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Noise generated by vortices is a sensible measure of the strength of wakesgenerated in a flow field. This paper presents an innovative approach toactively control wakes and noise generated in a flow past a cylinder using deepreinforcement learning (DRL) as a continuously learning active controlalgorithm with noise levels recorded using acoustic level around the wakeregion. The two primary objectives of this study is to investigate thefeasibility and effectiveness of use of acoustic level as controlling parameterand employing DRL algorithms to optimize the control of flow dynamics. Ahydrophone array is utilised to capture the wake pattern along the flow fielddownstream of a circular cylinder in the form of acoustic signals. Thecollected data are used to create a real-time feedback loop for a DRL agent toadjust jet actuators strategically placed on the cylinder's surface. Thisapproach enables the agent to learn and adapt its control strategy based on theobserved acoustic feedback, resulting in a closed-loop control system. Wedemonstrate that the DRL-based flow control strategy can effectively reducewake amplitude and the noise generated. The noise level reduces by anappreciable 6.9\% and 9.5\% in the given setting for two controlconfigurations. Similarly, the drag coefficient reduces by a remarkable 15.9\%and 23.8\% respectively. Specifically, it reduces the oscillation amplitude indrag and noise. The proposed method offers promising results in terms ofreducing flow-induced vibration. The paper highlights the potential for usingDRL, jets, and hydrophone arrays in active flow control, opening new avenuesfor optimizing flow control in practical engineering applications.

Acoustics-based Active Control of Unsteady Flow Dynamics using Reinforcement Learning Driven Synthetic Jets