Wall Modeling in LES of Turbulent Flows Using Reinforcement Learning

This work seeks to design a reinforcement learning (RL)-based wall models (WMs) for large-eddy simulation (LES) that can first recover the law of the wall in equilibrium flows before capturing the dynamics of non-equilibrium flows.

Actual WMs indeed fail to reproduce strong non-equilibrium effects especially when they are spatially diffused.

As a result, the potential of machine learning (ML)-based WMs becomes a promising solution.

Two essential requirements for the development of ML-based WM are the generalization to larger Reynolds numbers and the validation of fundamental physical laws.

The use of RL for WM development removes the high-fidelity data cost issues that exist for other supervised learning methods.

Furthermore, it can provide a high level of interpretability of the model behavior.

A novel RL WM, utilizing agents dispersed near the flow wall, is proposed in this study.

Initially, the model will be compared with existing ML-based WMs using equilibrium half-channel flow up to large Reynolds numbers.

The agents' states-action map will provide valuable insights into the model's behavior, thereby enhancing the interpretability of the model.

Following this, the model's performance will be evaluated against non-equilibrium half-channel flows, under medium to high pressure gradients in both the spanwise and streamwise directions.

This research is supported by the Independent Research Fund Denmark (DFF) under the Grant No. 1051-00015B.