A Sensitivity Study of Occupant Thermal Comfort in a Cabin Using Virtual Thermal Comfort Engineering SAE TECHNICAL PAPER SERIES

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1 -- SAE TECHNICAL PAPER SERIES A Sensitivity Study of Occupant Thermal Comfort in a Cabin Using Virtual Thermal Comfort Engineering Taeyoung Han Delphi Research Labs Linjie Huang Delphi Thermal and Interior Reprinted From: Climate Control (SP-) SAE World Congress Detroit, Michigan April -, Commonwealth Drive, Warrendale, PA - U.S.A. Tel: () - Fax: () - Web:

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3 -- A Sensitivity Study of Occupant Thermal Comfort in a Cabin Using Virtual Thermal Comfort Engineering Taeyoung Han Delphi Research Labs Linjie Huang Delphi Thermal and Interior Copyright SAE International ABSTRACT Simulation of cabin climatic conditions is becoming increasingly important as a complement to wind tunnel and field testing to help achieve improved thermal comfort while reducing vehicle development time and cost. Delphi developed the Virtual Thermal Comfort Engineering (VTCE) process to explore different climate control strategies as they relate to occupant thermal comfort in a quick and inexpensive manner. The comfort model has the ability to predict the local thermal comfort level of an occupant in a highly non-uniform thermal environment as a function of air temperature, surrounding surface temperatures, air velocity, humidity, direct solar flux, as well as the level of activity and clothing type of each individual. In the present study, we used test data to validate VTCE for a Sport Utility Vehicle (SUV) cabin environment and used VTCE to perform sensitivity studies of various vehicle cabin environments on the occupant thermal comfort, including discharge temperature, breath level temperature and air velocity, solar intensity and solar angles. The sensitivity of the occupant thermal comfort, in the present study, is based on an OEM s comfort scale for both summer and the winter rides. INTRODUCTION Occupant thermal comfort is an important concern in the design of a vehicle. However, the tendency to use more glass in vehicle styling, tightening fuel-economy constraints, the change to less efficient environmentally safe refrigerants and reduced condenser air flow, particularly at idle, are significant challenges to the ability to achieve occupant thermal comfort. As a consequence, it has become necessary to develop tools that can predict the impact of various design choices on passenger thermal comfort early in the design process. Predicting the thermal comfort in a vehicle is very complex due to the fast transient behavior of cool-down after a hot soak, and the non-uniform thermal environments associated with highly localized air velocity and temperature distributions, solar flux, and radiation heat flux from surrounding interior surfaces. Analysis tools for the temperature and velocity distributions in passenger compartments coupled with thermal comfort predictions can guide design directions during the early stage of vehicle development process [,,,,,]. Virtual Thermal Comfort Engineering [] was developed jointly with UC Berkeley to predict the passenger compartment thermal environment and passenger thermal comfort. This report describes the validation of VTCE for a full-size SUV cabin environment and sensitivity studies of the effects of various vehicle cabin thermal environments on the occupant thermal comfort, including discharge temperature, air velocity, breath level temperature, and solar intensity and solar incidence angles. In the vehicle environment, many of these parameters are dependent on each other and the relationship among them is complex and not known exactly. This makes an experimental parametric study a nearly impossible task. However, with VTCE tools, the parameters under investigations can be analyzed easily and it is possible to vary only one parameter without influencing other design parameters. The key elements of the VTCE process, as shown in Figure, are described in []. In the present report, we will cover briefly the VTCE process and focus on the validation of the baseline case for the summer and the winter comfort rides for full-size SUV. After the validation of VTCE, we addressed the sensitivities of various vehicle cabin thermal environments on the occupant thermal comfort. The elements of VTCE process will be described in the following sections. Iteration Delphi Passenger Compartment Model Solar Load Radiation View Factors Refrigeration Cycle Analysis D Fl ow and Thermal Analysis Human Physiology Model Thermal Comfort Prediction Figure. Schematic of Virtual Thermal Comfort Engineering Process. Design Iterations

