SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design

• URL: http://arxiv.org/abs/2512.14397v1
• Date: Tue, 16 Dec 2025 13:35:45 GMT
• Title: SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design
• Authors: Yunjia Yang, Weishao Tang, Mengxin Liu, Nils Thuerey, Yufei Zhang, Haixin Chen,
• Abstract summary: We present SuperWing, a comprehensive open dataset of transonic swept-wing aerodynamics.<n>The wing shapes in the dataset are generated using a simplified yet expressive geometry parameterization.<n>To demonstrate the dataset's utility, we benchmark two state-of-the-art Transformers that accurately predict surface flow.
• Score: 19.941629337887484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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