论文标题
在湍流条件下，将深度加固学习应用于主动流动控制
Applying deep reinforcement learning to active flow control in turbulent conditions
论文作者
论文摘要
机器学习最近已成为流体力学方面的一种有前途的技术，尤其是用于主动流量控制（AFC）应用。最近的工作[J.流体机械。 （2019），第1卷。 865，pp。281-302]证明了深钢筋学习（DRL）在$ re = 100 $的圆柱上执行AFC（即在层状流量状态下）的可行性和有效性。作为一项后续研究，我们在中间雷诺数（即$ re = 1000 $）上研究了相同的AFC问题，其中流量的湍流对控制构成了巨大的挑战。结果表明，DRL代理仍然可以找到有效的控制策略，但需要更多的学习发作。实现了$ 30 \％$的显着减少，伴随着再循环气泡的伸长和圆柱唤醒中湍流波动的减少。据我们所知，这项研究是DRL在弱动荡条件下的首次成功应用。因此，它在强烈的湍流中为AFC迈出了一个新的里程碑。
Machine learning has recently become a promising technique in fluid mechanics, especially for active flow control (AFC) applications. A recent work [J. Fluid Mech. (2019), vol. 865, pp. 281-302] has demonstrated the feasibility and effectiveness of deep reinforcement learning (DRL) in performing AFC over a circular cylinder at $Re = 100$, i.e., in the laminar flow regime. As a follow-up study, we investigate the same AFC problem at an intermediate Reynolds number, i.e., $Re = 1000$, where the turbulence in the flow poses great challenges to the control. The results show that the DRL agent can still find effective control strategies, but requires much more episodes in the learning. A remarkable drag reduction of around $30\%$ is achieved, which is accompanied by elongation of the recirculation bubble and reduction of turbulent fluctuations in the cylinder wake. To our best knowledge, this study is the first successful application of DRL to AFC in weak turbulent conditions. It therefore sets a new milestone in progressing towards AFC in strong turbulent flows.
