Predictive large-eddy-simulation wall modeling via physics-informed neural networks

Abstract

A study shows that when trained using channel flow data at just one Reynolds number and informed with known flow physics, the neural network works robustly as a wall model in large-eddy simulation (LES) of channel flow at any Reynolds number. It also outperforms the equilibrium wall model in LES of a 3D boundary layer flow.