are found and add a description about the plastic quantity, spreading and extension. By pressing the "Add" button, the data inserted by the volunteers, are used to compose a dedicated JSON that will be submitted to the data server through a REST request. On the server side, a service is in charge to collect the submitted JSONs, parse them and store the retrieved data into the PostGIS database. After that, an acknowledgement of the succeeded operation is sent to the application. Users can also view all the nearby reports from the opening screen by pressing the "View Map" button. Through the REST APIs, the application retrieves from the Data server all the previously submitted reports located near the user, and displays them on the map, where different types of reports are shown with different colours, as shown in Fig. 4. By selecting a report, users can access further information, such as its typology, the time of submission, exact GPS position, and eventual other details (e.g. distance of the plastic sightings, photo taken at the site).

IV. CONCLUSIONS AND FURTHER WORK

As described in Section II, the application is developed exploiting the software framework React Native that allows to easily publish and share it on both iOS and Android devices. This promotes the app diffusion and contributes to its wider usage. Moreover, the simplicity of the interface and the practicality of the available functionalities encourage volunteers to use it.

Despite at its current status the adoption of a spatial-enabled database (PostGIS) is used only to retrieve reports near to the user position, PostGIS will guarantee to develop more complex queries that relate to spatial information, e.g. considering the distance between several reports of the same type, discovering riskier areas or detecting dangerous clusters of events. A dedicated Web browser interface will be developed by integrating a GeoServer [START_REF]GeoServer[END_REF] in charge of generating a set of Web Map Services (WMSs) relative to the stored reports. In a such Geographic Information System (GIS) system, it will be possible to display the stored information and perform complex interactions to obtain statistical information about the submitted reports.

Extensive use of this app is planned in various collection campaigns, spanning from recreational sailors within the scope of the Italian National Project S4E, to citizen scientists in the frame of H2020 Project NAUTILOS, which will provide new and different data, particularly concerning the area in front of Leghorn, in the north Tyrrhenian Sea.

A prompt and performing system for the early detection of anomalies is of paramount relevance to pursue marine habitat safeguard, preservation, and to reduce threats related to the presence of pollutants or harmful chemicals.

Potential developments concern the exploitation of the proposed system as a bridge between multiple punctual observations and a consistent informative picture describing a given scenario at increasing spatial scales. Eventually benefiting from the data abundance ensured by the adopted pervasive sensing paradigm, novel services will address the integration of tasks dedicated to signal manipulation within the chain of the crowdsensing process.

To this aim, the app will be enriched by adding functionalities to manage images related to citizen science activities using artificial intelligence. Algorithms for automatic segmentation, classification and identification of biological fauna and flora species, as well as techniques to perform quantitative measurements of dissolved substances, represent the objective of future research.

Submitted reports and their elaboration will be converted into an ERDDAP (a well established open-source data platform [START_REF] Wilson | Erddap: Providing easy access to remote sensing data for scientists and students[END_REF]) accepted format to promote data diffusion. The overall system will be kept alive after the timeframe of the projects involved to provide a valuable tool for reporting anomalous events or of particular interest that may occur at sea or coastal areas.

Fig. 1 .

 1 Fig. 1. Working diagram of the app.

Fig. 2 .

 2 Fig. 2. Opening screen of the app.

Fig. 3 .

 3 Fig. 3. Example of screen for adding a plastic's report in the app.

Fig. 4 .

 4 Fig. 4. Example of map view with reports depicted as marker with different colours (in the example blue for plastic report, green for oil spill report)


ADAPTIVE EXTRUSION NON-PLANAR SLICING FOR ROBOTIZED FFF

Tanneguy DE VILLEMAGNE 1 , François EDY 1 , Simon ROBERT 1 1 Institut de Recherche Technologique Jules Verne, 44340 Bouguenais, France. tanneguy.de-villemagne@irt-jules-verne.fr, francois.edy@armor-group.com, simon.robert@irt-jules-verne.fr

Robotized Fused Filament Fabrication (R-FFF)

Tangential Continuity Method applied to R-FFF One of the major drawbacks in the FFF process is the poor adhesion at the interface of adjacent filament and therefore the low mechanical performances in the stacking direction (ST) of the printed part [START_REF] Barocio | Characterization of the Mechanical Properties of FFF Structures and Materials: A Review on the Experimental, Computational and Theorical Approaches" Enrique Cuan-Urquizo[END_REF]. Several studies have been conducted at IRT Jules Verne to improve ST mechanical properties focusing on building strategy. An innovative approach has been proposed based on the orientation of the extrusion nozzle to exert maximum pressure on the previous layer using a robotic arm and its 6 degrees of freedom. Robotized FFF (R-FFF) consists in adding an extrusion head to the arm which allows specific toolpath to be generated and 6D orientation of the nozzle. The tangential continuity method developed in concrete 3D printing ( [START_REF] Gosselin | Large-scale 3D printing of ultra-high performance concretea new processing route for architects and builders[END_REF]) allows wider interface areas between each material deposition layer and thereby maximizing layer adhesion. For each layer the nozzle is specifically oriented according to the direction of the next one (Fig 1). Fig. 1 -Schematic cut perpendicular to layers 3D printed using the cantilever method commonly found in commercial 2D slicing software (left) and the tangential continuity method (right). [START_REF] Gosselin | Large-scale 3D printing of ultra-high performance concretea new processing route for architects and builders[END_REF] One of the benefits of this approach is the possibility to avoid printing support material in the process, as the extrusion direction is no longer Z oriented but tangential to its coincident layer. This method also prevents overhanging extrusion.

Once applied to R-FFF process various constraints need to be managed such as the size and the part complexity and collision risks between the 6D moving nozzle and the part itself. Therefore, the slicing must be adapted from a planar (Fig. 1, grey lines) to a non-planar strategy.

