Optimizing synthetic jet placement for control stability in increasing Reynolds number flows based on deep reinforcement learning

Abstract

This study utilizes reinforcement learning algorithms to develop advanced control strategies for manipulating synthetic jets positioned on a cylinder, aiming to achieve effective flow control. It addresses critical challenges in achieving robust performance across moderate to high Reynolds numbers (Re) and varying jet placement configurations. The results demonstrate that placing synthetic jets near flow separation regions yields optimal control performance. At Re = 500, the multi-objective flow control strategy achieves up to 37.4% drag reduction and 99% suppression of lift oscillations, while completely suppressing vortex shedding. At Re = 1,000, drag reduction improves to 49.1%, with equally effective lift suppression and complete vortex shedding suppression. At Re = 2,000, the strategy achieves a maximum drag reduction of 58.8% and suppresses 90% of lift oscillations, although some residual wake instabilities persist. Additionally, synthetic jets effectively disrupted detached vortices and stabilized the wake, resulting in streamlined flow and suppressed vortex shedding. These findings underscore the pivotal influence of jet placement in optimizing flow control efficacy, while affirming the advanced capability of control strategies to tackle intricate challenges.