A systematic dataset generation technique applied to data-driven automotive aerodynamics

Abstract

A novel strategy for generating datasets has been developed within the context of drag prediction for automotive geometries using neural networks. A primary challenge in this field is constructing a training database of sufficient size and diversity. Our method relies on a small number of initial data points and provides a recipe to systematically interpolate between them, generating an arbitrary number of samples at the desired quality. We tested this strategy using a representative automotive geometry and demonstrated that convolutional neural networks perform exceptionally well at predicting drag coefficients and surface pressures. Promising results were obtained in testing extrapolation performance. Our method can be applied to other problems of aerodynamic shape optimization.