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

DOI10.1016/j.compfluid.2022.105312OpenAlexW4205354742MaRDI QIDQ2670066

Cihat Duru, Hande Alemdar, Ozgur Ugras Baran

Publication date: 10 March 2022

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

Full work available at URL: https://doi.org/10.1016/j.compfluid.2022.105312

zbMATH Keywords

shock wavestransonic flowdeep learningairfoil aerodynamics

Mathematics Subject Classification ID

Fluid mechanics (76-XX)

Related Items (2)

A comparative study of learning techniques for the compressible aerodynamics over a transonic RAE2822 airfoil ⋮ Towards high-accuracy deep learning inference of compressible flows over aerofoils

Uses Software

• PyTorch
• ImageNet
• Adam
• AlexNet
• Spalart-Allmaras

Cites Work

• Restoration of the contact surface in the HLL-Riemann solver
• A supervised neural network for drag prediction of arbitrary 2D shapes in laminar flows at low Reynolds number
• Prediction of aerodynamic flow fields using convolutional neural networks
• Lift coefficient prediction at high angle of attack using recurrent neural network
• Data-driven prediction of unsteady flow over a circular cylinder using deep learning

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