Vibration Suppression Study of Parallel Combined Damping Nonlinear Energy Sink on Piecewise Linear Systems

Journal: Architecture Engineering and Science DOI: 10.32629/aes.v4i4.1460

Xingke Qi1, Jianchao Zhang2, Jun Wang3

1. Department of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China
2. State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China
3. Department of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China; State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China

Abstract

The Nonlinear Energy Sink (NES) is a passive control device capable of achieving targeted energy transfer and effectively suppressing system vibrations. This paper conducts dynamic modeling and vibration suppression analysis of parallel combined damping nonlinear energy sinks in piecewise linear systems. Initially, the system's amplitude-frequency response curve is obtained using the harmonic balance method, validated by the Runge-Kutta method for accuracy. Subsequently, an analysis is performed on the vibration reduction capabilities of the parallel combined damping nonlinear energy sink and the influence of parameters on its vibration suppression effectiveness. Finally, the study investigates changes in the main system's maximum response amplitude with varying damping under different stiffness conditions and determines optimal parameter values based on trend analysis. Research findings indicate that the vibration reduction performance of the parallel combined damping nonlinear energy sink surpasses that of a single connected damping nonlinear energy sink. Furthermore, after parameter optimization, the main system achieves superior vibration reduction effects.

Keywords

Nonlinear Energy Sink; Harmonic Balance Method; Vibration Reduction Performance; Parameter Optimization

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Copyright © 2023 Xingke Qi, Jianchao Zhang, Jun Wang

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