How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning
This work addresses active flow control in complex fluid dynamics, potentially enabling efficient strategies for similar problems, though it appears incremental as it applies an existing method to a specific domain.
The authors tackled the problem of controlling hydrodynamic forces on a fluidic pinball system using deep reinforcement learning, achieving control strategies comparable to or better than brute-force methods through thousands of iterations of automatic learning.
Deep reinforcement learning (DRL) for fluidic pinball, three individually rotating cylinders in the uniform flow arranged in an equilaterally triangular configuration, can learn the efficient flow control strategies due to the validity of self-learning and data-driven state estimation for complex fluid dynamic problems. In this work, we present a DRL-based real-time feedback strategy to control the hydrodynamic force on fluidic pinball, i.e., force extremum and tracking, from cylinders' rotation. By adequately designing reward functions and encoding historical observations, and after automatic learning of thousands of iterations, the DRL-based control was shown to make reasonable and valid control decisions in nonparametric control parameter space, which is comparable to and even better than the optimal policy found through lengthy brute-force searching. Subsequently, one of these results was analyzed by a machine learning model that enabled us to shed light on the basis of decision-making and physical mechanisms of the force tracking process. The finding from this work can control hydrodynamic force on the operation of fluidic pinball system and potentially pave the way for exploring efficient active flow control strategies in other complex fluid dynamic problems.