Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates

• Published in: Journal of fluid mechanics Vol. 919
• Main Authors: Chen, Li-Wei, Cakal, Berkay A., Hu, Xiangyu, Thuerey, Nils
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.06.2021
• Subjects: Aerodynamic forces; Aerodynamics; Algorithms; Artificial neural networks; Computational fluid dynamics; Curves; Deep learning; Depth profiling; Drag; Fields; Fluid dynamics; Forces; Gradient flow; Inverse problems; Investigations; JFM Papers; Laminar flow; Machine learning; Minimum drag; Neural networks; Numerical analysis; Partial differential equations; Reynolds number; Shape; Shape optimization; Two dimensional flow; computational methods; Navier–Stokes equations; general fluid mechanics

Efficiently predicting the flow field and load in aerodynamic shape optimisation remains a highly challenging and relevant task. Deep learning methods have been of particular interest for such problems, due to their success in solving inverse problems in other fields. In the present study, U-net-bas...