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Deep Learning for Real-Time Aerodynamic Evaluations of Arbitrary Vehicle Shapes
Journal Article
15-15-02-0006
ISSN: 2770-3460, e-ISSN: 2770-3479
Sector:
Topic:
Citation:
Jacob, S., Mrosek, M., Othmer, C., and Köstler, H., "Deep Learning for Real-Time Aerodynamic Evaluations of Arbitrary Vehicle Shapes," SAE Int. J. Passeng. Veh. Syst. 15(2):77-90, 2022, https://doi.org/10.4271/15-15-02-0006.
Language:
English
Abstract:
The aerodynamic optimization process of cars requires multiple iterations between
aerodynamicists and stylists. Response surface modeling and reduced-order
modeling (ROM) are commonly used to eliminate the overhead due to computational
fluid dynamics (CFD), leading to faster iterations. However, a primary drawback
of these models is that they can work only on the parameterized geometric
features they were trained with. This study evaluates if deep learning models
can predict the drag coefficient (cd
) for an arbitrary input geometry without explicit parameterization. We
use two similar data sets (total of 1000 simulations) based on the publicly
available DrivAer geometry for training. We use a modified U-Net architecture
that uses signed distance fields (SDF) 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 data-driven
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.