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Investigation of the Under-Hood Aero-Thermal Flow Features Using 3D CFD Simulation
Technical Paper
2017-01-0142
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The demand for better fuel economy pushed by both consumer and Environmental Protection Agency (EPA), made OEMs to put more effort on other areas beside vehicle external aerodynamics. As one of these areas, under-hood aero-thermal management has taken an important role in the new road vehicle design process, due to the combination of growing engine power demands, utilization of sophisticated under-hood and underbody devices, and emission regulations. The challenge of the under-hood aerothermal management is not only due to the complexity of under-hood compartment, but also as a result of the complex heat transfer phenomena involving conduction, convention and thermal radiation. In this study, 3D CFD simulations were used to investigate the under-hood aerothermal flow features. The full vehicle model with detailed under-hood components used in this study is a Hyundai Veloster. A commercial CDF code Star-CCM+ version 11.04 from CD-adapco was used to run all the simulations. The baseline case is a pure aerodynamic analysis without considering the heat transfer phenomena. Two more cases with conduction and convection models were simulated to investigate the influence of vehicle speed on the aerothermal flow features. The last and the final one included all the heat transfer modes to investigate the importance of thermal radiation in the under-hood aerothermal management. Condenser and radiator were modeled as porous media. The cooling fan rotation was modeled using the moving reference frames (MRF) method. Heat exchanger model was applied on radiator core to simulate the heat transfer from the coolant to airflow. We observed the under-hood flow feature to be strongly affected by the wheel rotation.
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Zhang, C., Uddin, M., and Foster, L., "Investigation of the Under-Hood Aero-Thermal Flow Features Using 3D CFD Simulation," SAE Technical Paper 2017-01-0142, 2017, https://doi.org/10.4271/2017-01-0142.Data Sets - Support Documents
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