Evolutionary response analysis of Duffing oscillator using Gaussian equivalent linearization in wavelet based time-frequency frame work is presented here. Cubic （i.e., odd type） non-linearity associated with stiffne...Evolutionary response analysis of Duffing oscillator using Gaussian equivalent linearization in wavelet based time-frequency frame work is presented here. Cubic （i.e., odd type） non-linearity associated with stiffness and damping is modeled. The goal of this research is to develop the mathematical model of an equivalent linear system which is applicable for different non-stationary input processes （i.e., either summation of amplitude modulated stationary orthogonal processes or digitally simulated non-stationary processes）. The instantaneous parameters of the ELTVS （equivalent linear time varying system） are evaluated by minimizing the error between the displacements of non-linear and equivalent linear systems in wavelet domain. For this purpose, three different basis functions （i.e., Mexican Hat, Morlet and a modified form of Littlewood-Paley） are used. The unknown parameters （i.e., natural frequency and damping） of the ELTVS are optimized in stochastic least square sense. Numerical results are presented for different types of input to show the applicability and accuracy of the proposed wavelet based linearization technique.展开更多
文摘Evolutionary response analysis of Duffing oscillator using Gaussian equivalent linearization in wavelet based time-frequency frame work is presented here. Cubic （i.e., odd type） non-linearity associated with stiffness and damping is modeled. The goal of this research is to develop the mathematical model of an equivalent linear system which is applicable for different non-stationary input processes （i.e., either summation of amplitude modulated stationary orthogonal processes or digitally simulated non-stationary processes）. The instantaneous parameters of the ELTVS （equivalent linear time varying system） are evaluated by minimizing the error between the displacements of non-linear and equivalent linear systems in wavelet domain. For this purpose, three different basis functions （i.e., Mexican Hat, Morlet and a modified form of Littlewood-Paley） are used. The unknown parameters （i.e., natural frequency and damping） of the ELTVS are optimized in stochastic least square sense. Numerical results are presented for different types of input to show the applicability and accuracy of the proposed wavelet based linearization technique.
