I Hamm

F Rossi

G Poulachon

B Marcon

H Birembaux

N Maury

S Valadon

F Labarthe

HIGHLY RESPONSIVE AND ACCURATE TEMPERATURE MEASUREMENTS IN ORTHOGONAL CUTTING THROUGH INNOVATIVE SINGLE LEG THERMOCOUPLE

Keywords: Temperature measurement, Ti-6Al-4V, Orthogonal cutting, Heating rate

During machining, most of the thermal sources are resulting from the conversion of mechanical energy during the chip shearing mechanism and the intense frictions between the tool, the chip, and the machined surface. Thermal gradients are high and localized, especially for low thermal diffusivity material like titanium alloys. Moreover, heat rates come close to few million degrees per second in the shearing zones. Even though several authors performed thermography techniques to determine the temperature distribution into the machined material, their results remain underestimated due to experimental limitations. In this research, a new temperature measurement technique has been developed based on a single wire thermocouple to measure at one exact location both the temperature and the heating rate during orthogonal cutting test. The tests are performed with uncut chip thicknesses of 0.020 mm and 0.100 mm and with a cutting speed of 120 m/min with two different cutting angles. Results are discussed and compared to prior studies.

INTRODUCTION

Almost all the mechanical energy developed during machining is converted into thermal energy. For abusing machining conditions, excessive thermal energy are brought to the cutting area leading to an strong temperature increase in the different cutting areas, reducing tool life and inducing surface integrity alterations. For instance, overheating plays a significant role on the fatigue life of Ti-6Al-4V. It is therefore necessary to quantify the temperature level in machined parts accurately. Since 1920's, many techniques have been used to measure the temperature distribution into the chip, the tool and the workpiece during interrupted cutting process as milling [Stephenson 1992] or continuous cutting process as turning. As heating rates may reach more than 10 6 °C/s in the primary shearing zone, selection of a suitable temperature measurement technique with a very low time response become an important criteria. Thermocouples (TC) have many advantages compared to spectral radiation thermometry. According to Davies et al., [Davies 2007], TC are relatively low cost, stable and reproducible, and also fast responders (10 µs). Identification of both the temperature intensity and the heating rates during machining is a valuable information to establish the thermal heat flux as well as the heat partitions acting on the tool and the machined surface. Accurate experimental measurements allow to compare the thermal partition equations and to assess the role of each cutting process parameters on the heat generation during machining. Indeed, several methods based on analytical calculations and numerical simulations were proposed by different authors such as [Hahn 1951], [START_REF] Trigger | An analytical evaluation of metal-cutting temperatures[END_REF]], [Boothroyd 1963], [START_REF] Komanduri | [END_REF]] whose models are based on the theory of a mobile heat source. In all those previous cited research cases, the predicted temperatures and those measured experimentally differ significantly due to analytical assumptions. In this article, a new experimental setup is described precisely and used to measure accurately and with a fast response the cutting temperatures beneath the machined surface through a single leg miniaturized thermocouple welded on the sample.

In addition, the creation of a new thermocouple made of the workpiece and a micrometric single leg thermocouple has many advantage compared to the common thermocouples measurements find in the literature.

As the workpiece is part of the thermocouple: 1-The measurement is no longer invasive enabling to get access to the temperature without thermal field modification.

2-The time response is equal to the speed of electrons in the metals, then the measurement is only limited to the acquisition device speed (100 kHz in this study). There is no more time delay of thermal conduction to the hot junction, because the workpiece is part of the junction. 3-The use of a single leg thermocouple enable to have access to the temperature on a surface equivalent to the size of the wire (25 µm). The material and the location of the return loop is carefully chosen to minimize the interferences.

EXPERIMENTAL SETUP

Cutting forces and temperature measurements

The experimental setup is described in Fig. 1. It consists of a Kistler type 9257 B dynamometer, a National Instruments 9211 module for cold junction temperature compensation, two 9215 National Instruments module for the hot junction temperature measurements and to acquire the three cutting forces. All acquisition cards were synchronized using a National Instrument 9188 slot and set with a sampling rate of 100 kHz. The 9211 module is limited to 14 Hz but was involved only to avoid offset problem in case of global part temperature variation far from the machining area. The thermocouple used for temperature measurement is a type K+ with a diameter of 25 μm. To avoid measurement uncertainty related to distortion in thermal field when inserted thermocouple [Attia 2002], the K+ wire is uncoated and directly welded on the workpiece near the cutting area. In order to split the thermocouple K+/K-, it is necessary to establish it is necessary to establish the absolute Seebeck EMF (measured electromotive force) for the materials used in the assembly as the part material acts in the thermocouple composition. Fig. 2(a) shows the electromotive forces (EMF) generated by the different pairs of materials used in the experimental device created by Seebeck effect. Fig. 2(b) shows the calculation required for the temperature measurement at the interface between the Ti-6Al-4V and the miniaturized thermocouple K+. The EMF of interest here, being the one allowing to compute the exact temperature at the TC welding point, is the sum of each electromotive forces generated by the couples of material. (a)

