Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD

TL;DR

This paper introduces a high-fidelity CFD dataset for motorsport, a novel graph neural operator called GIST, and demonstrates its effectiveness for interactive aerodynamic design exploration.

Contribution

The work presents a new CFD dataset, a spectral graph neural network model, and validates its use for efficient, accurate aerodynamic design in motorsport.

Findings

GIST achieves state-of-the-art accuracy on benchmarks.

The dataset covers complex geometries relevant to motorsport.

GIST enables interactive design-space exploration with CFD-level accuracy.

Abstract

Computational Fluid Dynamics (CFD) is central to race-car aerodynamic development, yet its cost -- tens of thousands of core-hours per high-fidelity evaluation -- severely limits the design space exploration feasible within realistic budgets. AI-based surrogate models promise to alleviate this bottleneck, but progress has been constrained by the limited complexity of public datasets, which are dominated by smoothed passenger-car shapes that fail to exercise surrogates on the thin, complex, highly loaded components governing motorsport performance. This work presents three primary contributions. First, we introduce a high-fidelity RANS dataset built on a parametric LMP2-class CAD model and spanning six operating conditions (map points) covering straight-line and cornering regimes, generated and validated by aerodynamics experts at Dallara to preserve features relevant to industrial motorsport. Second, we present the Gauge-Invariant Spectral Transformer (GIST), a graph-based neural operator whose spectral embeddings encode mesh connectivity to enhance predictions on tightly packed, complex geometries. GIST guarantees discretization invariance and scales linearly with mesh size, achieving state-of-the-art accuracy on both public benchmarks and the proposed race-car dataset. Third, we demonstrate that GIST achieves a level of predictive accuracy suitable for early-stage aerodynamic design, providing a first validation of the concept of interactive design-space exploration -- where engineers query a surrogate in place of the CFD solver -- within industrial motorsport workflows.