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Reinforcement-learning-based actuator selection method for active flow control
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DOI：10.1017/jfm.2022.1043.png)
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Abstract
En 中文
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 degrees 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 us to draw an accurate approximation of the Pareto front of performances versus actuator budget.
Keywords:
drag reduction
instability control
machine learning
Journal
IF:
3.9
Papers:
2.0W
Citations:
9.4W