Title: 3D Simulation Methodology to Predict Passenger Thermal Comfort Inside a Cabin
text: The vehicle Heating, Ventilation and Air conditioning (HVAC) system is designed to meet both the safety and thermal comfort requirements of the passengers inside the cabin. The thermal comfort requirement, however, is highly subjective and is usually met objectively by carrying out time dependent mapping of parameters like the velocity and temperature at various in-cabin locations. These target parameters are simulated for the vehicle interior for a case of hot soaking and its subsequent cool-down to test the efficacy of the AC system. Typically, AC performance is judged by air temperature at passenger locations, thermal comfort estimation along with time to reach comfortable condition for human. Simulating long transient vehicle cabin for thermal comfort evaluation is computationally expensive and involves complex cabin material modelling. Lattice-Boltzman (LBM) based PowerFLOW solver coupled with Finite element based PowerTHERM solver is employed in this study to simulate long transient soak and Cooldown along with thermal comfort. Additionally, the human thermal physiology is modeled, to account for subjective evaluation of the in-cabin thermal environment. Berkeley comfort model library is available in PowerTHERM. The model takes care of the vasodilation and vasoconstriction effects, based on the external human ambient, along with the effects of clothing and the passenger metabolic rate. Vasodilation and vasoconstriction regulate the blood flow by widening or narrowing the blood vessels depending upon the warm or cold ambient conditions. LBM based flow solver is used to predict convective heat transfer phenomenon for both the exterior and interior of the cabin. The conduction and the radiation effects including the solar loading were solved using PowerTHERM. Physical test is conducted under controlled ambient conditions of climate chamber for a car cabin. Results from the coupled approach correlates well the test results for both hot soaked and cool-down conditions with a significant reduction in simulation time. During the cabin cool-down phase, passenger thermal comfort is predicted using Predictive Mean Vote. This process is further used to study the effect of change in properties of the glazing surfaces for predicting cabin thermal environment like heat ingress and cabin surface and air temperatures. Thermal comfort is also predicted and compared with baseline design. Glazing material sensitivity is carried out for absorbing and reflective glass material and its impact on cabin surface and air temperature and thermal comfort is predicted here. This process is deployed and found useful for predicting vehicle level thermal comfort.