Surrogate Model Development for Prediction of Car Aerodynamics Using Machine Learning

Abstract

In the evaluation of car aerodynamics, Computational Fluid Dynamics (CFD) are frequently used as well as a wind-tunnel. However, the CFD simulations consume a lot of cost and time. In this study, a surrogate model using the machine learning was developed to reduce cost and time of CFD. In the proposed model, the relation between car shapes and CFD results was learned for rapid prediction of pressure, velocity and coefficient of drag for aerodynamics. In this paper, we introduce the proposed model, the training dataset, the accuracy and the computational time.