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[2O6-GS-13-02] Interactive prediction of automotive aerodynamic drag coefficient using machine learning
Keywords:Aerodynamics, Automotive, 3D Convolutional neural network, Voxel
In order to evaluate the coefficient of drag (CD) on aerodynamics, the wind-tunnel tests and Computational Fluid Dynamics (CFD) are generally used in the car development. However, these test and CFD require much cost and time. Therefore, the relationship between the car shape and CD was learnt using a machine learning technology to replace CFD with a machine learning model. In this study, the prediction model of CD was developed by using a 3-dimensional convolutional neural network and a voxel approximation of car shape. The developed prediction model shows lower cost and less time than the conventional CFD. Additionally, in order to predict CD while deforming a car shape interactively, a tool for the CD prediction with a graphical user interface was developed. This tool can help designers to solve the trade-off issues of aerodynamics, the package design of the car and the external styling.
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