Smart Structures and Systems

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Analysis and optimization of a typical quasi-zero stiffness vibration isolator

  • Li, Huan (School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Yu, Yang (School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Li, Jianchun (School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Li, Yancheng (School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney)
  • Received : 2020.01.06
  • Accepted : 2021.01.15
  • Published : 2021.03.25
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Abstract

To isolate vibration at a low-frequency range and at the same time to provide sufficient loading support to the isolated structure impose a challenge in vibration isolation. Quasi-zero stiffness (QZS) vibration isolator, as a potential solution to the challenge, has been widely investigated due to its unique property of high-static & low-dynamic stiffness. This paper provides an in-depth analysis and potential optimization of a typical QZS vibration isolator to illustrate the complexity and importance of design optimization. By carefully examining the governing fundamentals of the QZS vibration isolator, a simplified approximation of force and stiffness relationship is derived to enable the characteristic analysis of the QZS vibration isolator. The explicit formulae of the amplitude-frequency response (AFR) and transmissibility of the QZS vibration isolator are obtained by employing the Harmonic Balance Method. The transmissibility curves under force excitation with different values of nonlinear coefficient, damping ratio, and amplitude of excitation are further investigated. As the result, an optimization of the structural parameter has been demonstrated using a comprehensive objective function with considering multiple dynamic characteristic parameters simultaneously. Finally, the genetic algorithm (GA) is adopted to minimise the objective function to obtain the optimal stiffness ratios under different conditions. General recommendations are provided and discussed in the end.

Keywords

Acknowledgement

The authors would like to acknowledge the research funding from Australian Research Council under Discovery Project Scheme (DP150102636) to support PhD scholarship in conducting this research.

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