Journal
JOURNAL OF FLUID MECHANICS
Volume 865, Issue -, Pages 281-302Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/jfm.2019.62
Keywords
control theory; drag reduction
Categories
Funding
- Norwegian Research Council [233901, 280625, 256435]
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We present the first application of an artificial neural network trained through a deep reinforcement learning agent to perform active flow control. It is shown that, in a two-dimensional simulation of the Karman vortex street at moderate Reynolds number (Re = 100), our artificial neural network is able to learn an active control strategy from experimenting with the mass flow rates of two jets on the sides of a cylinder. By interacting with the unsteady wake, the artificial neural network successfully stabilizes the vortex alley and reduces drag by approximately 8%. This is performed while using small mass flow rates for the actuation, of the order of 0.5% of the mass flow rate intersecting the cylinder cross-section once a new pseudo-periodic shedding regime is found. This opens the way to a new class of methods for performing active flow control.
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