Journal Article

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

Pioch, Fabian, Peitzmann, Franz-Josef, el Moctar, Ould, Schramm, Dieter, Harmening, Jan Hauke, Müller, Andreas Maximilian

2023

Overview

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 demonstrated the capability of PINNs to solve the Naver–Stokes equations for laminar flows. However, little work has been published concerning the application of PINNs to solve the RANS equations for turbulent flows. This study applied a RANS-based PINN approach to a backward-facing step flow at a Reynolds number of 5100. The standard k-ω model, the mixing length model, an equation-free νt and an equation-free pseudo-Reynolds stress model were applied. The results compared favorably to DNS data when provided with three vertical lines of labeled training data. For five lines of training data, all models predicted the separated shear layer and the associated vortex more accurately.

• Publisher: MDPI AG
• Subject: Backward facing steps / Comparative analysis / DeepXDE / flow separation / Fluid dynamics / Fluid flow / Hypotheses / Laminar flow / Navier-Stokes equations / Neural networks / Ordinary differential equations / Partial differential equations / Physics / physics-informed neural networks / RANS / Reynolds averaged Navier-Stokes method / Reynolds number / Reynolds stress / Shear layers / Training / turbulence model / Turbulence models / Turbulent flow / Velocity / Viscosity / Wake turbulence