A comparative study of learning techniques for the compressible aerodynamics over a transonic RAE2822 airfoil

DOI10.1016/J.COMPFLUID.2022.105759OpenAlexW4312084767MaRDI QIDQ2698730

Pierre Baqué, Luca Zampieri, Giovanni Catalani, Daniel Costero, Vincent Chapin, Nicolas Gourdain, Michael Bauerheim

Publication date: 25 April 2023

Published in: Computers and Fluids (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.compfluid.2022.105759

zbMATH Keywords

CFDdeep learningRAE2822transonic aerodynamics

Mathematics Subject Classification ID

Fluid mechanics (76-XX)

Uses Software

• Scikit
• Adam
• U-Net

Cites Work

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• Hidden physics models: machine learning of nonlinear partial differential equations
• A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils
• Prediction of aerodynamic flow fields using convolutional neural networks
• POD and CVT-based reduced-order modeling of Navier-Stokes flows
• A deep learning approach for the transonic flow field predictions around airfoils
• Turbulence and the dynamics of coherent structures. I. Coherent structures
• Reduced-order modeling for unsteady transonic flows around an airfoil
• Two-Dimensional Subsonic Flow of Compressible Fluids

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