Reinforcement-learning-based actuator selection method for active flow control. (25th January 2023)

• Record Type: Journal Article
• Authors: Paris, Romain; Beneddine, Samir; Dandois, Julien
• Is Part Of: Journal of fluid mechanics. Volume 955(2023)
• Publication Date: 2023-01-25
• Subjects: drag reduction -- instability control -- machine learning; Fluid mechanics -- Periodicals; 532.005
• DOI: 10.1017/jfm.2022.1043
• Languages: English
• ISSNs: 0022-1120

Abstract

Abstract: 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 bidimensional flow around an airfoil at $Re=1000$ for different angles of attack ranging from $12^{\circ }$ to $20^{\circ }$, 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 us to draw an accurate approximation of the Pareto front of performances versus actuator budget.