Host: The Japanese Society for Artificial Intelligence
Name : 34th Annual Conference, 2020
Number : 34
Location : Online
Date : June 09, 2020 - June 12, 2020
In order to evaluate the coefficient of drag (C<sub>D</sub>) 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 C<sub>D</sub> was learnt using a machine learning technology to replace CFD with a machine learning model. In this study, the prediction model of C<sub>D</sub> 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 C<sub>D</sub> while deforming a car shape interactively, a tool for the C<sub>D</sub> 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.