V1 Cu/K+ + V2 K+/TA6V + V3 TA6V/K-+ V4 K-/Cu = EMF V2 K+/TA6V = EMF -(V4 K-/Cu + V1 Cu/K+ ) -V3 TA6V/K- V2 K+/TA6V = EMF -(V4/1 K-/K+ ) -V3 TA6V/K- T = f (EMF)K+/TA6V T = f (EMF)K+/K- T = f (EMF)TA6V/K- T K+/TA6V = EMF -(T K-/K+ ) -T TA6V/K- (b)

Separated legs thermocouple calibration

In order to establish the thermoelectric transformation equations for our single leg thermocouple, a calibration setup was used and shown in Fig. 3 to determine the relation between the tension and the temperature. The reference cold junction is immersed in stirred frozen bath of distilled water at atmospheric pressure. This bath method fixes the temperature reference Tref = 0° C. Multiple couples of workpiece material, as presented in Fig. 4 (a) -(b), and K thermocouples are tested and fitted with a six-order polynomial function according to eq. ( 1) :

Cold junction

Ti-6Al -4V Bar

T = ∑ a i . V i 6 i=0 (1)
With T the temperature of the measuring point (°C), and V the corresponding voltage (mV). For Ti-6Al-4V workpiece material, the identified coefficients of six-order polynomial function are described in Tab. 1.

Cutting conditions for orthogonal cutting tests

Cutting forces quantification is necessary to establish the intensity of the heat fluxes flowing into the machined surface. Indeed, the energy consumption Pc during cutting is the product of the cutting speed Vc and the cutting force Fc according to equation (2).

P c = F c × V c (2)
Therefore, it is possible to define heat Qw carried away by the workpiece as expressed in equation ( 3). As shown in Fig. 5, measured forces (Ft and Fr) includes both the forces due to shearing effect on the primary shear zone, and the forces in tertiary deformation zone due to ploughing and spring back phenomenon behind the cutting edge. In consequence, as exposed in [Armarego 1985], the global cutting forces are decomposed into a cutting effect and an edge effect described in equations ( 4) and ( 5):

Q W = B × P c (
Ft = Ft c + Ft e (

Experimental Cutting temperatures in orthogonal cutting

The temperature beneath the machined surface is measured with the very thin K+ thermocouple (25 µm diameter) welded on the side of the specimen as presented in Fig. 8. Successive cuts at a constant uncut chip thickness h allow building the thermal gradient below the machined surface; and this had been made for different cutting conditions, i. The increase in temperature is very localized. Indeed, for all cutting conditions, only the first 100 µm suffer a significant temperature increase of at least twice the workshop temperature. Due to low thermal conductivity and thermal diffusivity, the affected depth never exceeds 200 µm for all cutting conditions. The cooling speed of the surface is very low compared to the heating speed. show nevertheless that the surface temperatures in 3D regime are slightly higher that can be due to thermal cycling.

Experimental heating rates in orthogonal cutting

Thanks to the very low response time of the thermocouple, the heating rate during temperature increase becomes accessible. For the cutting conditions A5 and B5, respectively shown in Fig. 11 (a) and (b), the temperature and heat velocity have been measured into the primary shear zone with high accuracy. The temperature intensity measured experimentally inside the primary shear zone can be compared to prior work of [Cotterell 2008] where they predicted analytically the average shear band temperature θs using [Shaw 2005] thermal equations.

Fig. 12: Predicted shear band and rake face temperatures [Cotterell 2008] From Fig. 12, and at least for Vc = 120 m/min, the experimental temperature rise measured in this work inside the PSZ is in agreements with the predicted temperatures arising from Shaw's theory.

More recently, [Bai 2017] models the PSZ by a non-zero and asymmetric thickness band, and shows by analytical computation, and by integrating a modified JC law [Calamaz 2008], that the mean temperature for a cutting condition h=0.1mm and Vc=120m/min vary from 20°C to 350°C. Numerical simulations show that the temperature into the PSZ varies, for this same condition, from 20°C to 700°C near the tool-chip interface.

DISCUSSION

Influence of the uncut chip thickness on the surface heat generation

The temperature measurements show that the surface temperature increases gradually with the uncut chip thickness decrease, as show in Fig. 10. This is correlated with the feed rate from a global machining point of view. This phenomenon is driven by two mechanisms. The first one is the shear angle φn dependency with h, which decreases gradually with the primary shear plane, modelled by a moving heat band, is closer to the final surface. To resume, at equal thermal energy, the thermal diffusive power of the primary shear band is increased by a higher shearing plan angle.

The second mechanism responsible for the temperature increase is the ability to evacuate the thermal energy within the chip, rather than in the surface. The greater the thickness of the chip, the more calories are evacuated.