4 VIRTUAL THERMAL COMFORT ENGINEERING Delphi Compartment Model The geometry of the passenger compartment for the purpose of VTCE can be described by key design parameters that can be carefully selected from early stage vehicle architectural design parameters. The Delphi compartment model can potentially cover a wide range of vehicle shapes and sizes, from small sedans to full-size SUVs. The key design parameters, such as A/C outlet location and size, windshield angle, body vent locations, and many other parameters can be varied easily to accommodate potential design changes. Once the compartment model is available, the benefits of the model for developing the HVAC system design are tremendous. Due to readily available water-tight surface geometry from the Delphi compartment model, the mesh generation time can be drastically reduced compared to the traditional CFD process. The Delphi compartment model for a full size SUV baseline case is shown in Figure. Figure. Delphi Compartment model representation of a full-size SUV. Solar Load The solar load for the vehicle compartment is dependent on glass properties, solar incidence angle, and incident solar spectrum. The absorptivity, transmissivity, and the reflectivity of the glass vary depending on the incident angle of the sun and the wavelength distribution of the incident solar radiation. The solar intensity varies depending on the time, date, location, and vehicle orientation. The overall solar intensity can be obtained from the NREL's SOLPOS code [] and NREL s hourly solar database. This solar load program keeps track of the reflection from the glass, absorption by the glass, transmission into the cabin, and also incident radiation on the occupants in the cabin. The amount of solar load absorbed by the occupant influences the thermal comfort of the occupant by increasing exposed clothing and skin temperatures. A database of various automotive glass properties has been incorporated in VTCE, which allows the effect of solar absorbing and reflecting automotive glasses on thermal comfort can be assessed. Figure shows the solar load distribution on the glass and the interior surfaces of a full-size SUV cabin directly under the sun (solar angle altitude, azimuth) with w/m solar intensity. Figure. Solar load on the glass and on the cabin interior of a full-size SUV. Radiation Heat Load Radiant heat exchange occurs between the occupant and its surroundings. During vehicle cooldown and warm-up processes, the radiation heat load has roughly the same influence as air temperature on occupant thermal comfort. Using a realistic -D model of the occupant, we calculate the view factors between the polygons that define the occupant and the cabin interior surfaces, as shown in Figure. Typically, the total number of polygons needed for CFD analysis of a cabin interior is too many for view factor calculations due to extremely large requirement for computer CPU memory. This problem is made tractable by grouping the polygons to form relatively large interior surface patches. Each of these large interior surface patches is described by its position, surface area, temperature and emittance. This approximation is significantly more accurate than the Mean Radiant Temperature(MRT) [] method. The MRT is an approximate method which is more applicable to uniform thermal environments. The heat gain/loss by radiation from the occupant is computed using view factors between the occupant and the surrounding interior surfaces. Figure. Surface polygons for the occupants and the cabin interior. S

5 Discharge Temp, C Refrigeration Cycle Analysis The system airflow rate and the discharge air temperature for cool-down and warm-up analysis can be measured from tunnel test as shown in Figure or can be specified from the simulation of the refrigerant cycle system [], as shown in Figure. The system airflow rate and the discharge air temperature at the A/C and heater outlets provide boundary conditions for -D flow and thermal analysis of passenger compartment. H Soak KPH Soak st City nd City rd City th City KPH-Rec KPH-Rec KPH-Rec Figure. Typical discharge temperatures during the summer driving cycle. KPH-Rec mph mph City driving Re ci rcul ation Outside Air OEM Summer Driving Cycle KPH-Rec KPH-Rec KPH-Osa KPH-Osa KPH-Osa Front-AC Rear-AC KPH-Osa KPH-Osa st idle nd idle Idle rd idle cabin and around the occupants. Figure shows airflow distributions around the chest of a passenger and pathlines from the A/C outlets. Figure shows the interior temperature distribution in the full-size SUV cabin after minutes of cool-down after a hot soak. The accuracy of these simulations for a simplified passenger compartment was described in our previous work []. In order to simulate cabin thermal environments, we need to specify the thermal environment around an occupant, such as air velocity, air temperature, and radiation load around body segments corresponding to our human physiology model. The boundary conditions around the occupant are obtained from the Fluent CFD analysis. As shown in Figure, these two simulations (-D CFD analysis and human physiology simulation) need to exchange heat transfer between the cabin and the occupant during the thermal comfort simulations. Currently, Fluent code exchanges air temperatures and air velocities around occupant body segments with the human physiology model at every specified time interval during Fluent CFD analysis. The solution converges very quickly since the thermal interactions between the occupants and the cabin thermal environments are not strong in the present application. While the thermal interactions between the occupant and the cabin environments are relatively weak, occupants in the cabin significantly disturb the flow directions in the cabin and therefore, affect the overall flow and air temperature distribution in the cabin. Figure. A typical refrigerant cycle system. Cabin Thermal Environment The cabin thermal environment can be computed directly from -D flow and thermal analysis. The compartment CAD geometry was generated directly from the UG model shown in Figure. This CAD geometry was directly imported to Gambit, the Fluent pre-processor, and the time and therefore, the effort for preparation of clean surface geometry for -D mesh generation was significantly reduced. For CFD analysis, the physical domain of the compartment was subdivided into finite volumes, as shown in Figure. Then, the Reynolds-averaged Navier-Stokes equations were solved simultaneously with the conservation of energy equation to predict airflow, temperature, and humidity distribution around occupants. CFD analysis provides detailed airflow distribution information for the Figure. Air flow distribution in a full-size SUV cabin. Figure. Surface K temperature distribution in a full-size SUV.

