Log-law recovery through reinforcement-learning wall model for large eddy simulation

This paper focuses on the use of reinforcement learning (RL) as a machine-learning (ML) modeling tool for near-wall turbulence. RL has demonstrated its effectiveness in solving high-dimensional problems, especially domains such games. Despite potential, is still not widely used turbulence and primarily flow control optimization purposes. A new wall model (WM) called VYBA23 developed this work, which uses agents dispersed near wall. The trained single Reynolds number ($Re_\tau = 10^4$) does rely high-fidelity data, back-propagation process based reward rather than output error. states RLWM, are representation environment by agents, normalized to remove dependence number. tested compared another RLWM (BK22) an equilibrium model, half-channel at eleven different numbers \in [180;10^{10}]$). effects varying agents' parameters actions range, time-step, spacing also studied. results promising, showing little effect average field but some wall-shear stress fluctuations velocity fluctuations. work offers positive prospects developing RLWMs that can recover physical laws, extending type ML models more complex flows future.