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

TL;DR

SuperWing is an extensive open dataset of transonic wing aerodynamics designed to advance data-driven aerodynamic modeling, featuring diverse geometries and flow solutions for improved machine learning predictor development.

Contribution

The paper introduces SuperWing, a large, diverse, and publicly available transonic wing dataset with benchmark results demonstrating its utility for aerodynamic prediction models.

Findings

Transformers trained on SuperWing predict surface flow with high accuracy.

Pretrained models on SuperWing generalize well to complex benchmark wings.

SuperWing enhances the diversity and applicability of aerodynamic datasets.

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.