Physics-informed neural networks for solving Reynolds-averaged Navier$\unicode{x2013}$Stokes equations
This addresses fluid dynamics simulation challenges for engineers and researchers, but it is incremental as it applies an existing method to new problems.
The paper tackled solving Reynolds-averaged Navier-Stokes equations for turbulent flows using physics-informed neural networks without turbulence models, achieving less than 1% error for laminar flows and good accuracy for turbulent cases.
Physics-informed neural networks (PINNs) are successful machine-learning methods for the solution and identification of partial differential equations (PDEs). We employ PINNs for solving the Reynolds-averaged Navier$\unicode{x2013}$Stokes (RANS) equations for incompressible turbulent flows without any specific model or assumption for turbulence, and by taking only the data on the domain boundaries. We first show the applicability of PINNs for solving the Navier$\unicode{x2013}$Stokes equations for laminar flows by solving the Falkner$\unicode{x2013}$Skan boundary layer. We then apply PINNs for the simulation of four turbulent-flow cases, i.e., zero-pressure-gradient boundary layer, adverse-pressure-gradient boundary layer, and turbulent flows over a NACA4412 airfoil and the periodic hill. Our results show the excellent applicability of PINNs for laminar flows with strong pressure gradients, where predictions with less than 1% error can be obtained. For turbulent flows, we also obtain very good accuracy on simulation results even for the Reynolds-stress components.