SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design
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.
Keywords
Cite
@article{arxiv.2512.14397,
title = {SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design},
author = {Yunjia Yang and Weishao Tang and Mengxin Liu and Nils Thuerey and Yufei Zhang and Haixin Chen},
journal= {arXiv preprint arXiv:2512.14397},
year = {2026}
}