Deep Learning for Real-Time Aerodynamic Evaluations of Arbitrary Vehicle Shapes

Understand

The aerodynamic optimization process of cars requires multiple iterations between aerodynamicists and stylists.

• Response Surface Modeling and Reduced-Order Modeling are commonly used to eliminate the overhead due to Computational Fluid Dynamics, leading to faster iterations.
• However, a primary drawback of these models is that they can work only on the parametrized geometric features they were trained with.
• This study evaluates if deep learning models can predict the drag coefficient for an arbitrary input geometry without explicit parameterization.