Reinforcement-learning-based actuator selection method for active flow control

TL;DR

This paper introduces a reinforcement learning-based method for selecting actuators in active flow control, effectively reducing actuator count while maintaining performance, demonstrated on flow equations and airfoil simulations.

Contribution

It presents a novel sequential elimination approach for actuator selection using reinforcement learning, improving actuator efficiency in flow control applications.

Findings

Effective actuator sparsification achieved with minimal performance loss

Method accurately approximates the Pareto front of performance versus actuator count

Demonstrated on both one-dimensional and two-dimensional flow cases

Abstract

This paper addresses the issue of actuator selection for active flow control by proposing a novel method built on top of a reinforcement learning agent. Starting from a pre-trained agent using numerous actuators, the algorithm estimates the impact of a potential actuator removal on the value function, indicating the agent's performance. It is applied to two test cases, the one-dimensional Kuramoto-Sivashinsky equation and a laminar bi-dimensional flow around an airfoil at Re=1000 for different angles of attack ranging from 12 to 20 degrees, to demonstrate its capabilities and limits. The proposed actuator-sparsification method relies on a sequential elimination of the least relevant action components, starting from a fully developed layout. The relevancy of each component is evaluated using metrics based on the value function. Results show that, while still being limited by this intrinsic elimination paradigm (i.e. the sequential elimination), actuator patterns and obtained policies demonstrate relevant performances and allow to draw an accurate approximation of the Pareto front of performances versus actuator budget.