TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks

• URL: http://arxiv.org/abs/2503.17400v1
• Date: Wed, 19 Mar 2025 17:30:57 GMT
• Title: TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks
• Authors: Qian Chen, Mohamed Elrefaie, Angela Dai, Faez Ahmed,
• Abstract summary: TripNet is a machine learning-based framework leveraging triplane representations to predict the outcomes of high-fidelity CFD simulations.<n>TripNet achieves state-of-the-art performance in coefficient drag prediction, surface field estimation, and full 3D flow field simulations of industry-standard car designs.
• Score: 27.577307360710545
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Computational Fluid Dynamics (CFD) simulations are essential in product design, providing insights into fluid behavior around complex geometries in aerospace and automotive applications. However, high-fidelity CFD simulations are computationally expensive, making rapid design iterations challenging. To address this, we propose TripNet, Triplane CFD Network, a machine learning-based framework leveraging triplane representations to predict the outcomes of large-scale, high-fidelity CFD simulations with significantly reduced computation cost. Our method encodes 3D geometry into compact yet information-rich triplane features, maintaining full geometry fidelity and enabling accurate aerodynamic predictions. Unlike graph- and point cloud-based models, which are inherently discrete and provide solutions only at the mesh nodes, TripNet allows the solution to be queried at any point in the 3D space. Validated on high-fidelity DrivAerNet and DrivAerNet++ car aerodynamics datasets, TripNet achieves state-of-the-art performance in drag coefficient prediction, surface field estimation, and full 3D flow field simulations of industry-standard car designs. By utilizing a shared triplane backbone across multiple tasks, our approach offers a scalable, accurate, and efficient alternative to traditional CFD solvers.

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