Active flow control with rotating cylinders by an artificial neural network trained by deep reinforcement learning

• Published in: Journal of hydrodynamics. Series B Vol. 32; no. 2; pp. 254 - 258
• Main Authors: Xu, Hui, Zhang, Wei, Deng, Jian, Rabault, Jean
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.04.2020
• Subjects: Engineering; Engineering Fluid Dynamics; Hydrology/Water Resources; Numerical and Computational Physics; Simulation; Special Column on the International Symposium on High-Fidelity Computational Methods and Applications 2019 (Guest Editors Hui Xu; Wei Zhang; rotating cylinders; Artificial neural network (ANN); flow control; deep reinforcement learning (DRL)
• ISSN: 1001-6058; 1878-0342
• DOI: 10.1007/s42241-020-0027-z

In this paper, an artificial neural network (ANN) trained through a deep reinforcement learning (DRL) agent is used to perform flow control. The target is to look for the wake stabilization mechanism in an active way. The flow past a 2-D cylinder with a Reynolds number 240 is addressed with and without a control strategy. The control strategy is based on using two small rotating cylinders which are located at two symmetrical positions back of the main cylinder. The rotating speed of the counter-rotating small cylinder pair is determined by the ANN and DRL approach. By performing the final test, the interaction of the counter-rotating small cylinder pair with the wake of the main cylinder is able to stabilize the periodic shedding of the main cylinder wake. This demonstrates that the way of establishing this control strategy is reliable and viable. In another way, the internal interaction mechanism in this control method can be explored by the ANN and DRL approach.