  The present study applies a Deep Reinforcement Learning (DRL) algorithm to
Active Flow Control (AFC) of a two-dimensional flow around a confined square
cylinder. Specifically, the Soft Actor-Critic (SAC) algorithm is employed to
modulate the flow of a pair of synthetic jets placed on the upper and lower
surfaces of the confined squared cylinder in flow configurations characterized
by Re of 100, 200, 300, and 400. The investigation starts with an analysis of
the baseline flow in the absence of active control. It is observed that at Re =
100 and Re = 200, the vortex shedding exhibits mono-frequency characteristics.
Conversely, at Re = 300 and Re = 400, the vortex shedding is dominated by
multiple frequencies, which is indicative of more complex flow features. With
the application of the SAC algorithm, we demonstrate the capability of
DRL-based control in effectively suppressing vortex shedding, while
significantly diminishing drag and fluctuations in lift. Quantitatively, the
data-driven active control strategy results in a drag reduction of
approximately 14.4%, 26.4%, 38.9%, and 47.0% for Re = 100, 200, 300, and 400,
respectively. To understand the underlying control mechanism, we also present
detailed flow field comparisons, which showcase the adaptability of DRL in
devising distinct control strategies tailored to the dynamic conditions at
varying Re. These findings substantiate the proficiency of DRL in controlling
chaotic, multi-frequency dominated vortex shedding phenomena, underscoring the
robustness of DRL in complex AFC problems.