Influence of the cutting edge geometry on the surface heat generation

The present study shows that the rake angle has an important influence on the cut stability at low tool engagement (Fig. 7), but also that the temperature increases as the rake angle decreases (Fig. 10). Indeed, decreasing the rake angle decreases the shear angle, bringing the shear band closer to the finished surface. In addition, the decrease of the rake angle intensifies the shear deformations γ and its associate deformation rate dγ dt in the primary shear zone, leading to a temperature increase. In addition, the springback, important with titanium alloy such as Ti-6Al-4V, causes a temperature increase due to the intense friction generated along the tool flank. This phenomenon can be assimilated to a moving heat band shown in Fig. 13.

CONLUSION

In this research work, a new experimental setup for punctual temperature measurement using a single miniaturized thermocouple was developed and implemented to characterize the temperature and the heating rates as a function of the cutting parameters, such as the uncut chip thickness and the rake angle. The experimental results showed that:



The surface temperature is highly dependent on the uncut chip thickness. Thus, a decrease in the uncut chip thickness systematically causes an increase of the surface temperature. Therefore, particular attention must be paid concerning the selection of the feed rate fz during machining, especially during the finishing machining operations,



The cutting edge geometry has a significant influence on the cutting stability as well as on the temperature rise of the machined surface. Decreasing the rake angle results in an increase of the surface temperature,  Therefore, tools with a reduced rake angle should always be used in combination with high feed rates. This is a favourable case for roughing operations. Conversely, operations with low cutting engagement must always use special cutting angles to ensure good cutting stability and low thermal load on the machined surface.

Further investigations, based on these experimental results, will be carried out in order to identify the equivalent heat flow involved in machining as a function of a wide range of process parameters. This work will identify the major contributors to surface integrity deterioration and will allow the selection of the most efficient set of parameters to guarantee surface reliability.

Fig. 1 :

 1 Fig. 1: Experimental scheme of the setup

Fig. 2 :

 2 Fig. 2: (a) Schematization of thermoelectric couples -(b) Thermoelectric transformation equations

Fig. 3 :

 3 Fig. 3: Thermocouple calibration principle Junction temperatures are measured using a National instrument 9211 module. Temperature variation are recorded while hot junction is subjected to temperature variations from -200 °C to 900 °C.

Fig. 4 :

 4 Fig. 4: Thermoelectric functions for several couple of material. (a) "Material / K+" (b) "Material / K-"

  3) With B the total partition energy (%), depending of kinematic parameters and thermal diffusivity of the material. Orthogonal cutting tests are performed on a DMG DMC 85V CNC machine equipped with linear motors. The material used is a hot rolled and annealed Ti-6Al-4V. Mechanical properties are listed in Tab. 3.

Fig. 5 :

 5 Fig. 5: Serrated chip formation scheme for Ti-6Al-4V orthogonal cutting and cutting forces decomposition

Fig. 7 :

 7 Fig. 7: Cutting forces for: (a) γn = 0° and (b) γn = 20°

Fig. 8 :

 8 Fig. 8: Tool cuts scheme with theoretical thermal distribution arising from an analytical solution and the thermocouple legs (K+ and K-) locations on the workpiece

Fig. 9 :

 9 Fig. 9: Temperature beneath machined surface measured with K+ thermocouple : (a) h = 20 µm & γn = 20° -(b) h = 100 µm & γn = 20° -(c) h = 20 µm & γn = 0° -(d) h = 100 µm & γn = 0° Fig. 10 summarizes the maximal recorded temperatures located at a distance of 10-20 µm from the machined surface depending on the rake angle and the uncut chip thickness. The maximum reached temperature is highly dependent of the uncut chip thickness and the rake angle. These temperature gaps are justified in section 3.

Fig. 10 :

 10 Fig. 10: Maximum temperature depending on the rake angle and the uncut chip thicknessMore recently,[START_REF] Sun | An experimental investigation of the influence of cutting parameters on cutting temperature in milling Ti6Al4V by applying semi-artificial thermocouple[END_REF]] performed peripheral milling cutting tests, recording surface temperature with a semiartificial thermocouple. They point out that for small uncut chip thickness, between 40 µm to 0 µm (down milling configuration), the surface temperature vary from 350° to 422° for a 10° rake angle tool and Vc = 120 m/min. These results, of the same order of magnitude as the results obtained through orthogonal cutting tests in this study,

Fig. 11 :

 11 Fig. 11: Temperature and heating rate beneath the machined surface and into PSZ for: (a) h = 100 µm and γn = 0°, and (b) h = 100 µm and γn = 20°

Fig. 13 :

 13 Fig. 13: Moving heat sources scheme

ACKNOWLEDGMENT

The authors would like to thank the involvement of Airbus and the OMIMI manufacturing R&T team in the project, especially Samuel DROUVROY and Anne Laure LAFLY. The authors acknowledge as well as the IACM team, especially Laurent PINTO for their valuable technical support.