Non-planar slicing: high curvature part

One of the use cases that benefits from non-planar slicing is a highly curved parts (Fig. 2, left picture).

Fig. 2 -High curvature part, slicing of the part with a non-planar strategy, non-parallels yellow layers

In conventional FFF process, such geometry would imply the printing of supports and the sole use of the tangential continuity method would not be processable with a planar slicing. The proposed approach relies on the slicing of the part in the direction determined with tangential continuity method considering the orientation of the layers themselves rather than the nozzle only. Non-planar slicing induces variable material volume deposition inside each layer: on the right picture Fig 2 one can see that the space between the slicing planes (yellow lines) changes accordingly with the curvature of the part.

Adaptive extrusion

To ensure the good adhesion between the printed layers and to avoid under or over-extrusion, the deposited volume must be adapted to the expected geometry path (Fig 3. Left picture).

Fig.3 -Adaptive extrusion non-planar slicing 3D printing examples

A dedicated algorithm has been developed to determine the exact volume deposition that is expected at each point of the part to be printed by the robot. The program generates a robotic toolpath associated with its specific extrusion commands for a given part.

Conclusions

This adaptive extrusion ability combined with non-planar strategy allows printing highly curvature pipe and a double pipe (Fig 3 .) with no support and improved apparent mechanical performances that still has to be quantified Getting rid of supports opens the door to new opportunities in 3D printing:

-Avoiding material and cleaning costs: reducing the number of operations to be done on the part and reducing the risks of breakage; -In situ 3D printing: the robotic arm to adapt its printing paths according to almost any substrate orientation; -Adding functionalities: the possibility to print onto a given complex existing part.

This study is part of the FACT project managed by IRT Jules Verne (French Institute in Research and Technology in Advanced Manufacturing Technologies for Composite, Metallic and Hybrid Structures).


Ralph Sindjui
email: ralph.sindjui@ifpen.fr

Gianluca Zito
email: gianluca.zito@ifpen.fr

Shimin Zhang
email: shimin.zhang@total.com

Experimental study of systems and oils for direct cooling of electrical machine

Keywords: Electric motors, Oil cooling, Cooling systems, Experimental bench

This study presents a test campaign carried out at IFPEN aimed at understanding and characterizing the thermal behaviour of electric motors incorporating direct oil cooling. Several cooling systems and oils are evaluated at different operating points and the effect of parameter variations is investigated.

Experimentations are defined and performed to understand and quantify the impact of different oils and direct cooling systems on the performances of the electrical machine. The test results make it possible to verify and quantify the gain on the thermal behaviour obtained by adding an oil injection system directly to the active parts of the machine in addition of an indirect water jacket cooling. This gain is observed for a representative set of operating points and oil injection parameters. The impact of physico-chemical properties of oils on direct cooling performance is assessed by comparison of several oils. The viscous friction losses are also identified. The results presented include a repeatability and reproducibility study for speed values up to 14 krpm and continuous powers up to 60 kW.

Introduction

The work presented in this document has been carried out as part of a research project aimed at developing specific experimental means for evaluating cooling systems for electrical machines. For this purpose, an electric motor has been designed in order to be compatible with different oil injection architectures and tested on a test bench. This set-up, combined with the specific instrumentation implemented, allows the analysis of the thermal behaviour for motor speeds up to 14 000 rpm and a continuous power up to 80 kW. Various measurement campaigns were carried out in order to provide: a detailed understanding of the thermal functioning (fluids flow and temperature) of most common cooling architectures proposed in the literature [START_REF] Davin | Experimental study of oil cooling systems for electric motors[END_REF][START_REF] Gai | Cooling of Automotive Traction Motors: Schemes, Examples, and Computation Methods[END_REF][START_REF] Li | Thermal Management of Electrified Propulsion System for Low-Carbon Vehicles[END_REF]; the identification of design and operating parameters that impact the performance of the cooling system; and finally, the impact of oil properties on the thermal stabilization of the machine. Indeed, understanding these different elements is necessary for the design of efficient cooling systems and the appropriate formulation of fluids in order to improve temperature management in electric machines based on the targeted application [START_REF] Bertin | Refroidissement des machines électriques tournantes[END_REF][START_REF] Bertin | Refroidissement des machines tournantes. Études paramétriques[END_REF][START_REF] Gundabattini | Thermal Mapping of a High-Speed Electric Motor Used for Traction Applications and Analysis of Various Cooling Methods-A Review[END_REF].

Because of climate change, transport vehicle manufacturers are prepared to transition to more sustainable, more efficient and cleaner mobility to reduce the impact of transport on the environment. This environmental emergency is driving governments to take actions through increasingly stringent standards to reduce CO2 emissions and through ecological regulation systems [START_REF] Mock | CO2 emission standards for passenger cars and light-commercial vehicles in the European Union[END_REF]. A new approach to the design of traction systems and a change in vehicles are therefore inevitable. In recent years, the development of electric mobility has been seriously studied in this sector, thus pushing the electric machine design to the fore.

An electrical machine acts as a power transformer, which converts electrical power into mechanical power. In practice, this conversion is not perfect and gives rise to losses in the form of heat dissipation [START_REF] Mcleod | High precision calorimetry for the measurement of the efficiency of induction motors[END_REF]. This can cause serious problems especially if the materials of the active parts of the machine surpass their thermal limits. In fact, beyond a given temperature, these materials lose their properties and the consequences can range from loss of performance to irreversible degradation of the machine [START_REF] Wang | Powertrain preheating system of tracked hybrid electric vehicle in cold weather[END_REF][START_REF] He | Efficiency decrease estimation of a permanent magnet synchronous machine with demagnetization faults[END_REF]. This therefore imposes limitations on the maximum rated performances of an electrical machine, hence the need for efficient cooling systems. Furthermore, because of the strong requirements imposed by car manufacturers, the trend is to design more and more compact and lighter engines, while seeking a higher power density. This increases even more the challenges of heat dissipation and the design of more efficient cooling systems.

