东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (12): 1732-1738.DOI: 10.12068/j.issn.1005-3026.2019.12.012

• 机械工程 • 上一篇    下一篇

分段线性刚度非线性能量阱的参数优化方法

姚红良, 张钦, 杨沛然, 闻邦椿   

  1. (东北大学 机械工程与自动化学院, 辽宁 沈阳110819)
  • 收稿日期:2019-01-24 修回日期:2019-01-24 出版日期:2019-12-15 发布日期:2019-12-12
  • 通讯作者: 姚红良
  • 作者简介:姚红良(1979-),男,河北唐县人,东北大学教授,博士生导师; 闻邦椿(1930-),男,浙江温岭人,东北大学教授,博士生导师,中国科学院院士.
  • 基金资助:
    国家自然科学基金资助项目(U1708257); 中央高校基本科研业务费专项资金资助项目(N180313009).

Optimization Method of Nonlinear Energy Sinks with Piecewise Linear Stiffness

YAO Hong-liang, ZHANG Qin, YANG Pei-ran, WEN Bang-chun   

  1. School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China.
  • Received:2019-01-24 Revised:2019-01-24 Online:2019-12-15 Published:2019-12-12
  • Contact: YAO Hong-liang
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摘要: 基于作者先前提出的具有分段线性刚度的非线性能量阱(nonlinear energy sink,NES)模型,利用遗传算法研究了该NES的参数优化问题.首先,介绍了分段线性刚度NES的结构,并分析了其非线性特性;其次,提出通过对立方曲线端点连线拟合的方法以缩减设计空间,并采用二进制与实数混合编码方式设计了NESs的参数优化方法;最后,以框剪结构-NESs系统为例,研究了NESs在多自由度系统中的优化问题,并通过试验进行了验证.结果表明,采用所提出的优化方法所设计的NESs,能在较宽的激励频率下有效减小振动系统的振幅,可以获得较好的多模态振动抑制效果.

关键词: 分段线性刚度, 非线性能量阱(NES), 参数优化, 遗传算法, 端点连线拟合

Abstract: Based on the nonlinear energy sink (NES) model with piecewise linear stiffness previously proposed by the authors, the optimization problem of the NESs is studied by means of genetic algorithm (GA). Firstly, the NES with piecewise linear stiffness is introduced, and its nonlinear characteristic is analyzed. Then, the spatial dimension of optimum design is reduced by adopting the endpoint connection fitting for cubic polynomial, and the optimization method is presented with mixed binary and real coding. Finally, the optimization of the NESs in multi-degree freedom system is carried out by taking the frame-shear structure-NESs system as an example, and its effectiveness is verified by experiments. The results show that the optimized NESs can effectively suppress vibration at a wider excitation frequency and meanwhile obtain a better multi-modal vibration suppression effect.

Key words: piecewise linear stiffness, nonlinear energy sink (NES), parameter optimization, genetic algorithm(GA), endpoint connection fitting

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