  Deep artificial neural networks (ANNs) used together with deep reinforcement
learning (DRL) are receiving growing attention due to their capabilities to
control complex problems. This technique has been recently used to solve
problems related to flow control. In this work, an ANN trained through a DRL
agent is used to perform active flow control. Two-dimensional simulations of
the flow around a cylinder are conducted and an active control based on two
jets located on the walls of the cylinder is considered. By gathering
information from the flow surrounding the cylinder, the ANN agent is able to
learn effective control strategies for the jets, leading to a significant drag
reduction. In the present work, a Reynolds-number range beyond those previously
considered is studied and compared with results obtained using classical
flow-control methods. Significantly different nature in the control strategies
is identified by the DRL as the Reynolds number Re increases. For Re <= 1000
the classical control strategy based on an opposition control relative to the
wake oscillation is obtained. For Re = 2000 the new strategy consists of an
energisation of the boundary layers and the separation area, which modulate the
flow separation and reduce drag in a fashion similar to that of the drag
crisis, through a high frequency actuation. A cross-application of agents is
performed for a flow at Re = 2000, obtaining similar results in terms of drag
reduction with the agents trained at Re = 1000 and 2000. The fact that two
different strategies yield the same performance make us question whether this
Reynolds number regime (Re = 2000) belongs to a transition towards a
nature-different flow which would only admit a high-frequency actuation
strategy to obtain drag reduction. This finding allows the application of ANNs
trained at lower Reynolds numbers but comparable in nature, saving
computational resources.
