Removing the log-layer mismatch in wall-modeled LES using near-wall erroneous flows via physics-informed neural network

In this talk, we propose a physics-informed neural network that corrects the near-wall erroneous flows to accurately drive the equilibrium wall model using the first off-wall grid point as the input. The proposed neural networks predict the amounts of numerical errors in the flow variables near the wall, which is then used to input the physically correct values to the wall model. The input and output features of the neural networks are chosen based on near-wall turbulent physics for robustness against varying Reynolds and Mach number conditions. Tests on the zero-pressure-gradient flat-plate turbulent boundary layer show that the log-layer mismatch problem, which plagues the results of the conventional WMLES, is removed. Furthermore, the probability density distributions of the wall shear stress predicted by the proposed method show better agreement with the reference.