A b-spline-based generative adversarial network model for fast interactive airfoil aerodynamic optimization
AIAA Scitech 2020 Forum, Vol: 1 PartF, Page: 1-16
2020
- 57Citations
- 148Usage
- 27Captures
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Metrics Details
- Citations57
- Citation Indexes57
- 57
- CrossRef1
- Usage148
- Downloads146
- Abstract Views2
- Captures27
- Readers27
- 27
Conference Paper Description
Airfoil aerodynamic optimization is of great importance in aircraft design; however, it relies on high-fidelity physics-based models that are computationally expensive to evaluate. In this work, we provide a methodology to reduce the computational cost for airfoil aerodynamic optimization. Firstly, we develop a B-spline based generative adversarial networks (BSplineGAN) parameterization method to automatically infer design space with sufficient shape variability. Secondly, we construct multi-layer neural network (MNN) surrogates for fast predictions on aerodynamic drag, lift, and pitching moment coefficients. The BSplineGAN has a relative error lower than 1% when fitting to UIUC database. Verification of MNN surrogates shows the root means square errors (RMSE) of all aerodynamic coefficients are within the range of 20%–40% standard deviation of testing points. Both normalized RMSE and relative errors are controlled within 1%. The proposed methodology is then demonstrated on an airfoil aerodynamic optimization. We also verified the baseline and optimized designs using a high-fidelity computational fluid dynamic solver. The proposed framework has the potential to enable web-based fast interactive airfoil aerodynamic optimization.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85092413952&origin=inward; http://dx.doi.org/10.2514/6.2020-2128; https://arc.aiaa.org/doi/10.2514/6.2020-2128; https://scholarsmine.mst.edu/mec_aereng_facwork/4898; https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=6431&context=mec_aereng_facwork; https://dx.doi.org/10.2514/6.2020-2128
American Institute of Aeronautics and Astronautics (AIAA)
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