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Updated: Aug 5, 2026
09:17
Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
Published on: April 23, 2018
SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design
Yunjia Yang1,2, Weishao Tang1, Mengxin Liu1
1School of Aerospace Engineering, Tsinghua University, Beijing, China.
Scientific Data
|July 31, 2026
Summary
SuperWing, a new dataset, enhances aerodynamic design by providing diverse 3D wing shapes and flow solutions. This accelerates the development of generalizable machine-learning predictors for aircraft design.
Area of Science:
- Aerospace Engineering
- Computational Fluid Dynamics
- Machine Learning
Background:
- Accelerating aerodynamic design with machine-learning (ML) surrogate models is crucial.
- Limited dataset diversity hinders generalizable ML predictors for 3D wings.
Purpose of the Study:
- Introduce SuperWing, a comprehensive open dataset for transonic swept-wing aerodynamics.
- Enhance ML model generalizability for 3D wing design.
Main Methods:
- Generated 4,239 parameterized wing geometries with diverse spanwise variations.
- Computed 28,856 Reynolds-averaged Navier-Stokes flow field solutions.
- Simulated wings across a broad range of Mach numbers and angles of attack.
Main Results:
- Benchmarked state-of-the-art Transformers, achieving 2.5 drag-count error.
- Demonstrated strong zero-shot generalization to benchmark wings (DLR-F6, NASA CRM).
Conclusions:
- SuperWing dataset significantly advances ML-based aerodynamic prediction.
- The dataset's diversity supports practical applications in aircraft design.
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