6 Physiological Model The current human physiology model can simulate an arbitrary number of body segments. Each of these segments consists of four body layers (core, muscle, fat, and skin tissues) and a clothing layer. A separate series of nodes, representing the arteries and veins, provide for convective heat transfer between segments and tissue nodes and the countercurrent heat exchange between the arteries and the veins. Human body thermal regulation is mainly achieved by regulating blood flow, so a realistic blood flow model is important for any dynamic model of human thermal comfort. The body uses vasoconstriction and vasodilatation to regulate blood distribution in order to control skin temperature through an increase or decrease of heat loss to the environment. Veins and arteries are paired, even down to very small vessels, and veins carry heat from the arteries back to the core. The details of this human physiology model are described in [,]. The model is able to predict both core and extremity skin temperatures with reasonable accuracy under a range of environmental conditions. Detailed validations for transient conditions can be found in [].. Clothing Model The current model includes a clothing node to model both the heat and moisture capacitance of clothing. Heat capacity of the clothing is important when considering transient effects []. Moisture capacitance is important to correctly model evaporative heat loss from the body through clothing. The moisture model uses the regain approach [] to calculate the amount of moisture that a specific fabric will absorb at a given relative humidity.. Contact surfaces In almost any environment, the body is in contact with solid surfaces and loses or gains heat via heat conduction. In the vehicle, the seat contacts a considerable fraction of the body and must be considered to accurately model the occupant. The current model includes a contact surface for each body segment. The thermal properties of the contact surface are used to simulate its surface temperature. Each body segment includes the fractions of exposed skin and clothed skin in contact with the surface.. Physiological variation Human physiology varies significantly among individuals, and these differences can affect perceptions of thermal comfort; e.g., higher metabolic rate or increased body fat can cause people to feel warmer. The present model in VTCE maps six descriptive characteristics of the human body (height, weight, age, gender, skin color, and body fat) to the physiological data used by the comfort model. The simulations show that a change in body fat from % to % can result in a skin temperature change of nearly C. In the present study, a standard Stolwijk physiology model [] was used for the human physiology with a metabolic rate of w/m. Standard summer clothing (Clo=.) was specified for hot soak and cool-down simulations and standard winter clothing (Clo=.) was specified for winter warm up simulations. Thermal Comfort Model The human sense of thermal comfort is very complex, and involves both the physiological and the psychological states of a person under specific conditions. Bohm [] accepted the Equivalent Homogenous Temperature (EHT) proposed by Wyon [] for assessing non-uniform environments and developed limits for thermal comfort. We calculate EHT for each body segment from the human physiology model and generate a diagram that plots these together with the comfort limits established for body segments by Bohm [], as shown in Figure. A statistically determined comfort range between the cold and warm borderlines, in which % of the people would feel comfortable, is indicated by the bold lines shown in Figure for body segments. In a previous study [], we developed a model to correlate the overall EHT scale to a particular OEM s comfort rating. This OEM s comfort ratings are based on a scale from to as shown in Figure. Summer ride Comfort Range (% satisfied) Head Chest Back Pe lvis Lft upr arm Rht upr arm Lft forearm Rht forearm Lft hand Rht hand Lft thigh Rht thigh Lft calf Rht calf Lft foot Rht foot o EHT C Figure. EHT index for body segments for the baseline case near the end of the summer comfort ride. Cold Too Cool Cool Slightly Comfortable Cool Slightly Warm Warm Figure. OEM s comfort scales ( is cold, is comfortable, and is hot). Too Warm Hot

