TripOptimizer: Generative three-dimensional shape optimization and drag prediction using triplane variational autoencoder networks

The computational cost of traditional Computational Fluid Dynamics (CFD)-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 integrates a Variational Autoencoder with a triplane-based implicit neural representation for high-fidelity three-dimensional geometry reconstruction and a drag coefficient prediction head. Trained on DrivAerNet++, a large-scale dataset of 8000 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. The framework's primary contribution is a novel optimization strategy that modifies a subset of the encoder parameters to steer an initial geometry toward a target drag value, and we demonstrate its efficacy in case studies where optimized designs achieved drag coefficient reductions up to 11.8%. These results were subsequently validated using independent, high-fidelity Computational Fluid Dynamics simulations with more than 150 × 106 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.