Adrien Toesca

Damien David

André Kuster

Michel Lussault²

Kévyn Johannes

A new building thermal model to a better evaluation of the thermal comfort during heat waves

Keywords:

This paper is about urban building thermal simulation during summer and heat waves conditions. Summer building thermal behavior are already well performed by popular building simulation software such as TRNsys and EnergyPlus. The effect of the urban fabric could be simulated by an Urban Canopy Models (UCMs) or a District Scale Models (DSMs) but the coupling between these models and a building simulation software requires a high expertise and is not computer efficient. This paper focuses on the construction of a simulation chain of tools that is computer efficient and do not require high expertise to run simulation. The methodology is based on in-situ measurements. The paper describes how the in-situ measurements setup was designed to provide sufficient information to enhance the model settings. It also shows the good accuracy of the model based on the measured data.

Introduction

With global warming, climatic events such as heatwaves will be longer, more frequent and more intense. This issue will be more critical in cities since the heat waves are accentuated by the phenomenon of urban heat islands (UHI). Air temperatures in cities are likely to be increasingly unbearable. Extreme urban temperatures will impact a fair majority of the worldwide population since, according to (The World Bank, 2018), nearly 56 percent of the worldwide population lives in urban areas, with a net increasing trend over the past decades. The extreme temperature will cause discomfort and diseases in the urban outdoor environments and within the urban buildings. This is especially true in buildings which are not equipped with cooling systems. Appropriate building thermal models are needed to predict accurately the comfort of inhabitants in urban buildings under heat waves events, and more generally under hot summer conditions. The purpose of this paper is to propose such an appropriate model. The model is described in the first section of the paper. Its main specificity lies in the fact that it takes into account the impacts of the urban fabric on the thermal loads on the buildings, in a summer weather context. A measurement campaign was conducted in two apartments located in the city of Lyon (France) during the summer of 2020.

Those measurements produced reference data to assess the accuracy of the proposed model. Details about the measurements are given in the second section of the present paper. The confrontation between the measurements and the model's predictions are described in the last section.

Building thermal model

The developed building thermal model must fulfill two requirements: it must model properly all the prevalent heat transfer rates which are characteristic to building thermal behaviors under summer conditions, and it must take into account the effect of the urban fabric surrounding the building.

Building thermal behavior in summer conditions

In the present paper, the focus is done on buildings without cooling systems. During summer periods, the thermal regulation of such buildings is entirely ensured by actions of the occupants [START_REF] Fabi | Occupants' window opening behaviour: A literature review of factors influencing occupant behaviour and models[END_REF]. A distinction is made between three types of actions from the occupant. Actions which directly affect the building thermal characteristics are referred as actions of "type 1". They correspond to the opening of the windows for natural ventilation, and the activation of solar shadings [START_REF] Bui | Impact of the occupancy scenario on the hygrothermal performance of a room[END_REF][START_REF] Hong | How Building Energy Models Take the Local Climate into Account in an Urban Context -A Review[END_REF][START_REF] Mavrogianni | The impact of occupancy patterns, occupantcontrolled ventilation and shading on indoor overheating risk in domestic environments[END_REF][START_REF] Piselli | Occupant behavior longterm continuous monitoring integrated to prediction models: Impact on office building energy performance[END_REF]. Actions which modify the distribution and the amplitude of the internal gains within the buildings are referred as actions of "type 2". Under hot conditions, an occupant might prefer to stay in the cooler rooms, or to avoid the usage of gas cookers [START_REF] Bui | Impact of the occupancy scenario on the hygrothermal performance of a room[END_REF][START_REF] Hong | How Building Energy Models Take the Local Climate into Account in an Urban Context -A Review[END_REF][START_REF] Mavrogianni | The impact of occupancy patterns, occupantcontrolled ventilation and shading on indoor overheating risk in domestic environments[END_REF][START_REF] Piselli | Occupant behavior longterm continuous monitoring integrated to prediction models: Impact on office building energy performance[END_REF]. There are also type 3 actions. These are actions that have no impact on the building thermal characteristics but which allow the occupants to increase their comfort (drinking, changing clothes, resting [START_REF] Bui | Impact of the occupancy scenario on the hygrothermal performance of a room[END_REF][START_REF] Hong | How Building Energy Models Take the Local Climate into Account in an Urban Context -A Review[END_REF]). In summer conditions, solar radiation is one of the major thermal stresses on buildings. The way the solar radiation heats up the building greatly depends on the shading effects of the surrounding solar masks (topography, other buildings, trees, balconies, etc.), and of the windows (optical properties, dimensions, and nature and use of the shading devices). Under summer conditions, the air temperature difference between the indoor and outdoor environment is lower than under winter conditions, but the heat exchanges between indoor and outdoor air are intensified when windows are opened to enable natural ventilation. The cooling heating rate resulting from natural ventilation is very sensitive to the outdoor air temperature and to the wind pressure loads on the facade. The thermal models implemented in popular building simulation software such as TRNSys (Solar Energy Laboratory, 2007) and EnergyPlus (S. Department of energy, 2014) already incorporate features for modelling specific aspects of summer building thermal behavior. They incorporate advanced windows + shadings models with a possibility to provide schedules for the control of those openings by the occupants. They offer a full control of the internal heat production due to the activities of the occupants. They incorporate models of outdoor solar masks. They also incorporate, or they propose in the form of an additional module, an AirFlow Network model (AFN) to model natural ventilation air flows and their thermal effects. However, the thermal models implemented in popular building simulation software do not incorporate features for modelling the effects of the urban environment on the outdoor air temperature and on the building wind pressure loads.

