Guillermo Suárez

(University of Kaiserslautern-Landau (RPTU))
hosted by Seminar Series on Scientific Computing

"Reinforcement Learning Discovers Efficient Strategies for Active Flow Control"

We explore the use of reinforcement learning to develop effective strategies for active flow control in unsteady fluid dynamics. In a two-dimensional computational fluid dynamics simulation of flow past a circular cylinder at a Reynolds number of 100, a reinforcement learning agent learns to manipulate dual side jets to alter the vortex shedding dynamics. Without any prior knowledge of the flow physics, the agent discovers a control policy that suppresses vortex-induced oscillations and achieves a drag reduction of nearly 10%. This performance is attained with minimal actuation effort, using jet mass flow rates of less than 0.5% of the incoming flow.

Time:	Thursday, 08.05.2025, 10:15
Place:	Hybrid (Room 32-349 and via Zoom)
Video:	https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1

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