构建4239个跨音速机翼数据集，助力机器学习加速气动设计。
SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design
- 用参数化方法生成多样机翼形状，支持展向气动布局变化。
- 包含28,856组流场仿真，覆盖全飞行包线，精度达2.5阻力计误差。
- 适合从事气动设计、深度学习建模的研究者使用。
机器学习代理模型在加速气动设计方面展现出潜力，但受限于现有数据集的稀缺性和多样性不足，三维机翼的通用预测进展缓慢。本文提出SuperWing，一个开放的跨音速后掠翼气动数据集，包含4,239个参数化机翼几何形状和28,856个雷诺平均纳维-斯托克斯（RANS）流场解。机翼形状采用简化但表达力强的参数化方法，引入展向剖面、扭转和上反角变化，实现丰富多样性，无需依赖基准机翼的小扰动。所有几何体在广泛马赫数与迎角条件下进行模拟，覆盖典型飞行包线。为验证数据集效用，我们对比了两种先进Transformer模型，其对表面流场预测准确，且在保留样本上达到2.5阻力计误差。在SuperWing上预训练的模型还展现出对DLR-F6和NASA CRM等复杂基准机翼的强大零样本泛化能力，证明该数据集的多样性与实际应用潜力。
原文摘要 · Abstract (English)
Machine-learning surrogate models have shown promise in accelerating aerodynamic design, yet progress toward generalizable predictors for three-dimensional wings has been limited by the scarcity and restricted diversity of existing datasets. Here, we present SuperWing, a comprehensive open dataset of transonic swept-wing aerodynamics comprising 4,239 parameterized wing geometries and 28,856 Reynolds-averaged Navier-Stokes flow field solutions. The wing shapes in the dataset are generated using a simplified yet expressive geometry parameterization that incorporates spanwise variations in airfoil shape, twist, and dihedral, allowing for an enhanced diversity without relying on perturbations of a baseline wing. All shapes are simulated under a broad range of Mach numbers and angles of attack covering the typical flight envelope. To demonstrate the dataset's utility, we benchmark two state-of-the-art Transformers that accurately predict surface flow and achieve a 2.5 drag-count error on held-out samples. Models pretrained on SuperWing further exhibit strong zero-shot generalization to complex benchmark wings such as DLR-F6 and NASA CRM, underscoring the dataset's diversity and potential for practical usage.
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