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.

翻译：计算流体力学（CFD）是赛车空气动力学研发的核心，但其单次高保真评估需耗费数万核时的高昂成本，严重限制了实际预算内可探索的设计空间。基于人工智能的替代模型有望缓解这一瓶颈，但受限于公开数据集的复杂度不足——这些数据集以平滑的乘用车外形为主，无法充分测试替代模型在薄翼、复杂、高载荷部件（决定赛车运动性能的关键）上的表现。本文提出三项核心贡献：首先，我们构建了基于参数化LMP2级CAD模型的高保真RANS数据集，涵盖直线行驶与弯道工况的六种运行条件（映射点），该数据集由达拉拉公司空气动力学专家生成并验证，保留了工业赛车运动的相关特征；其次，我们提出规范不变性谱变换器（GIST）——一种基于图的神经算子，其谱嵌入编码网格连通性以增强对紧密排列复杂几何体的预测能力。GIST保证离散化不变性，且计算复杂度随网格规模线性增长，在公开基准与所提赛车数据集上均达到最优精度；第三，我们验证了GIST达到适合早期空气动力学设计的预测精度，首次证明了交互式设计空间探索概念在工业赛车运动工作流中的可行性——即工程师用替代模型替代CFD求解器进行查询。