Feedback control of transitional shear flows: sensor selection for performance recovery

Yao, Huaijin; Sun, Yiyang; Hemati, Maziar S.

doi:10.1007/s00162-022-00616-z

Cited by 2 publications

(2 citation statements)

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“…They used this method to control both the linearised complex Ginzburg-Landau equation and a flow around an inclined flat plate. Linear-quadratic-Gaussian control on balanced truncated reduced models is employed to derive optimal sensor and actuator placement using gradient descent methods (Manohar, Kutz & Brunton 2021;Yao, Sun & Hemati 2022). Modelling the control system as a linear plant is also used by Bhattacharjee et al (2018) who took advantage of the eigensystem realisation algorithm to compare the controllability (in a H 2 framework) of multiple jet actuators laid on the suction of an airfoil to select the best one depending on the performance criterion (lift or angle of attack upon flow separation).…”

Section: Introductionmentioning

confidence: 99%

“…They used this method to control both the linearised complex Ginzburg–Landau equation and a flow around an inclined flat plate. Linear-quadratic-Gaussian control on balanced truncated reduced models is employed to derive optimal sensor and actuator placement using gradient descent methods (Manohar, Kutz & Brunton 2021; Yao, Sun & Hemati 2022). Modelling the control system as a linear plant is also used by Bhattacharjee et al.…”

Section: Introductionmentioning

confidence: 99%

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

2023

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%