Conventional cooling consists in cooling the casing of the machine which surrounds the stator. However, in such a system, the heat generated at the heart of the machine must pass through areas and interfaces where the thermal conduction is not ideal. Direct cooling of the hot parts is therefore more efficient, by using a heat transfer fluid as an exchanger to dissipate the heat generated during the operation of the machine. The work presented in this article therefore focuses on the experimental study of systems and fluids for direct oil cooling of electric motors for traction.

The paper is organized as follows: first the experimental setup used for the study is presented detailing the different types of cooling systems and the different parameters of oils used. Then the methodology for calculating heat exchanges as well as the various tests carried out are also detailed in section 2. Section 3 presents the analysis of viscous friction losses of the machine and the comparisons made for different architectures and different oils. Thereafter, the analysis of heat exchanges is addressed for each configuration, as well as the analysis of the thermal behaviour of the machine for each type of cooling. Finally, a discussion of the experimental methods used, the perspectives for the study and a summary of the experimental results are presented.

Experimental setup

The first step in carrying out this study is the manufacturing of a machine prototype allowing the integration of different cooling architectures and the set-up of a test bench capable of accommodating several types of oils. Cooling architectures selected from the literature present the best compromise in terms of performances and complexity of implementation [START_REF] Davin | Experimental study of oil cooling systems for electric motors[END_REF][START_REF] Gai | Cooling of Automotive Traction Motors: Schemes, Examples, and Computation Methods[END_REF][START_REF] Li | Thermal Management of Electrified Propulsion System for Low-Carbon Vehicles[END_REF]: these architectures are studied upstream of the campaign to allow comparison with the same cooling parameters (temperature, flow) and the same machine. In addition, the aim of the study being to estimate the heat exchanges, the design of the engine cooling zones is driven by this objective and well thought out in order to allow better modelling of the heat flows. In the same way, the instrumentation has been judiciously defined. Moreover, in order to study the thermal stabilization of the machine over a wide operating range, a high-power application has been considered to be representative of traction electric motors.

According to these specifications, a prototype has been designed and manufactured and a test bench is implemented for the needs of the study.

System presentation

Principle

The designed machine has a peak torque of 260 Nm for a peak current of 640 A, a mechanical peak power of 150 kW, a peak efficiency of 96 % and a maximum operative speed of 14 000 rpm. In continuous performance (with a standard water jacket cooling), it allows operation at a continuous torque of 130 Nm at 6000 rpm corresponding to a continuous mechanical power of 80 kW. The motor is supplied by a prototype inverter with a nominal DC voltage of 350 V. Its mechanical performance curve is presented in Figure 1. The water and oil circuits are also instrumented with pressure, flow and temperature (thermistor) sensors to monitor the pressure at the outlet of the supply group, the flow in each branch of the cooling circuit as well as the temperature at the inlet and outlet. The sensors used have accuracies of ± 0.1°C, ± 1°C and ± 6 l/h, for thermistors, thermocouples and flow measurements respectively.

Additional sensors are added to the machine housing and the bench to allow modelling of heat exchanges by conduction and convection (between the machine and the bench environment). In order to quantify this thermal flow, preliminary tests are carried out in a temperature range between 25 °C and 80 °C to identify the thermal inertia. This allows to estimate the convective exchange coefficient between the electric machine and the bench cell. This exchange coefficient is necessary for the estimation of the flows exchanged with air. These tests highlight the need to thermally isolate the machine and the circuit. Indeed, without thermal insulation, the water circuit heats the casing and a large flow is dissipated in the air. This phenomenon would be problematic for an accurate estimation of the thermal flows as the water cooling will extract heat from the dissipative parts of the machine and part of this heat will be lost in the air and cannot be quantified. Figure 4 illustrates the electric motor prototype before and after the thermal insulation. It has been estimated that the insulation reduces the equivalent heat exchange coefficient by a factor of two compared to a configuration without insulation.

In addition, it leads to a reduction in the variation of the heat flux dissipated by the water over the entire temperature range and thus an accurate estimation of thermal balances.

. Injection systems

In order to test the thermal sensitivity linked to the cooling architecture, two main architectures have been selected for this study:

•

The first one enables to test injection nozzles [START_REF] Park | Thermal characteristics and effects of oil spray cooling on in-wheel motors in electric vehicles[END_REF][START_REF] Staton | Influence of different end region cooling arrangements on end-winding heat transfer coefficients in electrical machines[END_REF][START_REF] Kim | Spray cooling heat transfer: The state of the art[END_REF][START_REF] Jafari | Analysis of heat transfer in spray cooling systems using numerical simulations[END_REF] above the endwindings and a gravity flow around their periphery. For this architecture, two types of commercial injectors are used and allow working in a flow range from 120 to 480 l/h. One configuration is a flat jet nozzle allowing a large projection angle, the winding is therefore impacted over a large but not very wide angular range. The other one is a full cone nozzle which consists in impinging the upper part of the winding more widely. The rest of the winding is cooled by the gravity flow of the oil to the bottom of the casing.

•

In the second architecture, a ring is placed around the end-winding with a distribution of orifices on it. For this architecture, two configurations have been designed, and the admissible flow rate is in the range from 120 to 480 l/h: a configuration in multi-jet type with sixteen outlet holes over the entire circumference; a configuration in dripping type containing five outlet holes positioned on top of end-windings.

Figure 5 presents a summary of the different oil cooling architectures tested.

