Mechanical rotation at low Reynolds number via reinforcement learning

2021 ◽  
Vol 33 (6) ◽  
pp. 062007
Author(s):  
Yuexin Liu ◽  
Zonghao Zou ◽  
Alan Chen Hou Tsang ◽  
On Shun Pak ◽  
Y.-N. Young
Keyword(s):  
Reynolds Number ◽  
Reinforcement Learning ◽  
Low Reynolds Number ◽  
Low Reynolds ◽  
Mechanical Rotation

scholarly journals Publisher's Note: “Mechanical rotation at low Reynolds number via reinforcement learning” [Phys. Fluids 33, 062007 (2021)]

2021 ◽  
Vol 33 (7) ◽  
pp. 079902
Author(s):  
Yuexin Liu ◽  
Zonghao Zou ◽  
Alan Cheng Hou Tsang ◽  
On Shun Pak ◽  
Y.-N. Young
Keyword(s):  
Reynolds Number ◽  
Reinforcement Learning ◽  
Low Reynolds Number ◽  
Low Reynolds ◽  
Mechanical Rotation

Purcell’s “rotator”: mechanical rotation at low Reynolds number

2005 ◽  
Vol 47 (1) ◽  
pp. 161-164 ◽  
Author(s):  
R. Dreyfus ◽  
J. Baudry ◽  
H. A. Stone
Keyword(s):  
Reynolds Number ◽  
Low Reynolds Number ◽  
Low Reynolds ◽  
Mechanical Rotation

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

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5920
Author(s):  
Mikhail Tokarev ◽  
Egor Palkin ◽  
Rustam Mullyadzhanov
Keyword(s):  
Reynolds Number ◽  
Reinforcement Learning ◽  
Angular Velocity ◽  
Low Reynolds Number ◽  
Maximum Amplitude ◽  
Harmonic Oscillations ◽  
Loop Control ◽  
Reward Function ◽  
First Case ◽  
Low Reynolds

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 Re=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.

Experimental Investigation on Blunt-Edged UTM Delta Wing VFE-2 Configurations at Low Reynolds Number

2018 ◽  
Vol 12 (3) ◽  
pp. 255
Author(s):  
Muhammad Zal Aminullah Daman Huri ◽  
Shabudin Bin Mat ◽  
Mazuriah Said ◽  
Shuhaimi Mansor ◽  
Md. Nizam Dahalan ◽  
...  
Keyword(s):  
Experimental Investigation ◽  
Reynolds Number ◽  
Delta Wing ◽  
Low Reynolds Number ◽  
Low Reynolds

HEAT TRANSFER OF IMPINGING JET AT LOW REYNOLDS NUMBER

2018 ◽  
Author(s):  
Vadim V. Lemanov ◽  
Viktor I. Terekhov ◽  
Vladimir V. Terekhov
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Low Reynolds Number ◽  
Impinging Jet ◽  
Low Reynolds
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