Effect of the urban fabric on the building thermal loads

Popular energy models use weather data to determine the thermal solicitation of the buildings. Available weather data are representative of outdoor thermal conditions in rural areas. However, the urban fabric modifies the outdoor thermal conditions. Those variations happen at two distinct scales: the city scale (gradients over 100 meters length scales), and the district scale (gradients over much shorter distances). At the city scale, the air temperature and humidity variations are designated by the term Urban Heat Island (UHI). They result from modifications of the energy budget at the soil surface, between the rural area where weather data are monitored, and the urban area. At the district scale, the air temperature and humidity variations are due to very local phenomena, such as air stagnation in shaded areas, or hot air discharge from air conditioning systems. At the city scale, the variations of the vertical wind velocity profiles are due to a modification of the soil roughness due to the presence of buildings in the city. At the district scale, buildings form obstacles to wind flow. The wind bypasses these obstacles, resulting in a very inhomogeneous distribution of wind speed and direction in the urban canopy. This complex flow affects the building pressure loads and the external convection heat transfer rate. At the city scale, the variations of incoming solar and thermal radiation heat transfer rates are negligible. At the district scale, the surfaces located near the building of interest form solar mask. They reduce the building visibility to the sky vault, which modifies its outdoor thermal radiative environment.

Existing models dedicated to urban fabric effect

The models used to simulate the city-scale thermal effects are called urban canopy models (UCMs). Those models simulate the energy budget at the surface of the city. A survey of the existing UCMs was proposed by [START_REF] Grimmond | The International Urban Energy Balance Models Comparison Project: First Results from Phase 1[END_REF]. The UCMs can be used online (coupled with a full meso-scale atmospheric simulation tool, which requires a lot of computational resources), or offline (the thermal solicitations of the urbanised surface are directly extracted from a weather data file). The latter option is more computer efficient, but it is less accurate because the temperature evolution of the air located in the urban boundary layer, i.e. above the city, is not modelled. A compromise was developed by (Bueno et al., 2012) who combined an UCM and a simplified thermal model of the urban boundary layer. Their model was implemented in the simulation tool Urban Weather Generator (UWG). District Scale Models (DSMs) are dedicated to the simulation of district-scale thermal effects. They model the heat transfers in the various forms (solar and thermal radiation, convection, conduction, latent…) in and around groups of buildings. The degree of precision of the DSMs can vary greatly. The models with the highest level of precision (SOLENE-Microclimate [START_REF] Musy | The Use of SOLENE-Microclimat Model to Assess Adaptation Strategies at the District Scale[END_REF], EnviBatE [START_REF] Gros | Simulation Tools to Assess Microclimate and Building Energy -A Case Study on the Design of a New District[END_REF], ENVI-Met [START_REF] Bruse | Simulating Surface-Plant-Air Interactions inside Urban Environments with a Three Dimensional Numerical Model[END_REF]) generate a 3D mesh in the air volume to estimate explicitly the whole 3D wind velocity and temperature fields; they also generate a fine mesh of the building surfaces to be able to predict accurately the distribution of the radiative heat transfer rates over those surfaces. The intermediated level of precision (SOLENE, City-SIM [START_REF] Robinson | CITYSIM: Comprehensive Micro-Simulation of Resource Flows for Sustainable Urban Planning[END_REF]Frayssinet, 2018) is attained with models which still mesh finely the outdoor building surfaces for radiative heat transfers, but which use a simplified model to predict the air velocity and temperature distributions. Most of the DSMs require either high computer resources to perform long term simulations, or a strong expertise to establish the district thermal models, or both. Moreover, the building thermal models they contain are limited, in terms of functionalities, compared to the thermal models implemented in the popular building simulation software (especially concerning the windows shading options). DSMs must then be coupled to building simulation software to provide relevant simulation results (Lauzet et al., 2019). The coupling between a DSMs and a building simulation software requires a high expertise. It could be a great value to avoid this kind of model and use a simpler tool, more computer efficient and still precise inside building in order to perform long term simulations (a summer period for example).

The wind distribution at district scale (without buoyancy effect) can be estimated separately using any computational fluid mechanics (CFD) software. Due to the Reynolds invariance (Van Hooff, 2010), only a limited number of simulations are needed to cover all the possible incoming wind speed and direction values. Those simulations are then be performed as pre-processing simulations. The establishment of the CFD model might need some expertise if it is built with a general-purpose CFD software. However, dedicated tools such as UrbaWind [START_REF] Fahssis | UrbaWind, a Computational Fluid Dynamics Tool to Predict Wind Resource in Urban Area[END_REF] were recently developed to ease the model settings.