Figure 5 Oil injection architectures integrated in the prototype The cooling performance of fluid could be assessed by a physically based Figure of

Merit (FOM) [START_REF] Ehrenpreis | Physically-motivated Figure of Merit (FOM) assessing the cooling performance of fluids suitable for the direct cooling of electrical components[END_REF]. In the case of direct cooling by a laminar flow, the FOM on average heat transfer 𝐹 ℎ is expressed as

𝐹 ℎ = 𝜌 𝑎 𝐶𝑝 𝑏 𝑘 𝑐 𝜈 𝑑 (1)
where ρ is the density, Cp the heat capacity, k the thermal conductivity and ν the kinematic viscosity; a-d are parameters depended on the contact geometry.

These four fluids are different in their formulation. The most important difference between these fluids is their kinematic viscosity. At 40 °C, the fluid F1 has a viscosity three times higher than F2 and F3. The other properties have a slight difference of between 5 % and 7 %, over the entire temperature range. Fluid F4 has a reduced viscosity with shear due to the presence of polymers in its formulation. As the shear rate changes with the size of the injector, this fluid could have interesting behaviour for some injection architectures.

F1 has been studied as a reference for the comparison between different injection architectures, and the other three fluids are studied in the context of the comparison of fluids. For this reason, their comparison will not be presented (in this document) for all architectures. However, the verification of trends was indeed carried out as part of the study. The physicochemical properties are also illustrated in Figure 6.

Tests

Several types of measurements are performed for this study.

Preliminary tests

Convective exchanges between the ambient air and the engine have a direct influence on the accuracy of the thermal balances because of the uncertainty of measurement in each test. This is the reason why it is necessary to quantify them, with a dedicated process in order to determine their impact on the thermal balance and consider corrective actions on prototype conditioning if required. Therefore, specific thermal inertia tests have been carried out in order to estimate the convective exchanges between the casing of the electric machine and the ambient air in the cell of the test bench. These tests are performed with the electric machine at rest, activating only the water-cooling circulation (50-50 glycol-water). The details of this phase are discussed in section 2.3.1.

No-load tests

The aim of this type of test is to quantify the impact of fluid's properties and/or of an oil injection architecture on viscous friction losses. The friction losses depend essentially on the speed. However, since the cooling is direct, it is essential to check the influence of the addition of a fluid (including its properties and the injection method used)

on the viscous friction of the machine. The test method consists in driving the electric machine by the motor of the test bench by carrying out a sweep over the entire speed range (from 500 rpm to 14 000 rpm in our case). The no-load torque is thus measured on the shaft line at the coupling with the electric machine. The power lost by friction is thus calculated.

In order to define the impact of adding direct oil cooling system, it is necessary to define reference running tests without oil injection. These tests will serve as benchmarks for comparison with different given cooling configurations. In this study, the reference tests are carried out with the presence of water cooling allowing a thermal envelope around the electric machine (water jacket). Oil cooling is then added to water cooling at the same temperature, allowing the estimation of the impact of oil cooling. The viscosity of the oil being strongly dependent on the temperature (as shown on Figure 6), this characterization is performed at different temperatures. Several characteristics are thus obtained as a function of the speed, a curve for each oil temperature. It is also necessary to characterize the impact of the injection flow rate on viscous friction losses. The same approach as that of the temperature is therefore followed within the framework of the characterization for different flow rates.

On-load tests

On-load tests with cooling allow quantification and analysis of the impact of oil cooling on the thermal behaviour of the machine in operation. Initially, reference tests are carried out without oil cooling (cooling by the water jacket only). The same test points are carried out with oil cooling using the different architectures under study. These points with oil cooling are compared to the reference and thus the impact of oil cooling can be quantified. Similarly, to the no-load case, the impact of the flow rate and the temperature of the oil injected is evaluated.

Methodology

The methodology is detailed for each type of test presented in the previous section 2.2.

Preliminary tests methodology

In this test configuration, the water provides heat which is diffused in the casing of the machine and is transferred by convection into the ambient air and by conduction towards the test bench. A heat balance is carried out using the stabilized values of the measurements in order to quantify the heat fluxes provided by the water and received by the ambient air, as well as the conductive losses towards the bench.

The thermal power provided by the water 𝑃 𝑤𝑎𝑡𝑒𝑟 is calculated by

𝑃 𝑤𝑎𝑡𝑒𝑟 = 𝑚̇𝑤 𝑎𝑡𝑒𝑟 𝑐 𝑝 𝑤𝑎𝑡𝑒𝑟 (𝑇 𝑤𝑎𝑡𝑒𝑟 𝑜𝑢𝑡 -𝑇 𝑤𝑎𝑡𝑒𝑟 𝑖𝑛 ) (2)
where 𝑚̇ is the mass flow, 𝑐 𝑝 the heat capacity, 𝑇 𝑤𝑎𝑡𝑒𝑟 𝑜𝑢𝑡 the outlet temperature and 𝑇 𝑤𝑎𝑡𝑒𝑟 𝑖𝑛 the inlet temperature of the coolant.

The thermal power transferred by conduction to the test bench 𝑃 𝑏𝑒𝑛𝑐ℎ is considered in order to refine the thermal balance. This power can be estimated by

𝑃 𝑏𝑒𝑛𝑐ℎ = ∆𝑇 𝑀-𝑏𝑒𝑛𝑐ℎ 𝑅 𝑡ℎ (3)
where 𝑅 𝑡ℎ is the thermal resistance calculated for the four columns used to support the machine (see Figure 4) and which thus thermally connect the electric machine to the bench, ∆𝑇 𝑀-𝑏𝑒𝑛𝑐ℎ is the temperature gradient measured between the machine housing and its supports [START_REF] Boglietti | Evolution and Modern Approaches for Thermal Analysis of Electrical Machines[END_REF][START_REF] Boglietti | Electrical Machines Thermal Model: Advanced Calibration Techniques[END_REF].

