|Table of Contents|

Parameter identification of frame structure heterogeneous system model based on hybrid quantum particle swarm and standard particle swarm algorithm

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2020年02期
Page:
177-184
Research Field:
Publishing date:

Info

Title:
Parameter identification of frame structure heterogeneous system model based on hybrid quantum particle swarm and standard particle swarm algorithm
Author(s):
Zhang QianDing Jianguo
School of Science,Nanjing University of Science and Technology,Nanjing 210094,China
Keywords:
multibody system transfer matrix method frame structure mechanical model hybrid quantum particle swarm and standard particle swarm algorithm parameter identification
PACS:
TU311
DOI:
10.14177/j.cnki.32-1397n.2020.44.02.008
Abstract:
In order to study the seismic collision problem of adjacent frame structures,the dynamic model of the frame structure is often simplified to a tree topology to improve its calculation efficiency. This paper proposes a parameter identification method that combines the quantum particle swarm and the standard particle swarm. Based on the principle of structural dynamic characteristic equivalence,the topological system dynamic model of the frame structure suitable for efficient calculation of the multibody system transfer matrix method(MS-TMM)is established. The relevant mechanical parameters of this dynamic model are calculated by the finite element method. The modal parameters are determined based on the parameter identification of the hydrid quantum particle swarm algorithm and the standard particle swarm algorithm. In this paper,a three-layer frame structure is used as an engineering example. The three-layer frame structure is transformed into a tree-topological mechanics model. The parameter identification method and standard quantum particle swarm algorithm proposed in this paper are used to identify the parameters. The multi-body system transfer matrix method(MS-TMM)is used to calculate the frequency,and the frequency calculation results of the two methods are compared with the frequencies calculated by the Ansys. The results show that the parameter identification method proposed in this paper is supperior to the standard quantum particle swarm algorithm in terms of identification accuracy.

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Last Update: 2020-04-20