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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2023, Vol. 44 ›› Issue (7): 226559.doi: 10.7527/S1000-6893.2022.26559

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles     Next Articles

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)

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

CLC Number: