Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control

By Jean Rabault, Miroslav Kuchta, Atle Jensen, U. Réglade, Nicolas Cerardi. Journal of Fluid Mechanics, 2018

553 citations recorded by the source corpus.

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 Kármán 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.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/721556a4fcce35ac0d2db8c058b66165553d48b3

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Citations in the corpus

A review on Deep Reinforcement Learning for Fluid Mechanics (2019, methodology): “Indeed, in most studies, the control values are explicitly bounded to remain low compared to the physical quantities of the system [14].”
Unsupervised deep learning for super-resolution reconstruction of turbulence (2020, background): “2018) and the development of a new flow-control scheme (Rabault et al. 2019).”
Machine Learning for Fluid Mechanics (2019, background): “Big data has been a reality in ﬂuid mechanics (Pollard et al. 2016) over the last decade due to high-performance computing architectures and advances in experimental measurement capabilities.”