Physics informed data-driven near-wall modelling for lattice Boltzmann simulation of high Reynolds number turbulent flows

Abstract

Data-driven approaches offer novel opportunities for improving the performance of turbulent flow simulations, which are critical to wide-ranging applications from wind farms and aerodynamic designs to weather and climate forecasting. However, current methods for these simulations often require large amounts of data and computational resources. While data-driven methods have been extensively applied to the continuum Navier-Stokes equations, limited work has been done to integrate these methods with the highly scalable lattice Boltzmann method. Here, we present a physics-informed neural network framework for improving lattice Boltzmann-based simulations of near-wall turbulent flow. Using a small amount of data and integrating physical constraints, our model accurately predicts flow behaviour at a wide range of friction Reynolds numbers up to 1.0 × 106. In contradistinction with other models that use direct numerical simulation datasets, this approach reduces data requirements by three orders of magnitude and allows for sparse grid configurations. Our work broadens the scope of lattice Boltzmann applications, enabling efficient large-scale simulations of turbulent flow in diverse contexts. The authors provide a data-driven near-wall modelling framework for the lattice Boltzmann method using IDDES data. Their model can predict flows with friction Reynolds numbers up to 1,000,000 and effectively handle sparse near-wall grids.