Deep Reinforcement Learning Control of Cylinder Flow Using Rotary Oscillations at Low Reynolds Number

Mikhail Tokarev, Egor Palkin and Rustam Mullyadzhanov
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Mikhail Tokarev: Institute of Thermophysics SB RAS, Lavrentyev ave. 1, 630090 Novosibirsk, Russia
Egor Palkin: Physics and Mathematics Departments, Novosibirsk State University, Pirogov str. 1, 630090 Novosibirsk, Russia
Rustam Mullyadzhanov: Institute of Thermophysics SB RAS, Lavrentyev ave. 1, 630090 Novosibirsk, Russia

Energies, 2020, vol. 13, issue 22, 1-11

Abstract: We apply deep reinforcement learning to active closed-loop control of a two-dimensional flow over a cylinder oscillating around its axis with a time-dependent angular velocity representing the only control parameter. Experimenting with the angular velocity, the neural network is able to devise a control strategy based on low frequency harmonic oscillations with some additional modulations to stabilize the Kármán vortex street at a low Reynolds number R e = 100 . We examine the convergence issue for two reward functions showing that later epoch number does not always guarantee a better result. The performance of the controller provide the drag reduction of 14% or 16% depending on the employed reward function. The additional efforts are very low as the maximum amplitude of the angular velocity is equal to 8 % of the incoming flow in the first case while the latter reward function returns an impressive 0.8 % rotation amplitude which is comparable with the state-of-the-art adjoint optimization results. A detailed comparison with a flow controlled by harmonic oscillations with fixed amplitude and frequency is presented, highlighting the benefits of a feedback loop.

Keywords: flow control; ANN; DRL (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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