Description of the tool chain

The proposed model is an assembly of models contained in three simulation tools. The resulting chain tool is represented in Figure 1. The software EnergyPlus is used to simulate the thermal behaviour of the building of interest. To model the natural ventilation effects, this tool uses an Air Flow Network (AFN), which has been successfully compared to experimental data by [START_REF] Gu | Airflow Network Modeling in EnergyPlus[END_REF]. EnergyPlus also models the effect of close and distant solar masks on the building external solar radiation loads. The urban heat island is modelled with Urban Weather Generator (UWG). UWG modifies air temperature and humidity values from the rural weather file to produce an urban weather file. UWG requires an extensive set of informations about the city morphology, and the thermal characteristics of the building population within the city to predict accurately the amplitude of the urban heat island effects. The software UrbaWind is used to determine the pressure coefficients near the building openings. UrbaWind models require a geometrical representation of the urban morphology around the building of interest. Simulations are performed for wind directions varying between 0 and 360 degrees with an increment of 45°. The predicted pressure coefficients are then introduced in the EnergyPlus building model though the interface DesignBuilder. The proposed simulation tool is then a tool chain composed of 3 robust and validated simulation tools. The coupling between the different tools is only a one-way coupling which does not require computational expertise. It takes into account the major urban effects on the outdoor thermal parameters which predominantly affect the building behaviour in summer conditions: the solar shadings at various distance from the building facade, the city-scale variation of the air-temperature (UHI), the cityscale variation of the velocity profile, and the district scale distribution of the wind velocity and its impact on the pressure coefficients.

The main limitations of the proposed simulation tool are the absence of specific models to evaluate the air and surface temperature inhomogeneities in the urban canopy.

To overcome this limitation, a DSMs would be needed which would result in a gap in term of complexity of for the model implementation and in calculation speed.

Measurement Campaign

The simulation tool chain was designed to estimate the indoor thermal conditions in urban buildings under summer meteorological conditions. This simulation tool chain needs to be confronted to measurements values in order to assess the accuracy of its predictions. For this reason, an in-situ measurement campaign was conducted in Lyon during the summer of 2020. This campaign is detailed in this section.

Case studies

In order to cover the diversity of urban fabrics and building designs found in typical French cities, buildings from different construction periods were selected as case studies. Here are presented the measurements results for two apartments included in buildings which correspond to the following construction periods:

• the Industrial period; building from the late 19th century, early 20th century. • The contemporary period; building which are subject to the last thermal regulation in France (RT2012). In this district, the buildings arrangement forms closed blocks, as it is shown in the Figure 2.

The walls of the industrial building are made of concrete, with a thermal insulation on the inside face. The studied apartment is a west facing mono-oriented apartment (in blue in the figure 2). It is facing toward the core volume of the block. It is located on the 6 th floor out of 7. Its plan is given in the figure 3.

Contemporary building

This building is located in the 9 th arrondissement of Lyon.

Here the urban fabric is composed of detached collective buildings (Figure 4). It is less dense than the previous case study, which might result in a higher exposition of the building to solar radiations and wind loads.

The building walls are also made of concrete but there is a higher thermal insulation here. The studied apartment is located on the top floor of the building. It is a crossing apartment (in blue in the figure 4). Its main orientations are North and South. The plan of the contemporary apartment is shown in the figure 5.

Input data for the simulation

The district morphology was extracted from Google Earth which provides 3D representations of urban areas all over the world. The apartment shapes and dimensions were measured in-situ, drawn on the software Revit, and exported to DesignBuilder to build the EnergyPlus models. The wall compositions of the contemporary apartment were directly given extracted from architects plans. The wall compositions of the industrial apartments were deduced from the building wall composition database Tabula [START_REF] Rochard | Bâtiment résidentiels: typologie du parc existant et solutions exemplaires pour la renovation énergétique en France[END_REF]. The rural weather file is provided by Météo France from data measured in the weather station of Lyon-Bron.

For each test case, the UWG model was parametrized with urban data collected over a 300m square area around the building.

In-situ measurements

The in-situ measurement setup followed two purposes.

The first purpose was to monitor the type 1 occupant actions in order to properly parametrize those actions in EnergyPlus (those measurements are identified with a blue dot in the figure 1). The second purpose was to measure some output and intermediate quantities predicted by the simulation chain-tool to evaluate their accuracy (identified with red dot in the figure 1). The resulting sensor locations are given in the figures 3 and 5. Each window is equipped with one contact sensor for the window's opening, and one contact sensor for the blinds. Each door is equipped with one contact sensor. Additionally, an interview was done with one occupant of each apartment at the end of the measurement period, in order to reconstitute the occupant's presence schedule, and to substitute the sensor measurement data in case of measurement failure.

