  This work studies the application of a reinforcement-learning-based (RL) flow
control strategy to the flow past a cylinder confined between two walls in
order to suppress vortex shedding. The control action is blowing and suction of
two synthetic jets on the cylinder. The theme of this study is to investigate
how to use and embed physical information of the flow in the RL-based control.
First, global linear stability and sensitivity analyses based on the time-mean
flow and the steady flow (which is a solution to the Navier-Stokes equations)
are conducted in a range of blockage ratios and Reynolds numbers. It is found
that the most sensitive region in the wake extends itself when either parameter
increases in the parameter range we investigated here. Then, we utilise these
physical results to help design RL-based control policies. We find that the
controlled wake converges to the unstable steady base flow, where the vortex
shedding can be successfully suppressed. A persistent oscillating control seems
necessary to maintain this unstable state. The RL algorithm is able to
outperform a gradient-based optimisation method (optimised in a certain period
of time) in the long run. Furthermore, when the flow stability information is
embedded in the reward function to penalise the instability, the controlled
flow may become more stable. Finally, according to the sensitivity analyses,
the control is most efficient when the probes are placed in the most sensitive
region. The control can be successful even when few probes are properly placed
in this manner.
