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Parameter inversion method of double wake oscillators model with vortex induced response |
Received:March 26, 2020 Revised:May 18, 2020 |
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DOI:10.7511/jslx20200326001 |
KeyWord:double wake oscillators model vortex induced vibration BP neural network parameter inversion vibration test |
Author | Institution |
颜凝雨 |
西北工业大学 数学与统计学院, 西安 |
杨自豪 |
西北工业大学 数学与统计学院, 西安 |
张洁琼 |
西北工业大学 数学与统计学院, 西安 |
林巍 |
中交悬浮隧道工程技术联合研究组, 珠海 |
刘孟源 |
中交悬浮隧道工程技术联合研究组, 珠海 |
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Abstract: |
The double-wake oscillator model is an important model to study the vortex induced vibration response of a cylindrical structure,and the accuracy of model parameters is of great significance to the theoretical design of a submerged floating tunnel.Firstly,the double-wake oscillator model with the form of multivariable second order nonlinear ordinary differential equations is transformed into the first order equations by the method of order reduction.Then,a new test design scheme of the cylindrical structure is given,in which the stiffness of the experimental model can better reflect the stiffness of the practical engineering structure such as the submerged floating tunnel,and through the camera dynamic capture and computer program of video recognition,the displacements of the cylindrical structure in horizontal and vertical directions are obtained.Based on the test results and Runge-Kutta method,BP neural network intelligent algorithm is used to the inversion of model parameters,and a genetic algorithm is used to optimize the initial weights and thresholds of neurons.The average error of the results is only 5.50%,which is superior to the genetic algorithm and the unoptimized BP neural network model.The results show that the BP neural network intelligent algorithm based on the genetic algorithm optimization can determine the parameters of the double-wake oscillator model accurately,which will provide theoretical basis for the vortex induced vibration response analysis of cylindrical structures. |
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