Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session A30: Reinforcement Learning for Flow Control
8:00 AM–9:57 AM,
Sunday, November 19, 2023
Room: 154AB
Chair: Ricardo Vinuesa, KTH (Royal Institute of Technology)
Abstract: A30.00007 : Reinforcement learning for real-time flow control of vertical axis wind turbines
9:18 AM–9:31 AM
Presenter:
Baptiste Corban
(ISAE-Supaero)
Authors:
Baptiste Corban
(ISAE-Supaero)
Daniel Fernex
(EPFL)
Karen Mulleners
(EPFL)
Emmanuel Rachelson
(ISAE-Supaero)
Michaël Bauerheim
(ISAE-Supaero)
Thierry Jardin
(ISAE-Supaero)
But the occurrence of dynamic stall during the rotation of the blades is detrimental to the performance and the life expectancy of the turbines. Closed-loop control is a solution to mitigate the effect of undesired flow features.
Here, we optimize the power generation of a reduced-scale turbine immersed in a water channel. A reinforcement learning agent is trained using the Soft Actor Critic algorithm. It performs 45 pitching action per cycle, based on real-time measurement of the loads on the blade.
The trained agent can multiply the power coefficient of the turbine by 2.5 compared to a non-pitching blade in only one hour of training.
We also provide guidelines on how to choose the reward function or the discount factor, and their effect on the shape and the periodicity of the optimal policies.
Our work shows the potential of reinforcement learning for real-time flow control on a complex fluid mechanics problem.
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