  We utilize deep reinforcement learning (DRL) algorithms to precisely control
the mass flow rates of synthetic jets located on the upper and lower surfaces
of a square cylinder for active flow control. Through DRL-based active flow
control (AFC) technology, we significantly reduce the lift and drag
coefficients of the square cylinder at Reynolds number (Re) = 100 and Re=500,
while completely suppressing vortex shedding in the wake flow field.
Additionally, we conduct a sensitivity analysis of the position and width
parameters of the synthetic jets regarding flow control performance. Our
observations indicate that positioning the synthetic jets near the trailing
edge corners of the square cylinder, rather than the leading edge corners, can
completely suppress vortex shedding, resulting in more stable lift and drag
coefficients in the controlled flow. When the synthetic jets are positioned at
the trailing edge corners, flow control reduces the mean drag coefficient by
14.4% and the standard deviation of the lift coefficient by 86.1% for the
baseline flow at Re=100. For the baseline flow at Re=500, flow control reduces
the mean drag coefficient by 51.4% and the standard deviation of the lift
coefficient by 90.5%. At both Reynolds numbers, vortex shedding in the wake
flow field is completely suppressed. Furthermore, using narrower synthetic jets
results in a lower reduction rate of the standard deviations of the lift and
drag coefficients, while increasing the mean and standard deviation of the mass
flow rate of the jets used for flow control. This study provides guidance on
optimizing the width and position of synthetic jets for DRL-based active flow
control.