The outdoor air temperature is monitored to determine the relevance of the Urban Heat Island predictions. Each apartment is equipped with one or two shaded outside air temperature and humidity sensors (1 for the monooriented apartment and 1 on each side of the crossing one).

Then to assess the accuracy of the building model predictions, simulation output quantities are measured [START_REF] Piselli | Occupant behavior longterm continuous monitoring integrated to prediction models: Impact on office building energy performance[END_REF][START_REF] Rodler | Thermal behaviour of a building in its environment: Modelling, experimentation, and comparison[END_REF]: the indoor air temperatures and humidity in different living rooms (red dots in figures 3 and 5).

The models and the accuracy of sensors are listed in the table 1. All sensors communicate with a gateway using the protocol ENOCEAN. This gateway transfers the data to an online server using a 4G key.

Simulation Results

Accuracy of UWG predictions

Figures 6 and7 show outdoor air temperature values from the UWG predictions (orange line), from the in-situ outdoor measurements (blue line), and from the rural weather (green line), for both test cases. The figure 6 shows the outdoor air temperature values over the whole measurement campaign. For the industrial building, there was a failure with the outdoor air temperature sensor from the 22 nd of August. Figure 7 focuses on the period between the 8 th of august and the 22 nd of August.

The comparison between the outdoor air temperatures measured in the rural area (green) and in the urban area (blue) reveals the evolution of the UHI effects. The temperatures are similar during the day. The UHI mostly takes place during the night with higher outdoor air temperatures in the urban area. During the night, the amplitude of the UHI varies between 0°C (around August 20 th ) and 5°C. The UHI amplitude is slightly higher at the location of the industrial building (bottom curve) than at the location of the contemporary building.

For the industrial building (bottom curves), the measured temperatures show some peak during the daytimes: those peaks are due to direct solar radiation which heats up the thermal shield of the sensor. Unfortunately, a portion of this heat reaches the temperature sensor. The temperature peaks during daytimes were not observed by the outdoor sensor of the contemporary building, because this sensor was located in a shaded area. UWG clearly overestimates the UHI effects during the night by predicting higher outdoor temperature values. Indeed, night time UWG temperature predictions (orange) are sometimes fairly higher than the outdoor measured temperatures (blue). Some investigations are still ongoing to identify the origin of those overestimations. However, some aspects of the UWG predictions are encouraging. UWG predicts no UHI during the day, which corresponds to the measurement observations. Also, UWG predictions follow quite well the evolution of the UHI amplitude among the nights: during the nights where no UHI was observed, UWG predicts a little UHI, and during the nights when a substantial UHI was detected, UWG predicts a high UHI amplitude. Figure 7: Zoom on the outdoor air temperature for UWG (orange), measurements (blue) and the rural weather file (green) for both case study from the 8 th to the 22 nd of August. Figure 8 shows the cumulated distribution of the absolute error between the UWG predictions and the measured outdoor air temperatures. 75% of the error are below 3°C for both test cases. For the contemporary apartment, the mean error between UWG and the measured outside air temperature is 2,2°C and the RMSE is 2,9°C. For the industrial apartment, the mean error between UWG and the measured outside air temperature is 1,9°C and the RMSE is 2,6°C. Figure 9 shows that the trends of the indoor temperature evolutions are well reproduced by the simulations. The daily averaged simulated temperatures follow closely the daily average measured temperatures for both test cases, and during the whole measurement campaign. The curves do reveal any bias in the simulated temperature. This observation is particularly important because it means that the energy budget involving all kinds of thermal solicitations of the apartments (solar radiation, outdoor air temperature, outdoor thermal radiation, indoor loads) is well reproduced on a daily average basis by the simulation tool. For the contemporary building (top curves of figures 9 and 10), the simulation seems to slightly overestimate the daily amplitude of the indoor temperature. This could be interpreted as an overestimation of the natural ventilation effects, which reduces significantly the indoor temperature during the night. During this period, the windows were opened but the shutters were partially closed, which might have reduced significantly the ventilation air flow. The parametrization of EnergyPlus is particularly delicate for this opening configuration. For the industrial apartment, there is no overestimation of the daily temperature fluctuations: the amplitude of the simulated temperature is in great concordance with the measurements. Figure 11 shows the cumulative distribution of the error between the simulation and the measured living room air temperature. In blue for the contemporary building and in orange for the industrial one. 80% of the errors are below 1,5°C for both. For the contemporary case study, the mean error between the simulation and the measured living room air temperature is 0,95°C and the RMSE is 1,3°. For the industrial case study, the mean error between UWG and the measured outside air temperature is 0,82°C and the RMSE is 0,99°C. For both buildings, the simulated inside air relative humidity follows quite well the measurements. There isn't a presence of an offset and the daily variation of the simulation and the measurement are quite similar. Figure 14 shows the cumulated distribution of error between the simulated and the measured living room air relative humidity. 80% of the errors are below 5% for the industrial building and below 7% for the contemporary one. For the contemporary case study, the mean error between the simulation and the measured living room air relative humidity is 4,3% and the RMSE is 5,6. For the industrial case study, the mean error between UWG and the measured outside air relative humidity is 3,1% and the RMSE is 4,1.

