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基于速度和位移反馈的圆柱涡激振动主动控制研究

邹琳 王家辉 王程 郑云龙 徐劲力

邹琳, 王家辉, 王程, 郑云龙, 徐劲力. 基于速度和位移反馈的圆柱涡激振动主动控制研究. 力学学报, 2023, 55(9): 1834-1846 doi: 10.6052/0459-1879-23-183
引用本文: 邹琳, 王家辉, 王程, 郑云龙, 徐劲力. 基于速度和位移反馈的圆柱涡激振动主动控制研究. 力学学报, 2023, 55(9): 1834-1846 doi: 10.6052/0459-1879-23-183
Zou Lin, Wang Jiahui, Wang Cheng, Zheng Yunlong, Xu Jinli. Active control of vortex-induced vibration of cylindr based on velocity and displacement feedback. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(9): 1834-1846 doi: 10.6052/0459-1879-23-183
Citation: Zou Lin, Wang Jiahui, Wang Cheng, Zheng Yunlong, Xu Jinli. Active control of vortex-induced vibration of cylindr based on velocity and displacement feedback. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(9): 1834-1846 doi: 10.6052/0459-1879-23-183

基于速度和位移反馈的圆柱涡激振动主动控制研究

doi: 10.6052/0459-1879-23-183
基金项目: 国家自然科学基金资助项目(11972268, 12372232)
详细信息
    通讯作者:

    邹琳, 教授, 主要研究方向为流动控制. E-mail: l.zou@163.com

    徐劲力, 教授, 主要研究方向为计算力学. E-mail: xujl1996@163.com

  • 中图分类号: O357.1

ACTIVE CONTROL OF VORTEX-INDUCED VIBRATION OF CYLINDR BASED ON VELOCITY AND DISPLACEMENT FEEDBACK

  • 摘要: 以单自由度二维圆柱为研究对象, 将速度反馈与位移反馈加入涡激振动理论模型中, 探究了速度反馈和位移反馈控制规律, 同时引入智能算法, 利用神经网络将流场信息映射到反馈增益大小, 采用遗传算法优化神经网络参数, 从而得到不同折合流速 ($ {U_r} $ = 3.5 ~ 8)下速度反馈与位移反馈较优组合, 由此提出圆柱涡激振动增强策略, 进而实现不同流速下圆柱涡激振动主动控制. 研究表明: 速度反馈与位移反馈为涡激振动系统提供了能量源, 激发了圆柱的振动和漩涡脱落, 使涡激振动的起振时间要早于未受控状态, 且振动频率高于未受控状态, 使圆柱的涡脱频率不再受圆柱固有频率的支配, 最终实现了在折合流速范围 $ {U_r} $ = 3.5 ~ 8 内使圆柱的振动幅值比稳定保持在目标振幅比(0.6 ~ 0.8)内, 这一结果说明外部激励能够控制结构的振动速度和起振时间; 同时将反馈增益约束引入智能控制算法模型中, 对原计算模型进一步优化, 使平均能耗 J 较无约束情况降低了 33.08%, 极大地减少了主动控制过程的能耗. 本研究可实现主动控制钝体涡激振动增强, 将有益于更有效地捕获风振能量.

     

  • 图  1  二维涡激振动系统结构示意图

    Figure  1.  Schematic diagram of a two-dimensional vortex-induced vibration system

    图  2  利用遗传算法寻找最优的神经网络结构参数的计算框架

    Figure  2.  A computational framework for the optimal neural network structural parameters by employing the genetic algorithm

    图  3  不同种群数量对最大适应值的影响

    Figure  3.  The effect of different population sizes on maximum adaptation values

    图  4  计算域, 边界条件和网格组成示意图

    Figure  4.  Schematic diagram of computational domain, boundary condition and mesh composition

    图  5  实验装置示意图

    Figure  5.  Schematic diagram of the experimental setup

    图  6  本文数值仿真结果与实验结果和文献结果对比

    Figure  6.  The numerical simulation results are compared with the experimental results and literature results

    图  7  速度反馈和位移反馈对振幅比的影响

    Figure  7.  The effect of velocity feedback and displacement feedback on vibration amplitude ratio

    7  速度反馈和位移反馈对振幅比的影响 (续)

    7.  The effect of velocity feedback and displacement feedback on vibration amplitude ratio (continued)

    图  8  折合流速$ {U_r} $ = 2.5 ~ 14.5时, 圆柱在4种神经网络的4×10组输出增益下涡激振动幅值比 (续)

    Figure  8.  The amplitude ratio of vortex-induced vibration of the cylinder under the output gain of 4 × 10 groups of 4 neural networks (Ur=2.5 ~ 14.5) (continued)

    图  9  不施加约束和施加约束情况下10个不同的样本的J值, 种群数量为100

    Figure  9.  J values for 10 different samples without and with constraints imposed, the population size is 100

    图  10  (a) ~ (b)施加反馈约束后的输出反馈增益以及(c) ~ (f)反馈增益下的振动幅值比

    Figure  10.  (a) ~ (b) Output feedback gain with feedback constraint applied and (c) ~ (f) vibration amplitude ratio at feedback gain

    图  11  圆柱振动涡量图, “ + ”符号表示圆柱的平衡位置

    Figure  11.  Vibration vortex structures of the cylinder , The symbol “+” indicates the equilibrium position of the cylinder

    图  12  圆柱振动位移和速度相图

    Figure  12.  Diagram of vibration displacement and velocity of the cylinder

    图  13  折合流速$ {U_r} $ = 3.5, 8时, 圆柱的振动位移和升力系数时间历程变化曲线

    Figure  13.  The time history of the vibration displacement and lift coefficient of the cylinder (Ur =3.5, 8)

    图  14  圆柱位移和升力的频谱图

    Figure  14.  Spectrogram of displacement and lift force of the cylinder

    表  1  计算模型参数

    Table  1.   The parameters of computational model

    Namem/kgD/m$ \xi $ω0/(rad·s−1)$ {\rho _f} $/(kg·m−3)CL0CDλ PSt
    size0.0010.020.000 562.831.290.31.20.3120.2
    下载: 导出CSV

    表  2  遗传算法参数设置

    Table  2.   Genetic algorithm parameters

    Genetic algorithm parameterEvolution generationPopulation sizeIndividuals of tournament selectionCrossover rateMutation rate
    number3010060.70.45
    下载: 导出CSV

    表  3  重叠网格数量无关性验证(无反馈控制, ${{\boldsymbol{U}}_{\boldsymbol{r}}}$ = 5)

    Table  3.   Validation of the number of overlapping meshes (no feedback control, $ {U_r} $ = 5)

    Grid typeBackground grid (H × H)Foreground grid
    (L × d)
    Number of gridsTime steps (∆t)Amplitude ratio (A/D)
    M1150 × 15050 × 16066 1530.003 s0.686
    M2200 × 20080 × 240104 3530.003 s0.694 (1.166%)
    M3250 × 250100 × 320169 0430.003 s0.697 (0.432%)
    M4200 × 20080 × 240104 3530.001 s0.690 (0.576%)
    M5200 × 20080 × 240104 3530.006 s0.652 (6.052%)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-15
  • 录用日期:  2023-08-07
  • 网络出版日期:  2023-08-08
  • 刊出日期:  2023-09-18

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