Guillermo Suárez, Chair for Scientific Computing (SciComp), University of Kaiserslautern-Landau (RPTU)

Title: Reinforcement Learning Discovers Efficient Strategies for Active Flow Control

Abstract:

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.

Building on these results, ongoing work investigates the integration of model-based reinforcement learning, aiming to reduce training time and improve generalization.

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