发表机构
Ratio Labs（雷迪奥实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Airfoil2Vec，一种结合谱几何编码的神经代理模型，用于快速预测翼型流场，并在新数据集上验证了其高效性与泛化能力。
AI 中文摘要
我们介绍了一个包含约10,000次雷诺平均纳维-斯托克斯（RANS）模拟的数据集，这些模拟针对生成下压力的NACA四位数字翼型周围的稳态、不可压缩、二维亚音速流动，目标是汽车和赛车运动应用中相关的空气动力学领域（公开可在该https URL上获取）。利用这一资源，我们研究了用于快速流动预测的几何条件神经代理模型，比较了神经场与神经ODE、MLP与基于图的模型，以及几种谱几何条件方法。我们进一步提出了Airfoil2Vec，一种翼型特定的谱几何编码器，它结合了联合轮廓谱与弯度和厚度的分离谱表示，用于预测连续的压力和速度场。我们通过攻角插值、对未见NACA四位数字几何体的插值和外推，以及对未见非NACA翼型的泛化来评估泛化能力。所得到的代理模型准确捕捉了空气动力学量和定性流动特征，同时相比传统计算流体动力学提供了数量级的加速。
英文摘要
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.
