Airfoil2Vec: Spectral Geometry-Conditioned Neural Surrogate Models for Airfoil Aerodynamics and a Downforce-Generating CFD Dataset

2026-09-25

AI Summary

This study addresses the prohibitive computational cost of CFD simulations in automotive aerodynamics by proposing a spectral geometry-conditioned neural surrogate model for rapid flow field prediction. Methodologically, a downforce airfoil dataset is constructed, and an Airfoil2Vec encoder is introduced to fuse airfoil contours, camber, and thickness into a unified spectral representation. Efficient modeling is achieved by integrating Reynolds-Averaged Navier–Stokes (RANS) simulations with graph neural networks and neural ordinary differential equation architectures. Experimental results demonstrate that the proposed model accurately captures complex flow field characteristics while achieving orders-of-magnitude speedup over conventional CFD solvers. Furthermore, it exhibits strong generalization capabilities across diverse geometries, providing an efficient alternative for aerodynamic design optimization.

Abstract

We introduce a dataset of approximately 10,000 Reynolds-Averaged Navier-Stokes (RANS) simulations of steady, incompressible, two-dimensional subsonic flow around downforce-generating NACA 4-digit airfoils, targeting aerodynamic regimes relevant to automotive and motorsport applications (openly available on https://huggingface.co/datasets/ratiolabs/downforce-airfoils). Using this resource, we study geometry-conditioned neural surrogates for fast flow prediction, comparing neural fields with neural ODEs, MLPs with graph-based models, and several spectral geometry-conditioning methods. We further propose Airfoil2Vec, an airfoil-specific spectral geometry encoder that combines the joint contour spectrum with separate spectral representations of camber and thickness, for predicting continuous pressure and velocity fields. We evaluate generalization through angle-of-attack interpolation, interpolation and extrapolation to unseen NACA 4-digit geometries, and generalization to unseen non-NACA airfoils. The resulting surrogate accurately captures aerodynamic quantities and qualitative flow features while providing orders-of-magnitude speedups over conventional computational fluid dynamics.

Problem

• airfoil aerodynamics
• neural surrogate models
• computational fluid dynamics
• downforce-generating airfoils
• flow prediction

Innovation

• Spectral Geometry Encoder
• Neural Surrogate Models
• Airfoil2Vec
• Computational Fluid Dynamics
• Downforce Airfoils