scholarly journals Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

2020 ◽  
Vol 32 (5) ◽  
pp. 053605 ◽  
Author(s):  
Hongwei Tang ◽  
Jean Rabault ◽  
Alexander Kuhnle ◽  
Yan Wang ◽  
Tongguang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforcement Learning ◽  
Flow Control ◽  
Active Flow Control ◽  
Reynolds Numbers ◽  
Artificial Neural ◽  
Active Flow

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

2019 ◽  
Vol 865 ◽  
pp. 281-302 ◽  
Author(s):  
Jean Rabault ◽  
Miroslav Kuchta ◽  
Atle Jensen ◽  
Ulysse Réglade ◽  
Nicolas Cerardi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforcement Learning ◽  
Flow Control ◽  
Mass Flow ◽  
Active Flow Control ◽  
Flow Rates ◽  
Mass Flow Rates ◽  
Artificial Neural ◽  
Active Flow

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.

From active learning to deep reinforcement learning: Intelligent active flow control in suppressing vortex-induced vibration

2021 ◽  
Vol 33 (6) ◽  
pp. 063607
Author(s):  
Changdong Zheng ◽  
Tingwei Ji ◽  
Fangfang Xie ◽  
Xinshuai Zhang ◽  
Hongyu Zheng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Active Learning ◽  
Flow Control ◽  
Active Flow Control ◽  
Vortex Induced Vibration ◽  
Active Flow

scholarly journals Reinforcement learning for bluff body active flow control in experiments and simulations

2020 ◽  
Vol 117 (42) ◽  
pp. 26091-26098
Author(s):  
Dixia Fan ◽  
Liu Yang ◽  
Zhicheng Wang ◽  
Michael S. Triantafyllou ◽  
George Em Karniadakis
Keyword(s):  
Reinforcement Learning ◽  
Fluid Mechanics ◽  
Drag Reduction ◽  
Flow Control ◽  
Bluff Body ◽  
Control Strategies ◽  
Active Flow Control ◽  
Reduction Process ◽  
Automatic Sequence ◽  
Active Flow

We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow control problems both in experiments and simulations by automatically discovering active control strategies for drag reduction in turbulent flow. Specifically, we aimed to maximize the power gain efficiency by properly selecting the rotational speed of two small cylinders, located parallel to and downstream of the main cylinder. By properly defining rewards and designing noise reduction techniques, and after an automatic sequence of tens of towing experiments, the RL agent was shown to discover a control strategy that is comparable to the optimal strategy found through lengthy systematically planned control experiments. Subsequently, these results were verified by simulations that enabled us to gain insight into the physical mechanisms of the drag reduction process. While RL has been used effectively previously in idealized computer flow simulation studies, this study demonstrates its effectiveness in experimental fluid mechanics and verifies it by simulations, potentially paving the way for efficient exploration of additional active flow control strategies in other complex fluid mechanics applications.

scholarly journals Performance Optimization of Wind Turbine Rotors with Active Flow Control (Part 1)

2012 ◽  
Vol 134 (04) ◽  
pp. 51-51 ◽  
Author(s):  
G. Pechlivanoglou ◽  
C.N. Nayeri ◽  
C.O. Paschereit
Keyword(s):  
Neural Network ◽  
Flow Control ◽  
Wind Turbine ◽  
Performance Optimization ◽  
Active Flow Control ◽  
Fine Tuning ◽  
Dynamic Force ◽  
Empirical Parameter ◽  
Turbine Rotors ◽  
Active Flow

This article describes the performance optimization of wind turbine rotors with active flow control. The active Gurney flap concept was tested in the wind tunnel under dynamic AoA variations to simulate unsteady inflow conditions. A high-deflection micro flap was actuated by four digital electric servos with a maximum deflection rate of 360°/sec. A custom code was created to allow dynamic AoA variations of the test wing with simultaneous dynamic force measurements. During the dynamic investigations, various control strategies were tested, starting from standard PID controllers with semi-empirical parameter tuning models to Direct Inverse Controllers with neural network tuning strategies and pure self-learning neural network controllers. The results of the closed-loop measurements using the manually tuned PID controller showed a reduction potential for the dynamic lift loads in the range of 70% as well as a stable controller behavior. The Direct Inverse Controller not only showed a load reduction of 36.8%, but also significant improvement potential with respect to its fine-tuning.

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