  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. We use two similar data sets based
on the publicly available DrivAer geometry for training. We use a modified
U-Net architecture that uses Signed Distance Fields to represent the input
geometries. Our models outperform the existing models by at least 11% in
prediction accuracy for the drag coefficient. We achieved this improvement by
combining multiple data sets that were created using different
parameterizations, which is not possible with the methods currently used. We
have also shown that it is possible to predict velocity fields and drag
coefficient concurrently and that simple data augmentation methods can improve
the results. In addition, we have performed an occlusion sensitivity study on
our models to understand what information is used to make predictions. From the
occlusion sensitivity study, we showed that the models were able to identify
the geometric features and have discovered that the model has learned to
exploit the global information present in the SDF. In contrast to the currently
operational method, where design changes are restricted to the initially
defined parameters, this study brings surrogate models one step closer to the
long-term goal of having a model that can be used for approximate aerodynamic
evaluation of unseen, arbitrary vehicle shapes, thereby providing complete
design freedom to the vehicle stylists.