The power exchanged with the ambient air 𝑃 𝑎𝑖𝑟 is given by

𝑃 𝑎𝑖𝑟 = 𝛼 𝐻𝑇𝐶 𝑎𝑖𝑟 𝑆 𝑒 (𝑇 ℎ𝑜𝑢𝑠 -𝑇 𝑎𝑖𝑟 ) (4)
where 𝛼 𝐻𝑇𝐶 𝑎𝑖𝑟 is the convective air exchange coefficient, 𝑆 𝑒 the exchange surface between the machine and the ambient air, 𝑇 ℎ𝑜𝑢𝑠 the machine housing temperature and 𝑇 𝑎𝑖𝑟 the ambient air temperature.

A first balance of powers 𝑃 𝑡𝑜𝑡 is thus obtained allowing the estimation of the convective transfer coefficient between the machine and the test bench

𝑃 𝑤𝑎𝑡𝑒𝑟 + 𝑃 𝑎𝑖𝑟 + 𝑃 𝑏𝑒𝑛𝑐ℎ = 𝑃 𝑡𝑜𝑡 ≈ 0 (5)

No-load tests methodology

The power lost by friction is the product of the measured friction torque at noload and the motor speed. Since the initial conditions of the machine have an impact on loss measurement, the sequence of tests should be properly defined in order to avoid measurement errors. In this study, a rest time is left between each no-load test until the measured temperatures return to the same initial condition. Each test is repeated several times in order to estimate the dispersion of the measurements. This allows to quantify the random errors. The comparison between the different cooling parameters is then performed on the arithmetic means of the associated series of observations. For comparison, each point is illustrated with an error bar representing the maximum standard deviation associated with each series. For better readability, error bars are not displayed when two curves overlap.

On-load tests methodology

For each test point, a thermal balance is carried out and the different thermal powers are calculated/estimated from the measurements obtained in steady state. The total thermal power obtained is compared to the measured total losses of the machine (𝑃 𝑡𝑜𝑡𝑎𝑙-𝑙𝑜𝑠𝑠 ) in order to quantify the quality of the test considered. The total losses of the machine are obtained by difference between the three-phase electrical power measured at the input and the mechanical power measured at the output.

In addition, the temperatures measured by the sensors integrated in different places of the stator (see section 2.1.1) are compared in order to quantify the performance of oil cooling and to compare the impact of the cooling architectures on the temperature distribution (winding and stator stacks).

The different thermal powers are calculated as detailed in section 2.3.1. When oil cooling is used, the heat power absorbed by the fluid 𝑃 𝑜𝑖𝑙 is calculated by

𝑃 𝑜𝑖𝑙 = 𝑚̇𝑜 𝑖𝑙 𝑐 𝑝 𝑜𝑖𝑙 (𝑇 𝑜𝑖𝑙 𝑜𝑢𝑡 -𝑇 𝑜𝑖𝑙 𝑖𝑛 ) (6)
This heat absorbed by the oil circuit is added to the heat balance written in relation [START_REF] Bertin | Refroidissement des machines tournantes. Études paramétriques[END_REF] to give the overall thermal power 𝑃 𝑡ℎ𝑒𝑟𝑚𝑎𝑙 of the system (including the oil circuit). The contribution of each cooling circuit to the heat exchange is obtained by means of the corresponding heat flows. Thus, we can quantify the accuracy of each test through the following parameter 𝜀 :

𝜀 = 𝑃 𝑡ℎ𝑒𝑟𝑚𝑎𝑙 -𝑃 𝑡𝑜𝑡𝑎𝑙-𝑙𝑜𝑠𝑠 𝑃 𝑡𝑜𝑡𝑎𝑙-𝑙𝑜𝑠𝑠 (7)

Friction losses analysis

To assess the impact of oil injection on viscous friction losses, tests are first carried out without oil as described in section 2.2.2. Figure 7 shows the no-load losses obtained for a variation in glycol-water temperature ranging from 25 °C to 85 °C and for a constant flow rate of 720 l/h. Figure 7 No-load losses for different water-cooling temperatures For a given cooling temperature, the mechanical power at no-load can be approximated by a quadratic form [START_REF] Vanhaelst | A systematic analysis of the friction losses on bearings of modern turbocharger[END_REF] depending on the driving speed of the machine.

Moreover, it decreases when the cooling temperature increases. For a speed of 14 000 rpm, a decrease in the mechanical no-load power of about 600 W is observed for an increase in water cooling from 25 °C to 85 °C. This decrease can be explained by the reduction in mechanical losses in the bearings and seals with the increase in temperature [START_REF] Vanhaelst | A systematic analysis of the friction losses on bearings of modern turbocharger[END_REF]. In addition, when the temperature increases, the magnetic flux induced by the magnets decreases and leads to a decrease in iron losses [START_REF] He | Efficiency decrease estimation of a permanent magnet synchronous machine with demagnetization faults[END_REF].

The oil circuit is then activated in addition to the water circuit and parametric variations are made for each architecture and for each oil, under the conditions described in section 2.2.2. For all these measurement conditions (temperature variations and oil flow ranges considered presented in Table 2), there is no significant impact of the addition of direct oil cooling on the no-load losses, in comparison with the reference tests.

The variation of oil flow also has a low impact on viscous friction losses for the different oils and injection architectures. The temperature variation of the water and oil follows the trend presented in Figure 7. Concerning the variation in flow, an example is given in Figure 8 which presents the variation from 120 l/h to 480 l/h with a flat jet nozzle (see Figure 5), for a given cooling temperature of 60 °C (water and oil). The reference test is also shown on the same graph to illustrate the impact of the oil presented above. These trends are observed for the entire measurement range presented in Table 2.

Friction losses analysis: Architectures comparison

Similarly, no significant impact of the cooling architectures on the viscous friction losses of the electric machine has been observed. As an example, the Figure 9 presents the comparison of the losses observed for a given flow rate of 360 l/h and a temperature of 60 °C for the four architectures presented in Figure 5.

