Data-Driven Car Drag Prediction With Depth and Normal Renderings
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md_146_5_051714.pdf
Description
Published version
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1006.59 KB
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Checksum (MD5)
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Author(s)
Song, Binyang
Yuan, Chenyang
Permenter, Frank
Arechiga, Nikos
Ahmed, Faez
Date Issued
March 28, 2024
Journal
Journal of Mechanical Design
Publisher
ASME International
Citation
Song, B., Yuan, C., Permenter, F., Arechiga, N., and Ahmed, F. (March 28, 2024). "Data-Driven Car Drag Prediction With Depth and Normal Renderings." ASME. J. Mech. Des. May 2024; 146(5): 051714.
Version
Final published version
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.
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4065063
