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

• URL: http://arxiv.org/abs/2604.18491v2
• Date: Mon, 27 Apr 2026 17:38:54 GMT
• Title: Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD
• Abstract summary: Computational Fluid Dynamics (CFD) is central to racecar aerodynamic development.<n>Its cost severely limits the design space exploration feasible within realistic budgets.<n>New AI-based surrogate models promise to alleviate this bottleneck.
• Score: 7.948688528462053
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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