Figure 9 No-load losses: injection architectures comparison for a given cooling temperature of 60 °C (water and oil) and an oil flow rate of 360 l/h

Friction losses analysis: Oils comparison

The main observation made for the comparison of fluids is that the viscous friction losses are mainly related to the viscosity of the fluid in contact with the shaft line [START_REF] Vanhaelst | A systematic analysis of the friction losses on bearings of modern turbocharger[END_REF]. In fact, lower viscosity implies lower losses. Concerning the fluids of this study, the comparison is therefore more effective at low temperature (40 °C) because it allows to discriminate the kinematic viscosity of the fluids (see Figure 6). Moreover, the higher the temperature of the fluid is, the lower the losses will be (as presented on Figure 7) and therefore more difficult to discriminate different fluids. It is important to mention, however, that the observed variation in no-load losses with viscosity is small and is around 100 W (at the most) on all four fluids.

Heat exchange analysis

Using the methodology presented in part 2. The power captured by the oil linearly decreases with increasing oil temperature as illustrated in the Figure 10. This strong linear correlation is identified on the thermal behaviour of the stator sensors. It can be observed that the temperature gain decreases drastically when the temperature of the cooling oil is increased (this trend is detailed in section 5). (see Figure 11). There is an increase on the order of 400 W maximum for a fixed temperature of 60 °C and a flow rate ranging from 180 l/h to 480 l/h. This small variation should not the overall thermal response of the machine because of the compensation of the water circuit on the heat exchange.

Figure 11 Thermal power captured by the coolants for a given oil temperature of 60 °C:

impact of the oil flow rate

Heat exchange analysis: Architectures comparison

Regarding the influence of the cooling architecture, no significant impact was observed (Figure 12) on the heat flow among the four architectures selected for the study.

It is important however to associate this observation with the thermal response of the machine (section 5) to have the thermal contribution of each architecture.

Heat exchange analysis: Oils comparison

Figure 13 shows the evolution of the power absorbed by the four oil variations versus temperature, for a fixed flow rate of 360 l/h and an operating point of 6000 rpm -50 kW. The gap in absorbed thermal power between F1, F2, F3 and F4 is the highest at low temperatures (40 °C) and decreases considerably for higher temperatures.

This observation is coherent with the trend observed for the kinematic viscosity when comparing different fluids (see Figure 6). For the other properties, the difference is relatively constant with the increase in temperature. We therefore deduce that the power captured by the oil is mainly dependent on its viscosity and the more viscous the fluid, the less the ability to capture heat will be. Both the viscosity and the heat capacity have an impact on the power absorption, while the impact of viscosity is less obvious. The heat transfer between the machine and oil flow is mainly via forced convection. At the jet impingement zone, the oil flow is partially turbulent. Flow regime is determined by the Reynolds number, which is related to the fluid viscosity. When the fluid has a low viscosity, its flow is prone to turbulence [START_REF] Dairay | Direct numerical simulation of a turbulent jet impinging on a heated wall[END_REF][START_REF] Cornacchia | Development of an Experimental Methodology to Characterize Liquid Cooling Systems for Electric Motors[END_REF], and therefore increase the heat transfer rate. Moreover, the viscosity impacts the thickness of viscous sublayer, which is a laminar layer between the wall and upward turbulent flow. A high viscosity fluid tends to have a thick viscous sublayer, and thus reduce the heat transfer rate. In this work, the fluids have almost similar heat capacity, while their viscosity is more diverse and therefore appear to have a more significant impact on the heat absorption. At 40 °C -360 l/h for the example shown in Figure 13, we have a difference of about 600 W for a viscosity difference of about 30 cSt (between F1 and F4).

Analysis of the thermal behaviour of the machine

The tests performed for the heat exchange analysis presented in Section 4 can also be used to study the impact of direct oil cooling on the thermal stabilization of the machine. In this context, the thermal reference (water jacket cooling) is observed at first (see Figure 14). This first step also allows us to observe the temperature distribution in the stator through the five different thermocouples mounted in the winding at strategic locations (see section 2.1). Thus, the gain in temperature can be evaluated over the entire speed range up to 14 000 rpm and a power range up to 60 kW. As a precaution for the machine, this load has not been exceeded for continuous tests and represents the maximum load up to which the observations are made in this part. Due to the presence of the water chamber around the stator, the centre of the stator (relatively to the active length) is better cooled and the hot spots are located at the coil end-winding. A relative difference of about 20 °C is observed between the centre of the stator and the coil end-winding, for an operating point of 3000 rpm -40 kW. As expected, the temperature measured on the stator is higher at low speed than at high speed as, for a given power, a higher torque is needed at low speed. As the torque is directly linked to the intensity of the current flowing through the winding, the higher the torque, the higher the current required, therefore implying a more dissipated power by Joule effect. As a result, the stator temperature is generally higher at low engine speeds, which creates a more favourable condition to highlight the impact of an additional coolant fluid.

Unlike to the observation made on thermal flows, the impact of the variation in flow rate is not significant on the thermal response of the electric motor (variation presented on Figure 15 for a flat jet nozzle and an operating point of 6000 rpm -50 kW).

Indeed, the power captured by the oil increases with the increase in the oil flow. However, this phenomenon does not affect the thermal response because the variation in the oil thermal power (as a function of the oil flow) is compensated with the power absorbed by the water circuit. This heat flux decreases linearly with the oil flow, which clearly shows this compensation effect. Moreover, the measured temperature decrease is inversely proportional to the oil temperature and highly dependent on the latter. The geometry of the machine being fixed, the parameter which will mainly impact the heat transfer by convection is the temperature gradient between the oil and the stator. When the oil 29 temperature increases, this gradient is reduced and therefore the power absorbed by the oil also decreases.

Thermal performance: Oils comparison

Figure 18 illustrates the comparison of the temperature gain obtained with a flat jet type injection, for the different oils and for an operating point of 6000 rpm -50 kW.

