Scientific multi-agent reinforcement learning for wall-models of turbulent flows

TL;DR

This paper introduces SciMARL, a multi-agent reinforcement learning approach for developing wall models in turbulent flow simulations, significantly reducing computational costs while maintaining accuracy across diverse conditions.

Contribution

The paper presents a novel multi-agent reinforcement learning framework for wall modeling in turbulence simulations, capable of generalizing to high Reynolds numbers and complex geometries.

Findings

Reduces computational cost by several orders of magnitude.

Accurately reproduces key flow quantities in turbulent simulations.

Generalizes well to unseen geometries and extreme Reynolds numbers.

Abstract

The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and weather prediction, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However, simulations of turbulent flows remain hindered by the inability of heuristics and supervised learning to model the near-wall dynamics. We address this challenge by introducing scientific multi-agent reinforcement learning (SciMARL) for the discovery of wall models for large-eddy simulations (LES). In SciMARL, discretization points act also as cooperating agents that learn to supply the LES closure model. The agents self-learn using limited data and generalize to extreme Reynolds numbers and previously unseen geometries. The present simulations reduce by several orders of magnitude the computational cost over fully-resolved simulations while reproducing key flow quantities. We believe that SciMARL creates unprecedented capabilities for the simulation of turbulent flows.