基于粒子群算法的钢丝网垫隔振器参数识别
Parameter Identification of Steel-net Pad Vibration Isolator Based on Particle Swarm Optimization Algorithms
王威 1王轲 1万竞择1
作者信息
- 1. 南京航空航天大学 机械结构力学及控制国家重点实验室,南京 210016
- 折叠
摘要
目的 证明粒子群优化算法对钢丝网垫隔振器参数识别的有效性和准确性.探究不同的加载条件对钢丝网垫元件力学性能的影响.方法 对钢丝网垫元件进行正弦加载试验,获取试验响应数据.以双折线模型对钢丝网垫的力学性能进行描述,建立隔振器的参数识别方程,应用粒子群算法和试验响应数据识别出模型参数,并研究不同加载条件下识别参数的变化规律.结果 用识别出的参数计算绘制的迟滞回线和试验结果吻合较好.改变激励幅值和频率,钢丝网垫的一次刚度、三次刚度、滑移刚度、等效阻尼均发生规律性变化,而改变钢丝网垫所受静载,曲线变化极为微小.结论 研究证明了粒子群优化算法对钢丝网垫隔振器参数识别的有效性和准确性,是一种有工程应用价值的参数识别方法.
Abstract
The work aims to prove the effectiveness and accuracy of particle swarm optimization algorithm for parameter identification of steel-net pad vibration isolator and investigate the effects of different loading conditions on the mechanical properties of steel-net pad.The sinusoidal loading test was carried out on the steel-net pad to obtain the test response data.The mechanical properties of the steel-net pad were described by the bilinear constitutive model,and the parameter identification equation of the vibration isolator was established.The particle swarm optimization algorithm and the test response data were used to identify the model parameters,and the variation of the identification parameters under different loading conditions was studied.The hysteresis loop calculated by the identified parameters was in good agreement with the experimental results.By changing the excitation amplitude and frequency,the primary stiffness,tertiary stiffness,slip stiffness and equivalent damping of the steel-net pad all changed regularly,while by changing the static load of the steel mesh pad,the curve changed very little.The study proves the effectiveness and accuracy of the particle swarm optimization algorithm in identifying the parameters of steel-net pad vibration isolator.It represents a parameter identification method with significant engineering application value.
关键词
钢丝网垫/双折线模型/粒子群算法/参数识别/迟滞回线/非线性力学Key words
steel-netpad/bilinear constitutive model/particle swarm optimization/parameter identification/hysteresis loop/nonlinear mechanics引用本文复制引用
出版年
2024
NETL
NSTL
万方数据