Low viscosity fluids have better cooling power. This observation is more marked when cooling at 40 °C than for higher temperatures. This latter temperature corresponds to the temperature at which the viscosity discrimination is most obvious (see Figure 6). As the difference in terms of temperature gain between the four fluids remains small, a repeatability study is integrated into the test campaign to check the degree of confidence on the differences obtained. This aspect is commented on in section 6. The impact of injection architectures on the cooling performance is low for fluids of different viscosities.

Figure 18 Gain from adding different oils with a flat jet nozzle on the end-windings average temperature, for different oil cooling parameters and an operating point of 6000 rpm -50 kW

Discussion

In order to obtain accurate thermal balances, all the results presented so far are carried out with an active water cooling. However, we observed that under certain conditions, especially for an oil at 40 °C, the thermal power absorbed by the water jacket is negative (see Figure 10). This negative flow means that overall, the water heats the engine instead of cooling it because the reference temperature is higher (65 °C). These results seem to confirm that the oil alone injected on the coil end-winding is sufficient to properly cool the machine, and that it is possible to carry out tests without a watercooling circuit. the stator grid is sufficient to maintain the temperature in the engine below the reference value (see Figure 14) in continuous operation. The temperature gradient in the stator is higher for tests without water. Among the five sensors integrated in the winding, the temperature at the stator centre shows a highest increase when removing the water circuit. This is consistent with the cooling architecture: the oil is sprayed on both sides of the stator, so it has a more significant impact on the coil heads and moves the hot spots towards the centre. Also, at 40 °C, the thermal power absorbed by the oil is about 400 W lower compared to a test with an active water jacket cooling. This confirms that the oil captures heat from the water in addition to that from the machine. Moreover, there is a lower variation in the power captured by the oil with temperature without the presence of the water chamber (for a passage of cooling from 40 °C to 60 °C, a variation of about 200 W without water against 1400 W with the presence of the water jacket).

The comparison of performance for indirect water jacket cooling and direct oil cooling could be conducted observing two physical quantities: thermal flow and stator temperature. This is completely coherent with the fact that the stator centre is more distant from the oil impact zone. Globally, the efficiency of direct oil cooling is well demonstrated from these comparisons. The viscosity of fluid F4 slightly reduced after the campaign. This decrease in viscosity is explained by the degradation of the polymers present in this fluid. The fluid undergoes strong shearing in the injector, the polymer chains break, and the fluid permanently loses its viscosity [START_REF] Wood | The change of viscosity of oils containing high polymers when subjected to high rates of shear[END_REF]. However, after a rheological analysis carried out on this fluid before and after the tests, it is verified that the degradation of the polymers is not severe and retains its kinematic properties specific to the presence of polymers. This ensures that the polymer does not degrade severely in the injection devices.

As discussed in section 5.2, a repeatability and reproducibility campaign is carried out in this study to determine the degree of confidence in our various observations. This campaign consists of repeating the same test several times. Five repetitions were performed for each test point. The test sequence is defined so that each repetition (for each point) is carried out on a different day and with the same initial conditions for starting the test (maceration time, mechanical stabilization of the machine, conditioning parameter of the fluid). Then, the methodology presented throughout this document is deployed for each test and the standard deviations are quantified to give the dispersion of the tests. Throughout this phase, a maximum standard deviation of 36 W (for thermal power absorbed in coolant) and 0.6 °C (for all measured stator temperatures) is obtained for the water jacket reference tests. About the tests with oils (in addition to water), a maximum standard deviation of 21 W and 0.4 °C. This applied for the entire operating range up to 14 000 rpm and powers up to 60 kW.

The perspectives of this study include extensive tests without water, using other injection methods (as direct cooling of the windings coupled with rotor hollow shaft cooling [START_REF] Lehmann | Fluid Choice Based on Thermal Model and Performance Testing for Direct Cooled Electric Drive[END_REF]), and the study of multifunction fluids (lubrication-cooling coupling).

In order to improve the methodology, other aspects which could impact the oil cooling of the electric machine will be studied: additional information could be provided implementing a method for the visualization of the flows [START_REF] Liu | Experimental Investigation on Oil Spray Cooling With Hairpin Windings[END_REF][START_REF] Ha | Study of Injection Method for Maximizing Oil-Cooling Performance of Electric Vehicle Motor with Hairpin Winding[END_REF] to allow for a better understanding of the phenomena observed by measuring viscous friction losses. It is also planned to add more sensors in order to have a more accurate measurement of the stator temperature distribution, and for the monitoring of the bearing's temperature. The thermal insulation of the electric machine will also be enhanced with the use of a climatic chamber, to improve the quality criterion of the tests [START_REF] Mcleod | High precision calorimetry for the measurement of the efficiency of induction motors[END_REF].

Conclusion

In this study, experimentations carried out aimed at understanding and characterizing the thermal behaviour of electric motors incorporating direct oil cooling are presented. A prototype representative of traction electric motors in terms of operating range is manufactured integrating different cooling architectures. A test bench capable of accommodating several types of oils is implemented. The instrumentation and the associated methodology are defined in order to assess the friction losses, the heat exchanges and the electric motor thermal behaviour.

Four fluids that are electrically compatible with motor active parts and have different properties are selected and prototyped by Total™. They are chosen by their chemistry and physicochemical properties in order to understand the impact of oils on cooling. The influence of the cooling architectures, flow rate, temperature on their performance is also assessed. Several configurations of oil cooling systems are thus evaluated at different operating points and different parametric variations.

Several campaigns are carried out in order to understand and evaluate the impact of different oils and architectures of direct cooling on the functions of the electrical machine. The test results make it possible to verify and quantify the gain on the thermal behaviour obtained by adding an oil injection system directly to the active parts of the machine (in addition to the indirect water-cooling system at the stator level). For example, a significant decrease of around 50°C in the coil end-winding temperature can be observed at an operating point of 6000 rpm -50 kW. This gain is observed for various operating points and a variation of the oil cooling parameters. The observations and results are confirmed by a repeatability and reproducibility study for speed values up to 14 000 rpm and continuous powers up to 60 kW.

