Airfoil aerodynamic/stealth design based on conditional generative adversarial networks

Shi Yi Jin, Shu Sheng Chen, Shi Qi Che, Jin Ping Li, Jia Hao Lin, Zheng Hong Gao

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

Aerodynamic/stealth design is becoming an important factor in the advanced airfoil design. In this work, a supervised machine learning method is proposed for aerodynamic and stealth integrated airfoil design. The conditional generative adversarial network (CGAN) is constructed for the multidisciplinary design of airfoil. Then, the generator and discriminator simply using deep neural network have good robustness and stability in training. The CGAN model also shows good generalization capability in the test set, with less than 1% error in fitting to the airfoil profile data, and the generated airfoils are within 10% error compared to the test airfoil aerodynamic stealth characteristics. In addition, the optimization results based on the CGAN model demonstrate that aerodynamic performance improvement would increase the airfoil camber and stealth performance improvement would sharpen the airfoil leading edge.

源语言英语
文章编号077146
期刊Physics of Fluids
36
7
DOI
出版状态已出版 - 1 7月 2024

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