论文标题
基于加强学习的基于活性流控制的执行器选择方法
Reinforcement-learning-based actuator selection method for active flow control
论文作者
论文摘要
本文通过提出一种基于增强剂学习代理之上的新方法来解决用于主动流控制的执行器选择问题。从使用众多执行器的预训练代理开始，该算法估计了潜在执行器去除对价值函数的影响，表明代理的性能。它适用于两种测试用例，即一维kuramoto-sivashinsky方程和在RE = 1000的机翼周围的层状双维流量，范围为12至20度，以证明其功能和限制。所提出的执行器 - 标准化方法依赖于从完全开发的布局开始的连续消除最不相关的作用组件。使用基于值函数的指标评估每个组件的相关性。结果表明，尽管仍然受到这种内在的消除范式的限制（即顺序消除），但执行器模式和获得的策略表明了相关性能，并允许对帕累托（Pareto）进行表现与执行器预算的准确近似。
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.
