航空学报 > 2023, Vol. 44 Issue (7): 226559-226559   doi: 10.7527/S1000-6893.2022.26559

基于新型粒子群算法的结构动力学热振模型修正

王震宇1,2, 王计真3, 杨婧艺1,2, 何鹏远1,2, 潘成浩1,2, 周凌波4, 何成5, 何欢1,2()   

  1. 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
    2.南京航空航天大学 振动工程研究所,南京 210016
    3.中国飞机强度研究所 结构冲击动力学航空科技重点实验室,西安 710065
    4.中国船舶科学研究中心 船舶振动噪声重点实验室,无锡 214082
    5.南京航空航天大学 无人机研究院,南京 210016
  • 收稿日期:2021-10-25 修回日期:2022-02-11 接受日期:2022-03-15 出版日期:2023-04-15 发布日期:2022-03-22
  • 通讯作者: 何欢 E-mail:hehuan@nuaa.edu.cn
  • 基金资助:
    国家自然科学基金(12072153);江苏高等学校重点学术项目发展与机械结构力学与控制国家重点实验室研究基金(南京航空航天大学)(MCMS-I-0121G01)

Dynamics model updating of structures at high temperature based on novel particle swarm optimization algorithm

Zhenyu WANG1,2, Jizhen WANG3, Jingyi YANG1,2, Pengyuan HE1,2, Chenghao PAN1,2, Lingbo ZHOU4, Cheng HE5, Huan HE1,2()   

  1. 1.State Key Laboratory of Mechanics and Control of Mechanical Structures,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
    2.Institute of Vibration Engineering Research,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
    3.Key Laboratory of Aviation Science and Technology on Structures Impact Dynamics,China Aircraft Strength Research Institute,Xi’an 710065,China
    4.National Key Laboratory on Ship Vibration & Noise,China Ship Scientific Research Center,Wuxi 214082,China
    5.Research Institute of Unmanned Aircraft,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • Received:2021-10-25 Revised:2022-02-11 Accepted:2022-03-15 Online:2023-04-15 Published:2022-03-22
  • Contact: Huan HE E-mail:hehuan@nuaa.edu.cn
  • Supported by:
    National Natural Science Foundation of China(12072153);the Priority Academic Program Development of Jiangsu Higher Education Institutions and the Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures (Nanjing University of Aeronautics and astronautics)(MCMS-I-0121G01)

摘要:

提出一种基于新型粒子群算法的结构动力学热振模型修正方法,并成功应用于高温环境下典型复杂多组件结构的模型修正问题。为了克服粒子群算法解决模型修正等非线性优化问题时早熟收敛的缺点,联合莱维飞行策略和正交学习方法提出莱维正交学习粒子群优化算法;将该新型方法和其他优化算法应用于修正轴系轴承-轴承座的等效刚度和阻尼参数进行对比分析,结果证明该新型算法具有更高的精度;针对典型复杂多组件结构在高温环境下的振动实验进行模型修正,修正后不同温度环境下各阶模态频率误差均下降到7%以内,有限元模型精度得到极大提高,表明该新型算法可以有效应用于工程实际。

关键词: 粒子群算法, 莱维飞行, 正交学习, 高温环境, 有限元, 模型修正

Abstract:

A dynamics model updating method for structures in high temperature environments based on a novel Particle Swarm Optimization (PSO) algorithm is proposed and successfully applied to the updating of the Finite Element (FE) model of a typical complex multi-component structure. The LOPSO algorithm was first designed by combining the Lévy flight strategy and the orthogonal learning method to overcome premature convergence while the PSO solves nonlinear optimization problems such as parameter identification and model updating. Then the proposed method was used to update the equivalent stiffness and damping parameters of shafting bearings. It is proved that the new algorithm has higher accuracy. Finally, we updated the FE model of a typical complex multi-component structure in high temperature environment based on the vibration experiment by the LOPSO method. After updating, the errors of the main modal frequencies in different temperature environments were reduced to less than 7%. The accuracy of the FE model was significantly improved, verifying the effectiveness of the proposed method and its applicability to engineering problems.

Key words: particle swarm optimization, Lévy flight, orthogonal learning, high temperature, finite element (FE), model updati

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