Nonlinear Structural Discrete-Time Integration Method Based on the Update of Particle Swarm Optimization Model

Through the model update method based on particle swarm optimization, the structural model parameters are optimized, and the problem of increasing calculation time in the explicit integral algorithm under higher-order mode is solved, and high-precision and high-efficiency structural dynamic analysis is realized, which is suitable for complex nonlinear structures.

CN116187172BActive Publication Date: 2025-05-27DALIAN UNIV OF TECH
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Patent Information

Application Number
CN202310032541.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-01-10
Publication Date
2025-05-27
Estimated Expiration
2043-01-10

AI Technical Summary

Technical Problem

The existing explicit integration algorithm requires a small time step in higher-order modes, resulting in an increase in calculation time. Under the action of high-intensity earthquakes, the stiffness and mass changes of the nonlinear structure make it difficult for traditional integration methods to ensure calculation accuracy and efficiency.

Method used

The model update method based on particle swarm optimization is adopted, and the optimal integral parameters are obtained by optimizing the parameters of the structural model, and the iterative gain matrix is ​​updated to ensure that the algorithm has unconditional stability and high numerical damping controllability under higher-order modes.

Benefits of technology

It realizes structural dynamic analysis with high precision and high computational efficiency in nonlinear systems, and is suitable for finite element analysis and real-time substructure experiments of complex nonlinear structures.

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Abstract

The present invention discloses a discrete-time integration method for nonlinear structures based on the update of a particle swarm optimization model. In the first step, the parameters of the particle swarm optimization integration algorithm are set. In the second step, according to the discrete-time domain formula of the structural dynamics equation, the initial parameters and initial conditions of the structure, the particle swarm optimization algorithm is used to obtain the optimal integration parameters. In the third step, the obtained optimal integration parameters are substituted into the iteration matrix, and the discrete-time domain motion equation is iteratively solved until the preset time condition is reached, and the change range of the structural characteristic parameters is output. N points with equidistant distribution are taken within the change range of the structural characteristic parameters, and according to the structural characteristic parameters corresponding to each point, particle swarm optimization is carried out to obtain the corresponding N groups of optimal integration parameters. In the fourth step, the motion equation is iteratively solved, and in each time step, the optimal integration parameters in the corresponding interval are selected according to the value of the structural characteristic parameters for calculation until the preset time limit is reached, and the structural response time history curve is output.
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