Advance Cabin Simulation in 1D CAE to Predict Occupants Nose Level Air Temperature 2022-28-0387
Mobile Air Conditioning (MAC) system provides year round thermal comfort to the occupants inside vehicle cabin. In present scenario, 1D CAE simulation tools are widely used for MAC system design, component sizing, component selection and cool down performance prediction. The MAC component sizing and selection mainly depends on cooling load which varies with ambient conditions, occupancy, cabin size, geometry and material properties. Therefore, detailed modeling of vehicle cabin is essential during MAC system digital validation as it helps to predict performance across wide number of contributing factors.
There are two different methods available in 1D Simulation for vehicle cabin modeling, viz. ‘simple cabin’ and ‘advance cabin’. With the simple cabin modeling approach, vehicle cabin is modelled as a group of lumped masses, which only enables prediction of average vent and average cabin temperatures. In advance cabin modeling approach, vehicle cabin is modelled more comprehensively. The input parameters for Advance Cabin Model (ACM) are vehicle geometry, thermo-physical properties of cabin material, conditioned air mass flow and diffusion field generated in 3D CFD. ACM enables prediction of, individual AC vent and occupant nose level air temperature which is a closer representation of the real world scenario. Hence with advance cabin modeling, prediction of cabin air temperature distribution is possible in 1D CAE.
This paper documents the approach followed to model advance cabin in 1D CAE for two row car cabin. 1D KULI simulation software is used to perform the simulation. After incorporation of detailed cabin inputs, conditioned air mass flow and diffusion field, a series of iterations were conducted to correlate the transient AC vent and occupant’s nose level air temperatures with physical test results. Accuracy of 96% for occupant’s nose level air temperature prediction was achieved for severe ambient condition. Along with conditioned air temperature distribution, refrigerant suction and discharge pressures were well correlated with physical test results in this study. Future work will be to analyze the impact of cabin insulation thickness and glass inclination angle on cabin air temperature distribution using ACM approach.