TripOptimizer: Generative 3D Shape Optimization and Drag Prediction using Triplane VAE Networks

• URL: http://arxiv.org/abs/2509.12224v1
• Date: Fri, 05 Sep 2025 15:14:19 GMT
• Title: TripOptimizer: Generative 3D Shape Optimization and Drag Prediction using Triplane VAE Networks
• Authors: Parsa Vatani, Mohamed Elrefaie, Farhad Nazarpour, Faez Ahmed,
• Abstract summary: Tripr is a framework for rapid aerodynamic analysis and shape optimization directly from vehicle point cloud data.<n>Tripr employs a Variational Autoencoder featuring a triplane-based implicit neural representation for high-fidelity 3D geometry reconstruction and a drag coefficient prediction head.
• Score: 4.4288915456583755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The computational cost of traditional Computational Fluid Dynamics-based Aerodynamic Shape Optimization severely restricts design space exploration. This paper introduces TripOptimizer, a fully differentiable deep learning framework for rapid aerodynamic analysis and shape optimization directly from vehicle point cloud data. TripOptimizer employs a Variational Autoencoder featuring a triplane-based implicit neural representation for high-fidelity 3D geometry reconstruction and a drag coefficient prediction head. Trained on DrivAerNet++, a large-scale dataset of 8,000 unique vehicle geometries with corresponding drag coefficients computed via Reynolds-Averaged Navier-Stokes simulations, the model learns a latent representation that encodes aerodynamically salient geometric features. We propose an optimization strategy that modifies a subset of the encoder parameters to steer an initial geometry towards a target drag value, and demonstrate its efficacy in case studies where optimized designs achieved drag coefficient reductions up to 11.8\%. These results were subsequently validated by using independent, high-fidelity Computational Fluid Dynamics simulations with more than 150 million cells. A key advantage of the implicit representation is its inherent robustness to geometric imperfections, enabling optimization of non-watertight meshes, a significant challenge for traditional adjoint-based methods. The framework enables a more agile Aerodynamic Shape Optimization workflow, reducing reliance on computationally intensive CFD simulations, especially during early design stages.

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