Abstract
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.
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The authors would like to acknowledge supports from National Numerical Wind Tunnel Project (Grant No. NNW2019ZT4-B09).
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Project supported by the National Natural Science Foundation of China (Grant Nos. 91852117, 91852106).
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Xu, H., Zhang, W., Deng, J. et al. Active flow control with rotating cylinders by an artificial neural network trained by deep reinforcement learning. J Hydrodyn 32, 254–258 (2020). https://doi.org/10.1007/s42241-020-0027-z
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DOI: https://doi.org/10.1007/s42241-020-0027-z