Surrogate Modeling of Car Drag Coefficient with Depth and Normal
  Renderings
Binyang Song Chenyang Yuan Frank Permenter Nikos Arechiga Faez Ahmed
Generative AI models have made significant progress in automating the
creation of 3D shapes, which has the potential to transform car design. In
engineering design and optimization, evaluating engineering metrics is crucial.
To make generative models performance-aware and enable them to create
high-performing designs, surrogate modeling of these metrics is necessary.
However, the currently used representations of three-dimensional (3D) shapes
either require extensive computational resources to learn or suffer from
significant information loss, which impairs their effectiveness in surrogate
modeling. To address this issue, we propose a new two-dimensional (2D)
representation of 3D shapes. We develop a surrogate drag model based on this
representation to verify its effectiveness in predicting 3D car drag. We
construct a diverse dataset of 9,070 high-quality 3D car meshes labeled by drag
coefficients computed from computational fluid dynamics (CFD) simulations to
train our model. Our experiments demonstrate that our model can accurately and
efficiently evaluate drag coefficients with an $R^2$ value above 0.84 for
various car categories. Moreover, the proposed representation method can be
generalized to many other product categories beyond cars. Our model is
implemented using deep neural networks, making it compatible with recent AI
image generation tools (such as Stable Diffusion) and a significant step
towards the automatic generation of drag-optimized car designs. We have made
the dataset and code publicly available at
https://decode.mit.edu/projects/dragprediction/.