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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Article number077146
JournalPhysics of Fluids
Volume36
Issue number7
DOIs
StatePublished - 1 Jul 2024

Fingerprint

Dive into the research topics of 'Airfoil aerodynamic/stealth design based on conditional generative adversarial networks'. Together they form a unique fingerprint.

Cite this