Details

Autor(en) / Beteiligte

• Rabault, Jean
• Kuchta, Miroslav
• Jensen, Atle
• Réglade, Ulysse
• Cerardi, Nicolas

Titel

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

Ist Teil von

• Journal of fluid mechanics, 2019-04, Vol.865, p.281-302

Ort / Verlag

Cambridge, UK: Cambridge University Press

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.