Data-Driven Car Drag Prediction With Depth and Normal Renderings

Author:

Song Binyang1,Yuan Chenyang2,Permenter Frank2,Arechiga Nikos3,Ahmed Faez4

Affiliation:

1. Virginia Tech Department of Industrial and Systems Engineering, , Blacksburg, VA 24060

2. Toyota Research Institute Human-Centered AI, , Cambridge, MA 02139

3. Toyota Research Institute Human-Centered AI, , Los Altos, CA 94022

4. Massachusetts Institute of Technology Department of Mechanical Engineering, , Cambridge, MA 02139

Abstract

Abstract Generative artificial intelligence (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 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 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 4535 high-quality 3D car meshes labeled by drag coefficients computed from computational fluid dynamics simulations to train our model. Our experiments demonstrate that our model can accurately and efficiently evaluate drag coefficients with an R2 value above 0.84 for various car categories. 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 toward the automatic generation of drag-optimized car designs. Moreover, we demonstrate a case study using the proposed surrogate model to guide a diffusion-based deep generative model for drag-optimized car body synthesis.

Publisher

ASME International

Reference63 articles.

1. Pixel2Mesh: Generating 3D Mesh Models From Single RGB Images;Wang,2018

2. 3D Shape Generation and Completion Through Point-Voxel Diffusion;Zhou,2021

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