A deep learning approach for the transonic flow field predictions around airfoils


Computers and Fluids, vol.236, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 236
  • Publication Date: 2022
  • Doi Number: 10.1016/j.compfluid.2022.105312
  • Journal Name: Computers and Fluids
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, MathSciNet, Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: Deep learning, Transonic flow, Airfoil aerodynamics, Shock waves, DESIGN
  • Middle East Technical University Affiliated: Yes


© 2022 Elsevier LtdLearning from data offers new opportunities for developing computational methods in research fields, such as fluid dynamics, which constantly accumulate a large amount of data. This study presents a deep learning approach for the transonic flow field predictions around airfoils. The physics of transonic flow is integrated into the neural network model by utilizing Reynolds-averaged Navier–Stokes (RANS) simulations. A detailed investigation on the performance of the model is made both qualitatively and quantitatively. The flow features associated with the transonic effects and the angle of attack variation, such as the shock waves and the flow separation, are well predicted. Furthermore, predicted flowfield data are used to compute the aerodynamic coefficients. The findings indicate that the presented model may allow avoiding time-consuming computational fluid dynamics (CFD) simulations, especially in the design optimization studies with a slight loss of accuracy.