7 VALIDATION OF VTCE In the present study, we first validated our VTCE to summer and winter comfort ride test data using a fullsize SUV vehicle. Thermal comfort ratings were voted by human subjects during soak and cool-down/warm-up vehicle comfort ride tests. Once we the overall EHT values are predicted from the coupled CFD and human physiology analysis we can obtain the OEM s comfort ratings from the correlation model developed previously []. The details of OEM s data were described in []. The measured discharge temperatures, as shown in Figure, decreased very rapidly when the A/C system was turned on. The discharge temperatures varied considerably with vehicle operating conditions during the comfort ride test. The measured discharge temperatures at A/C outlets were specified as boundary conditions for Fluent CFD analysis. Breath level temperatures and the cabin interior thermal environments, including solar load, were simulated with Fluent. Breath level temperatures vary with changes in the discharge temperature during the vehicle comfort ride. As shown in Figure, the predicted breath level temperatures were compared from the thermocouple data measured during the comfort ride. As shown in Figure, the breath level temperatures agreed very well with the present analysis. Accurate prediction of EHT values for an occupant depends on the accuracy of predicting cabin interior thermal environments, such as solar load, air velocities, breath level temperatures, and surrounding interior surface temperatures in a cabin. As shown in Figure, the predicted OEM s comfort scales for the summer ride agree well with the human subject data. The measured discharge temperatures, shown in Figure, were also specified for Fluent analysis of the winter ride. As shown in Figures and, the overall accuracy for breath level temperature and subject comfort rating is very similar to the results for the summer ride. The overall predictions for occupant thermal comfort for the summer and the winter rides are within a quarter scale accuracy. Discharge Temperature o C St art Soak st City CT CT mph nd st mph. Idle CT CT st nd Idle mph mph min min sec Driving (Recirculation) mph mph (Outside Air) Summer Weather Driving Cycle Full size SUV Figure. Measured discharge temperatures during the summer comfort ride. Idle Idle.min Breath Level Temperatures o C Test data CFD Aanlysis Soak City mph mph Idle Driving (Recirculation) (Outside Air) Summer Weather Driving Cycle Figure. Comparison of the front breath level temperatures between the simulation and the thermocouple data. OEM's Comfort Rating Discharge Temperature o C Comfort Votes Prediction Star Soak st sec City CT CT mph mph nd st mph e mi Idle t CT CT st mph nd mph Idl e. n mi n Driving (Recirculation) (Outside Air) Summer Weather Driving Cycle Figure. Prediction of the comfort ratings during the summer comfort ride. Full-size SUV - Soak mph mph Idle Cold Weather Driving Cycle Idl e.mi n Figure. Measured discharge temperatures during the winter comfort ride.

8 Breath Level Temperature o C OEM's Comfort Rating - - Soak mph mph Idle Cold Weather Driving Cycle Figure. Comparison of the front breath level temperatures between the simulation and the thermocouple data. Figure. Prediction of the comfort ratings during the winter comfort ride. SENSITIVITY STUDIES Test Data CFD Analysis min. st st nd Lap rd t h Lap Laps Laps. min.. min.. min. Soak mph mph Idle idle Lap Lap Cold Weather Driving Cycle Comfort Votes Prediction For the baseline case of the summer ride, a direct solar intensity of w/m and a diffused solar intensity of w/m were specified with an incidence angle at a noon time (which is degrees Altitude and degrees Azimuth). The computed solar load distribution on the full-size SUV is shown in Figure. The transmitted solar load on a driver is shown in Figure. Due to the vertical solar incidence angle, most of the occupant body surfaces were blocked from direct solar load by the roof of the vehicle. Only occupant hand and feet areas were impacted by direct solar load, as shown in Figure a. The effect of solar incidence angle influences the local thermal comfort and can produce higher EHT values at various body locations. Due to relatively small solar load for the baseline case, the overall comfort level for this case was near a thermally comfortable region corresponding to an OEM comfort scale rating of.. This same comfort scale value occurred at the end of the comfort ride test, as shown in Figure. In the following, we will discuss the effects of various passenger compartment climate conditions, such as breath level temperatures, air velocity, and solar incidence angle and solar intensity on occupant thermal comfort. Effects of Solar Load and Incidence Angle The transmitted solar load on the driver is dependent on the solar properties of the glass. The effect of different glass properties on the occupant thermal comfort is given in []. In the present study, we are interested in the effect of different solar intensity and various solar angles on occupant thermal comfort for a given set of glass properties. In order to understand the effects of solar load on occupant thermal comfort, we simulated the cases for no solar load, low solar intensity ( w/m ), and high solar intensity ( w/m ) (the baseline case). We also looked at a solar angle of altitude, - azimuth and compared these results with the vertical incident baseline case. As shown in Figure, the solar loads on the driver vary largely with solar intensity and solar incident angles. As expected, the effects of solar load are important for the body segments where there are direct solar load. The corresponding simulated EHT values for the body segments are shown in Figure. The body segments blocked by roof, IP and side doors are relatively insensitive to the solar load. EHT values for both hands were significantly lower for the case of no solar load, as shown in Figure. In particular, the EHT value for the left hand decreased by roughly o C compared to the baseline case. For the case of the altitude, - azimuth solar incidence angle, the right lower arm and the left upper arm areas had fairly high EHT values ( o C and. o C respectively) due to direct solar heating. It is a challenge to reduce asymmetric thermal load on cabin occupants in the case of extreme solar gain. (w/m ) w/m w/m w/m (a) (, ) degrees (b) (, -) degrees (c) (, -) degrees Figure. Solar load on the driver for different solar incidence angle and solar intensity.

