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.

中文翻译：

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通过深度强化学习训练的人工神经网络主动控制旋转气缸

在本文中，通过深度强化学习（DRL）代理训练的人工神经网络（ANN）用于执行流量控制。目标是积极寻求唤醒稳定机制。在有和没有控制策略的情况下，流经带有雷诺数240的2-D圆柱体的流量都会得到处理。该控制策略基于使用两个小的旋转气缸，它们位于主气缸后方的两个对称位置。反向旋转的小气缸对的转速由ANN和DRL方法确定。通过执行最终测试，反向旋转的小气缸对与主缸尾流的相互作用能够稳定主缸尾流的周期性脱落。这表明建立这种控制策略的方法是可靠和可行的。