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.

翻译：机器学习代理模型在加速气动设计方面展现出潜力，但针对三维机翼的通用预测器的发展一直受限于现有数据集的稀缺性和多样性不足。本文提出了SuperWing，一个全面的跨音速后掠翼气动学开放数据集，包含4,239个参数化机翼几何构型和28,856个雷诺平均Navier-Stokes流场解。数据集中的机翼形状采用一种简化但表达能力强的几何参数化方法生成，该方法融合了翼型形状、扭转角和上反角沿展向的变化，从而在不依赖基准机翼扰动的情况下实现了更高的多样性。所有形状均在涵盖典型飞行包线的广泛马赫数和攻角范围内进行了模拟。为展示数据集的实用性，我们评估了两种最先进的Transformer模型，它们能准确预测表面流动，并在保留样本上实现了2.5个阻力计数误差。在SuperWing上预训练的模型进一步展现出对DLR-F6和NASA CRM等复杂基准机翼的强零样本泛化能力，凸显了该数据集的多样性和实际应用潜力。