A nonlinear dynamic model and parameters identification method for predicting the shock pulse of rubber waveform generator. (October 2018)

• Record Type: Journal Article
• Authors: Wen, Jingjing; Liu, Chengwu; Yao, Houpu; Wu, Bin
• Is Part Of: International journal of impact engineering. Volume 120(2018)
• Page Start: 1
• Page End: 15
• Publication Date: 2018-10
• Subjects: Nonlinear dynamic model -- Rubber waveform generator -- Shock test -- Parameter identification -- Multiple population genetic algorithm; Impact -- Periodicals; Shock (Mechanics) -- Periodicals; Impact -- Périodiques; Choc (Mécanique) -- Périodiques; Impact; Shock (Mechanics); Periodicals; 620.1125
• DOI: 10.1016/j.ijimpeng.2018.05.009
• Languages: English
• ISSNs: 0734-743X

Abstract

Highlights: A nonlinear dynamic model for describing the shock characteristics of rubber waveform generator (RWG) was established. The proposed model considers the nonlinear viscoelasticity, the geometric factor of RWG and initial shock velocities. A multiple population genetic algorithm basedmethod was proposed to determine the model parameters and predict shock pulses. The proposed method considers the holistic shape of shock pulse instead of the mere peak acceleration and/or pulse duration. An inverse problem about how to calculate the parameters of theproposed model according to given shock pulses was solved. Abstract: This paper proposed a highly accurate nonlinear dynamic model and parameters identification method to predict the shock pulses generated by rubber waveform generator (RWG) used in shock test. The establishment of the model is based on the primary results of the Duffing equation, Pan-Yang model and empirical stiffness formulae of cylindrical rubber isolator. We proposed a series of measures to eliminate the interferences in the drop shock test experiment, which are also of benefit to design, regulate and calibrate drop shock testers. The overall nonlinear system is solved in time domain with Runge–Kutta. And the parameters of the nonlinear dynamic model are identified by minimizing the holistic shape difference between experimental and predicted shock pulses with multiple population genetic algorithm. Meanwhile, the capacity of this model in characterizing the dynamic behavior of RWG under shock excitations is also investigated. Compared with previous work, our model considers nonlinear viscoelasticity of RWG, the geometric factor of RWG and the effect of initial shock velocity, which makes our model more accurate and general. Experimental results shown great agreements with the predictions and that the proposed method can determine the parameters conveniently and synergistically.