9 The OEM thermal comfort levels as a function of solar load simulated are shown in Figure. As shown, the OEM comfort ratings increases fairly linearly with the total solar load on the driver. Figure. Thermal comfort diagram for the effects of solar incidence angle and solar intensity. OEM's Comfort Rating Head Chest Back Pelvis Lft up Rht up Lft for Rht fo Lft ha Rht ha Lft thi Rht th Lft calf r arm r arm earm rearm nd nd gh igh Rht calf Lft foot Rht foot Baseline No solar load Solar Angle (,-) Low Solar Intensity ( w/m) Comfort Range (% satisfied) EHT o C VTCE simulation Total Solar Load on Occupant (w) Figure. Effects of solar load on the OEM s thermal comfort ratings. Effects of Breath level Temperatures The breath level air temperature and air velocity around a driver directly influences thermal comfort. The effects of breath level temperatures were simulated by assuming that the other thermal environment variables, i.e. Solar load, air velocity, and surrounding interior surface temperatures, are the same as the baseline case. Breath level temperatures were increased and decreased by o C to assess its effect on the thermal comfort. As shown in Figure, the o C higher breath level temperature influenced most of the body segments OEM's Comfort Rating except the back and near the pelvis, which directly contacted the seat. EHT for the most part of the body segments increased by roughly. o C when the breath level temperature increased by o C. Figure. Thermal comfort diagram for the effects of breath level temperatures. The OEM s thermal comfort ratings were plotted against the breath level temperature increments as shown in Figure. The OEM s comfort ratings increases fairly linearly with increasing breath level temperature. Roughly, a o C increase in the breath level temperature corresponds to a. increase in the OEM comfort rating Head Chest Back Pelvis Lft upr arm Rht upr arm Lft forearm Rht forearm Lft hand Rht hand Lft thigh Rht thigh Lft calf Rht calf Lft foot Rht foot Baseline Low Breath Level T (- C) High Breath Level T (+ C) Comfort Range (% satisfied) EHT o C VTCE simulation Breath Level Temp Increments ( o C) Figure. Effects of the breath level temperatures on the OEM s thermal comfort ratings.

