  Deep reinforcement learning (DRL) algorithms are rapidly making inroads into
fluid mechanics, following the remarkable achievements of these techniques in a
wide range of science and engineering applications. In this paper, a deep
reinforcement learning (DRL) agent has been employed to train an artificial
neural network (ANN) using computational fluid dynamics (CFD) data to perform
active flow control (AFC) around a 2-D circular cylinder. Flow control
strategies are investigated at a diameter-based Reynolds number Re_D = 100
using advantage actor-critic (A2C) algorithm by means of two symmetric plasma
actuators located on the surface of the cylinder near the separation point. The
DRL agent interacts with the computational fluid dynamics (CFD) environment
through manipulating the non-dimensional burst frequency (f^+) of the two
plasma actuators, and the time-averaged surface pressure is used as a feedback
observation to the deep neural networks (DNNs). The results show that a regular
actuation using a constant non-dimensional burst frequency gives a maximum drag
reduction of 21.8 %, while the DRL agent is able to learn a control strategy
that achieves a drag reduction of 22.6%. By analyzing the flow-field, it is
shown that the drag reduction is accompanied with a strong flow reattachment
and a significant reduction in the mean velocity magnitude and velocity
fluctuations at the wake region. These outcomes prove the great capabilities of
the deep reinforcement learning (DRL) paradigm in performing active flow
control (AFC), and pave the way toward developing robust flow control
strategies for real-life applications.
