From active learning to deep reinforcement learning: Intelligent active flow control in suppressing vortex-induced vibration

Abstract

In the present work, an efficient active flow control strategy in eliminating vortex-induced vibration of a cylinder at Re = 100 has been explored by two machine learning frameworks, from active learning to reinforcement learning. Specifically, an adaptive control scheme by a pair of jets placed on the poles of the cylinder as actuators has been discovered. In the active learning framework, a Gaussian progress regression surrogate model is used to predict vibration amplitude of the cylinder using a limited number of numerical simulations by combining the Bayesian optimization algorithm with specified control actions while in the reinforcement learning framework, soft actor-critic deep reinforcement learning algorithm is adopted to construct a real-time control system. The results have shown that the triangle control agent in the active learning framework can reduce the vibration amplitude of the cylinder from A = 0.6 to A = 0.43. The real-time control in the reinforcement learning framework can successfully suppress the vibration amplitude to 0.11, which is decreased by 82.7%. By comparison, there are some similarities in the amplitude and phase of the action trajectories between two intelligent learning frameworks. They both aim at keeping track of the antiphase between the position and the action, which will restrain the cylinder at a low-amplitude vibration. The underlying physics shows that the jet will contain suction in the stage of vortex generation and injection in the stage of vortex shedding. The current findings have provided a new concept to the typical flow control problem and make it more practical in industrial applications.