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Bistable shock isolator tuned to zero-frequency singularity for platform protection

  • Jinhong Noh
  • , Pilkee Kim*
  • , Yong Jin Yoon*
  • *Corresponding author for this work
  • Korea Advanced Institute of Science and Technology
  • Nanyang Technological University

Research output: Contribution to journalJournal articlepeer-review

Abstract

In this study, we examine a bistable shock isolator (BSI) and its shock-transmitting behaviors, particularly when tuned to the zero-frequency singularity (ZFS) condition. Under this condition, a snap-through transition occurs, and the BSI oscillation exhibits critical slowing down. By tuning the BSI to the ZFS condition, harmful shock transmission to the protected platform can be significantly delayed, allowing protective measures to be implemented before the transmission of dangerous forces. Moreover, the magnitude of the transmitted force is reduced considerably. We establish a mathematical model of the BSI and theoretically analyze the ZFS. Using numerical investigations, we demonstrate the benefits of the ZFS in terms of shock-isolation performance, including delay and mitigation of force transmission. Parametric and spectral analyses further confirm the benefits of the BSI. We validate the existence of the ZFS and its effects from the perspective of shock isolation by developing a BSI prototype and conducting experiments. Furthermore, the need for future research focusing on the BSI in real-world scenarios is discussed. It is believed that the findings reported herein will pave the way for advances in shock-protection technologies based on nonlinear bistable systems.

Original languageEnglish
Article number111158
JournalMechanical Systems and Signal Processing
Volume210
DOIs
StatePublished - 2024.03.15

Keywords

  • Bistable oscillator
  • Double-well duffing oscillator
  • Nonlinear shock isolation
  • Snap-through
  • Zero-frequency singularity

Quacquarelli Symonds(QS) Subject Topics

  • Engineering - Mechanical
  • Computer Science & Information Systems
  • Engineering - Civil & Structural
  • Data Science

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