Effect of uncertainty on dynamic damping and stiffness of spherical hollow rubber isolators based on harmonic experiment. (May 2022)

• Record Type: Journal Article
• Authors: Li, Hao; Yang, Chengliang; Wang, Shaoxin; Su, Ping; Zhang, Xingyun; Peng, Zenghui; Mu, Quanquan
• Is Part Of: Polymer testing. Volume 109(2022)
• Publication Date: 2022-05
• Subjects: Spherical rubber isolator -- Dynamic stiffness -- Dynamic damping -- Uncertain parameters -- Resonant frequency; Polymers -- Testing -- Periodicals; Polymères -- Tests -- Périodiques; 620.1920287
• DOI: 10.1016/j.polymertesting.2022.107544
• Languages: English
• ISSNs: 0142-9418

Abstract

Abstract: This study aims to identify the effect of uncertainty on the dynamic characteristics of a thin-walled hollow spherical rubber isolator. Firstly, the nonlinearity and uncertainties of the spherical rubber isolator were obtained by swept-sine experiments. The relationship between dynamic stiffness and damping against relative displacement amplitude was established. Then, a high-order polynomial function with uncertain parameters introduced was used for simulation. The uncertain parameters were quantified using the singular value decomposition and Monte Carlo simulations. Furthermore, the dynamic stiffness and damping of the experimental data were optimized using the Covariance Matrix Adaptation and Evolution Strategy. Finally, the high consistency between the simulation results and experimental results were demonstrated. Overall, the nonlinear model established according to the uncertain parameter quantization is feasible. This method can predict the nonlinear characteristics of the rubber isolator well and also may be applied to other nonlinear models. Highlights: The dynamic characteristics of a thin-walled spherical hollow rubber isolator was studied. A high-order polynomial function was used for numerical simulations. Uncertain parameters were introduced into the nonlinear modeling and improved the accuracy of simulation results. The dynamic stiffness and damping of the experimental data were further optimized using the CMA-ES.[1–16 ]