Conclusion

A simulation chain of tools was presented in this paper. It is a simple chain of tools that do not use dynamical coupling, and is computer efficient. Indeed, it is fast and perform long term simulations. It takes into account the major urban effects on the outdoor thermal parameters which predominantly affect the building behaviour in summer conditions. The main limitations of the proposed simulation tool are the absence of specific models to evaluate district scale distribution of outdoor air and surface temperatures. To overcome this limitation, a DSMs would be needed which would result in a gap in term of complexity for the model implementation.

A campaign of in-situ measurements was conducted in Lyon during summer 2020 in order to confront the simulation tool chain with measurement. The accuracy of the tool UWG, which use a rural weather file and a description of the Town to calculate an urban weather file is quite good. Indeed, UWG detects well when there is and isn't an effect of the UHI but it overestimate its effect. The accuracy of the indoor ambiance is really good. For both case studies, the simulated air temperature and humidity follow the measurements. The daily variation is also quite well simulated. This simulation chain of tools is able to determine the indoor ambiance of a building during summer and heat waves conditions. Air temperature and humidity are decisive parameters for the assessment of thermal comfort. With this simulation tool chain, it is possible to evaluate the comfort inside buildings during heat waves and summer conditions.

Figure 1 :

 1 Figure 1: Simulation tool chain (Blue dot represent the measured simulation input data, red dot represents the measurement that are used to test the accuracy of the simulation tool chain)

Figure 2 :Figure 3 :

 23 Figure 2: Urban fabric of the industrial period (The blue rectangle corresponds to the studied apartment)

Figure 4 :Figure 5 :

 45 Figure 4: Urban fabric of the contemporary period (The blue rectangle corresponds to the studied apartment)

Figure 6 :

 6 Figure 6: Outdoor air temperature for UWG (orange), measurements (blue) and the rural weather file (green)for both case study from July to September.

Figure 8 :

 8 Figure 8: Density of the outside temperature error between the UWG weather file and the measured air temperature.Accuracy of indoor ambiance evaluationsThe evolutions of the air temperature in the living room of both case studies are shown in the figures 9 and 10. The data of the figure9are for the whole measurement campaign. The figure10focuses on the period between the 2 nd to the 16 th of September. The measurements are drawn in orange, and the simulation predictions in blue. The outdoor air temperature of UWG are reminded in green lines. Figure9shows that the trends of the indoor temperature evolutions are well reproduced by the simulations. The daily averaged simulated temperatures follow closely the daily average measured temperatures for both test cases, and during the whole measurement campaign. The curves do reveal any bias in the simulated temperature. This observation is particularly important because it means that the energy budget involving all kinds of thermal solicitations of the apartments (solar radiation, outdoor air temperature, outdoor thermal radiation, indoor loads) is well reproduced on a daily average basis by the simulation tool.For the contemporary building (top curves of figures 9 and 10), the simulation seems to slightly overestimate the daily amplitude of the indoor temperature. This could be interpreted as an overestimation of the natural ventilation effects, which reduces significantly the indoor temperature during the night. During this period, the windows were opened but the shutters were partially closed, which might have reduced significantly the ventilation air flow. The parametrization of EnergyPlus is particularly delicate for this opening configuration. For the industrial apartment, there is no overestimation of the daily temperature fluctuations: the amplitude of the simulated temperature is in great concordance with the measurements.Figure11shows the cumulative distribution of the error between the simulation and the measured living room air temperature. In blue for the contemporary building and in orange for the industrial one. 80% of the errors are below 1,5°C for both. For the contemporary case study, the mean error between the simulation and the measured living room air temperature is 0,95°C and the RMSE is 1,3°. For the industrial case study, the mean error between UWG and the measured outside air temperature is 0,82°C and the RMSE is 0,99°C.

Figure 9 :

 9 Figure 9: Simulated (blue), measured (orange) air temperature in the living room and outside air temperature (green) from July to September

Figure 12 :

 12 Figure 12: Simulated (blue), measured (orange) air relative humidity in the living room from July to September

Figure 13 :

 13 Figure 13: Zoom on the living room air relative humidity simulated (blue), measured (orange) from the 2 nd to the 16 th of September

Table 1 :

 1 type of sensors

	Measured variable	Sensor	Accuracy
	Internal temperature	Nodon	±0.16°C
	and humidity
	External temperature	FAFT60 from	±0.3°C
	and humidity	Eltako ekectronics	±5%
	Manipulation of doors	Nodon	-
	and windows
	Manipulation of	TRIO2SYS from	-
	shutters	O2LINE
	The measurements were performed during summer 2020.
	It began on the 3 rd of July 2020, and ended during October
	2020. During this summer season, outdoor air temperature
	over 35°C were measured at the rural station.