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任峰, 都军民, 李广华. 降低圆柱升力脉动的智能自适应旋转控制. 力学学报, 2024, 56(3): 1-8. DOI: 10.6052/0459-1879-23-449
引用本文: 任峰, 都军民, 李广华. 降低圆柱升力脉动的智能自适应旋转控制. 力学学报, 2024, 56(3): 1-8. DOI: 10.6052/0459-1879-23-449
Ren Feng, Du Junmin, Li Guanghua. Intelligent self-adaptive control for mitigating lift fluctuations of a circular cylinder. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(3): 1-8. DOI: 10.6052/0459-1879-23-449
Citation: Ren Feng, Du Junmin, Li Guanghua. Intelligent self-adaptive control for mitigating lift fluctuations of a circular cylinder. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(3): 1-8. DOI: 10.6052/0459-1879-23-449

降低圆柱升力脉动的智能自适应旋转控制

INTELLIGENT SELF-ADAPTIVE CONTROL FOR MITIGATING LIFT FLUCTUATIONS OF A CIRCULAR CYLINDER

  • 摘要: 针对圆柱绕流的闭环主动控制问题, 采用强化学习获得自适应的控制策略, 利用圆柱下游的6个速度测点作为状态反馈, 通过施加旋转作用来降低圆柱的升力脉动. 为了获得高保真的流场时空演化历程, 采用基于格子Boltzmann方法的求解器, 其中结合多块网格以获得足够大的计算域并增强对近场流动的解析效果, 以及多重直接力浸没边界方法以准确处理旋转圆柱边界. 基于该控制回路和高保真数值环境, 实现了降低孤立情形下圆柱升力脉动92.5%, 降低阻力脉动44.3%的效果, 同时尾流回流区长度增大了36.4%. 通过局部线性稳定性分析发现, 施加控制后流动的绝对不稳定性区域延长36%, 此时尾流绝对不稳定区域最不稳定扰动频率偏离涡脱落频率. 此外, 基于策略迁移学习, 在上游圆柱尾流干扰的情形下实现了降低下游圆柱升力脉动95.9%的控制效果, 但同时也带来阻力增加约1倍的代价. 本研究为降低圆柱升力脉动提供智能自适应旋转控制方案, 并为未来开展复杂流动的智能控制提供详细案例参考.

     

    Abstract: This article focuses on closed-loop active control of flow past a circular cylinder, where the reinforcement learning (RL) is utilized to obtain the intelligent self-adaptive control strategy. In the closed loop, six velocity sensors are used to provide feedback signals and establish the state space, and the real-time adjustable rotations are used to mediate the flow surrounding the cylinder such that the lift fluctuations can be mediated. In order to acquire high-fidelity data of flow dynamics in both temporal and spatial space, the lattice Boltzmann method is adopted, which couples the multi-block mesh partition technique so as to obtain a sufficiently large computational domain while providing sufficiently fine mesh in the near field, as well as multi-force immersed boundary method for accurately dealing with the rotating wall boundary. Based on this control loop, as well as the numerical environment, the lift fluctuation of an isolated cylinder is mitigated by 92.5%, the drag fluctuation is mitigated by 44.3%. In the meantime, the recirculation bubble is enlarged by 36.4%, implying smaller pressure drop between the front and rear side of the circular cylinder. Through the local linear stability analysis, the length of absolutely unstable region is elongated by 36%, while the frequency of the least stable perturbation deviates from the vortex shedding frequency. Through comparisons of uncontrolled and RL-controlled instantaneous pressure fields that corresponds to the maximum lift, it is directly revealed that the RL-based control successfully balances the pressure distributed near the upper and lower surface. Furthermore, based on the transfer learning, the lift fluctuation of a circular cylinder interfered by an upstream cylinder is mitigated by 95.9%, on the cost of a doubled mean drag. Through this study, an intelligent flow self-adaptive control for lift mitigations is proposed, and a detailed case study is provided for future flow control of more complicated flows.

     

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