10 Effects of Air Velocity Figure shows the effect of air velocity magnitude around a driver on the thermal comfort, based on EHT. The effects of the air velocity were simulated by assuming the other thermal environment variables, such as solar load, breath level temperature, and surrounding interior surface temperatures were the same as the baseline case. The air velocity was increased and decreased by. m/s to assess the effects of the air velocity on the thermal comfort. As shown in Figure, the increase of air velocity by. m/s influenced most of the body segments except the back and near the pelvis, which directly contacted the seat. The average EHT for the body segments increased by roughly. o C when the air velocity decreased by. m/s and decreased by roughly. when the air velocity was increased by. m/s. A large effect of air velocity was found on the head, arm, and hand body segments. Very little effect occurred for pelvis, back, and thigh because these body segments were in contact with the seat. The corresponding OEM s thermal comfort levels for the various air velocity increments are shown in Figure. As shown, the air velocity and the OEM comfort ratings are slightly nonlinearly related. This is due to the no-linear nature of the convection heat transfer between the occupant and the surrounding air. The comfort rating increased by. when the air velocity magnitude decreased by. m/s and decreased by about. decrease when the air velocity magnitude increased by. m/s. Overall, as expected, the comfort ratings were improved as the air velocity magnitude increased around the occupant. Head Chest Back Pelvis Lft upr arm Rht upr arm Lft forearm Rht forearm Lft hand Rht hand Lft thigh Rht thigh Lft calf Rht calf Lft foot Rht foot Baseline High air vel (+. m/s) Low Air vel (-. m/s) Comfort Range (% satisfied) EHT o C Figure. Thermal comfort diagram for the effects of air velocities. OEM's Comfort Rating VTCE simulation Air Velocity Increments (m/s) Figure. Effects of the air velocity on the thermal comfort ratings. Effects of Discharge Temperatures Lower discharge temperatures help to cool down a cabin interior more quickly for a given HVAC system flow rate. Different A/C systems can produce quite different system performances for the discharge temperature and, therefore, different thermal comfort levels. In the present study, we are interested in the effects of two different discharge temperature scenarios with the same total system airflow rate on thermal comfort during the summer ride. As shown in Figure, the A/C system for Discharge- produced a relatively poor performance during city driving and at idle compared to the A/C system for Discharge-. At relatively high vehicle speeds ( and mph), two A/C systems had very similar discharge temperatures, as shown in Figure. Discharge- produced about ~ o C lower discharge temperatures than that of Discharge- during city driving and at idle. These cases were simulated for the full-size SUV cabin with a total solar intensity of w/m and a vertical downward solar incidence angle. The simulated flow and the interior temperature distributions were already shown in Figures and. The simulations were performed with passengers in the cabin. However, in the present report, we will discuss only the front occupant breath level temperatures and thermal comfort. The predicted front breath level temperatures for the two different discharge temperature scenarios are compared in Figure for the summer ride. As shown in Figure, the breath level temperatures for Discharge- produced relatively higher temperatures during most of the comfort ride and this resulted in relatively poor thermal comfort levels. A roughly o C lower discharge temperatures ( Discharge- ) at idle operating conditions resulted in roughly a. improvement of OEM comfort rating. This is considered to be a significant improvement in the occupant thermal comfort.

11 Front Discharge Temperature o C Summer Weather Driving Cycle Discharge- Discharge- Soak City mph mph Idle Driving (Recirculation) (Outside Air) Figure. Two different discharge temperature scenarios during the summer comfort ride. Front Breath Level o C Discharge- Dischrage- Soak City mph mph Idle Driving (Recirculation) (Outside Air) Summer Weather Driving Cycle Figure. Simulated breath level temperatures for two different discharge temperatures. Front Comfort Rating Discharge- Discharge- Soak City mph mph Idle Driving (Recirculation) (Outside Air) Summer Weather Driving Cycle Figure. Prediction of the comfort ratings for two different discharge temperatures during the summer comfort ride. CONCLUSIONS VTCE is suitable for the evaluation of heat load and occupant thermal comfort in a vehicle cabin. This simulation tool allows the rapid assessment of various parameters with respect to thermal comfort during the early stage of vehicle development. The following conclusions are made from the present study:. Delphi VTCE (Virtual Thermal Comfort Engineering) can be used to simulate Equivalent Homogeneous Temperature (EHT) values for various cabin thermal environments. These values, in turn, can be used to predict the occupant thermal comfort in terms of the OEM s comfort ratings.. The present full -D flow and thermal analysis takes into account the geometrical configuration of the passenger compartment, including glazing surfaces and pertinent physical and thermal properties of the enclosure.. Accurate predictions of airflow and temperature distributions in the cabin are crucial to the success of VTCE. The full -D CFD analysis of soak and cool-down simulations demonstrated excellent agreement with experimental data for a full-size SUV.. The present thermal comfort model for VTCE was validated using an OEM s thermal comfort ratings for the summer and the winter ride tests.. VTCE can be used to explore different climate control strategies in the early stage of vehicle development without the need for timeconsuming vehicle level test. AKNOWLEDGEMENT The authors would like to thank Charlie Huizenga, Zhang Hui, and Edward Arens from UC Berkeley for many useful discussions. The project support of Prasad Kadle and Jim Giardino at Delphi Thermal & Interior and Mark Krage and Linos Jacovides at Delphi Research Labs are also gratefully acknowledged.

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