Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings

TL;DR

This paper introduces a new 2D shape representation for 3D cars, enabling efficient surrogate modeling of drag coefficients with high accuracy, facilitating AI-driven design optimization.

Contribution

It proposes a novel 2D shape representation for 3D cars and develops a surrogate drag model that accurately predicts drag coefficients using deep neural networks.

Findings

Model achieves R^2 above 0.84 in predicting drag.

Representation generalizes to other product categories.

Dataset and code are publicly available.

Abstract

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/.