A discrimination of the effect of the oil on cooling is also achieved, allowing to evaluate the impact of physico-chemical properties of oils on direct cooling performance as well as the impact of different cooling architectures selected for the study. The viscous friction losses are also observed in order to quantify the influence of the addition of oil on the active parts of the engine -due to the direct injection type, the parameters of the cooling (temperature, flow), or the chemistry and physicochemical properties of oils.

Further investigations will be carried out on a new version of the electric motor including direct injection supplied by the rotor shaft, and an enhanced instrumentation (oil injection visualization, sensors for an extended stator temperature distribution, bearings and rotor temperature monitoring) will be provided for a better understanding of the thermal issues. New multifunction fluids prototyped by Total™ will also be evaluated for the lubrication and cooling functions.

Figure 1

 1 Figure 1 Mechanical performance of the machine manufactured for the study

Figure 2

 2 Figure 2 General architecture implemented for the study on direct oil cooling

Figure 3

 3 Figure 3 Stator instrumentation

Figure 4

 4 Figure 4 Prototype and instrumentation before (left) and after (right) the thermal

Figure 6

 6 Figure 6 Illustration of experimental physicochemical properties of oils selected

Figure 8

 8 Figure 8 No-load losses for different flow rates for a flat jet nozzle and a given cooling

  [START_REF] Li | Thermal Management of Electrified Propulsion System for Low-Carbon Vehicles[END_REF] and the instrumentation in place, the flux captured by each branch of the circuit is calculated. This provides an overview of all heat exchanges at the engine level. The tests carried out for the identification of flows are those described in section 2.3.3. Tests are carried out over a period of 90 minutes to have a consistent thermal stabilization. A maceration time of around 60 minutes is also systematically left before the start of each test. The flow calculations are performed on temperature values averaged over the steady state (which corresponds to an averaging over 30 minutes minimum). As for the no-load losses test series, first reference tests with glycol-water cooling were carried out (for fixed temperatures and flow rates of 65 °C and 720 l/h, respectively) before adding the different oil cooling configurations for comparison.

Figure 10

 10 Figure 10 Power captured by the coolants for a given oil flow rate of 360 l/h: impact of

Figure 12

 12 Figure 12 Power absorbed by oil cooling: temperature variation and architecture

Figure 13

 13 Figure 13 Power absorbed by oil cooling: temperature variation and oil comparisons for

Figure 14

 14 Figure 14 Thermal reference test: water jacket cooling temperature 65 °C and flow rate

Figure 15

 15 Figure 15 Direct oil F1 cooling impact: coil end-winding average temperature for various

Figure 16 5 . 1 .Figure 17

 165117 Figure 16 Gain from adding oil F1 with a flat jet injector on the end-windings average

Figure 17

 17 Figure 17 Gain from adding oil F1 for different cooling architectures on the end-windings

Figure 19

 19 Figure 19 Water jacket impact at 6000 rpm -50 kW: (a) coil end-winding average, (b)

  Figure 20 illustrates the thermal flow absorbed by water and oil circuits, in a test where the two cooling systems are both active at an operating point of 6000 rpm -50 kW. The temperature of water and oil is similar; however, oil begins to absorb more heat at a flow rate of 240 l/h, which is a third of the water flow rate of 720 l/h, and this trend increases with the oil flow rate. Figure21illustrates the stator temperature of the electric machine cooled by either water jacket or direct oil jet. It can be clearly observed that oil reduces significantly the coil end winding temperature by the direct contact. The stator centre temperature increases slightly with direct oil cooling.

Figure 20 Figure 21

 2021 Figure 20 Comparison of thermal power absorbed by water and oil at 6000 rpm -50 kW

  Figure Captions List

[cSt] KV 100* [cSt] Purpose

	Reference KV 40* F1 Composition Gr III/PAO Additive 25.89	5.28	Reference
	F2	Gr V	Additive	7.96	2.47	Formula chemistry
	F3	Gr III/PAO Additive	8.73	2.48	Viscosity
	F4	Gr III/PAO Additive Polymer	11.71	5.24	Viscosity under shear
			Table 1 Characteristics of oils selected for the study
		*KV40: kinematic viscosity at 40°C; *KV100: kinematic viscosity at 100°C
		2.1.3. Different types of oils
		Four fluids (referenced as F1, F2, F3 and F4) that are electrically compatible and
	have different properties are selected and manufactured in partnership with Total™ for
	this study. Their characteristics are listed in Table 1. They are chosen by their chemistry
	and physicochemical properties in order to understand the impact of oils on cooling, and
	the interactions between the fluid and other factors, such as cooling architecture, flow
	rate, temperature.

Table 2

 2

			Water flow
	Test	Operating point	Water temperature	Oil temperature Oil flow rate
			rate
	Preliminary	0 rpm	From 25 °C to 80 °C	N/C	N/C
		Speed up to 14 000 rpm		From 25 °C to	From 120 l/h
	No-load		From 25 °C to 85 °C
		No load	720 l/h	80 °C	to 480 l/h
		Speed up to 14 000 rpm		From 40 °C to	From 240 l/h
	On-load		65 °C
		Power up to 60 kW		80 °C	to 480 l/h

presents a summary of the different types of tests and parameter (temperature, flow rate, operating point) variations carried out.

Table 2

 2 Summary of the different types of tests carried out and the different parameters

The authors declare that there is no conflict of interest.

Acknowledgment

The authors gratefully acknowledge the contributions of the people from the IFPEN Mobility and Systems Research Division involved in this project. This study would not have been possible also without the collaboration with Total™.