Integration of 1D and 3D Simulation Tools for Cabin Cool Down Simulations

Paper #:
  • 2018-01-0773

Published:
  • 2018-04-03
Abstract:
This study presents a method for a cool down simulation of passenger compartments. The purpose was to integrate the 3D CFD software Fluent with the 1D thermal management software KULI. The targets were to achieve accurate prediction of temperature diffusion inside the cabin for a transient cycle simultaneously reducing the modelling effort and CPU-time consumption. The 1D simulation model was developed in KULI and the flow field data required to simulate mass flow and diffusion inside the cabin was implemented from ANSYS Fluent. The simulation model consists of a multi-zone cabin model capable of dealing with multiple inlets, detailed cabin walls description comprising of layers of different thermal conductivities and capacities, solar radiation and recirculation inside the cabin. The simulation also models the complete refrigerant circuit consisting of evaporator, condenser, Thermal Expansion Valve (TXV) and compressor. This paper describes the process flow, definition of the inputs required and finally the validation of the simulation data with experiments. The cabin has a dual evaporator system with separate evaporator and blower for the 3rd row seats. The model consists of modelling the refrigeration circuit as well as the air side flow where the path of air is traced along the front as well as rear evaporator. To model the transient nature of the development of temperature distribution inside the cabin, solar irradiation, air flow rate over the evaporators as well as the condenser is given as a function of vehicle speed or time. Also, thermal point masses are included for the seats, AC ducts, evaporator, and air mass inside the cabin to consider this transient nature. Not only the temperature profiles are validated but also the system pressures of the refrigerant circuit such as the suction pressure and discharge pressure. The model predicts cabin temperatures within an error of 5 % and pressures within an error of 10% with a significant reduction in modelling effort and CPU-time consumption.
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