Dimensionless and Parameter Identification of Dynamic System of Beam on Winkler Foundation
In order to simplify the dual parameter identification calculation of the dynamic system of the beam on Winkler Foundation,a new dimensionless method is proposed.The time and space coordinates of the system are linearly transformed to realize the complete normalization of the coefficients of the dy-namic equation.The generalized frequency equation decoupled from the system parameters is obtained.It is found that the frequency and frequency ratio are only determined by the dimensionless span.Based on this discovery,a dual parameter identification algorithm based on the reciprocal relationship of frequency ratios is proposed.This algorithm can obtain the resolvent set of frequency and frequency ratio with re-spect to the dimensionless span under the corresponding boundary conditions by solving the generalized frequency equation once.After obtaining any two order measured frequencies of the system,the dual system parameters can be determined by relying on the linear transformation relationship established by the time and space reduction coefficients.Compared with the traditional two parameter identification al-gorithm,this algorithm has two characteristics:(1)The identification calculation only involves the solu-tion and linear transformation of the univariate transcendental equation,which avoids the nonlinear itera-tion problem of the two parameter transcendental equation set in the traditional method,and can effec-tively simplify the identification calculation.(2)The change of any system parameter value only affects the size of the time and space reduction coefficients.Therefore,the resolvent set has generality applicable to any change of system parameter value,which can effectively avoid the repeated iterative solution caused by the change of system parameter value in traditional methods,and realize the generalization of the solution.
beam on winkler foundationdynamic systemdimensionlessnormalizationparam-eter identification
万方数据
维普
