Turbulence Modeling for Physics-Informed Neural Networks: Comparison of Different RANS Models for the Backward-Facing Step Flow

Turbulence Modeling for Physics-Informed Neural Networks: Comparison of Different RANS Models for the Backward-Facing Step Flow

Physics-informed neural networks (PINN) can be used to predict flow fields with a minimum of simulated or measured training data. As most technical flows are turbulent, PINNs based on the Reynolds-averaged Navier–Stokes (RANS) equations incorporating a turbulence model are needed. Several studies de...

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Detaylı Bibliyografya
Asıl Yazarlar: Fabian Pioch, Jan Hauke Harmening, Andreas Maximilian Müller, Franz-Josef Peitzmann, Dieter Schramm, Ould el Moctar
Materyal Türü: Makale
Dil:English
Baskı/Yayın Bilgisi: MDPI AG 2023-01-01
Seri Bilgileri:Fluids
Konular:
physics-informed neural networks
RANS
turbulence model
flow separation
DeepXDE
Online Erişim:https://www.mdpi.com/2311-5521/8/2/43

Internet

https://www.mdpi.com/2311-5521/8/2/43

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