PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 3, 20260 citations

Rayleigh damping parameters estimation using hammer impact tests

FIFernando Sánchez IglesiasALAntonio Fernández López

Key Points

  • Rayleigh damping parameters significantly enhance simulation reliability for structural health monitoring systems.
  • Results show precise parameter estimation based on transient signals from impact tests on carbon-fiber reinforced panels.
  • Analysis utilizes time-frequency methods and validates findings through explicit finite element simulations.
  • Improved damping estimates could aid in the assessment of aerospace composite structures and future applications.

Abstract

Structural Health Monitoring (SHM) systems for aerospace applications are becoming more prevalent and its potential cost-benefit relation is rapidly improving as more structures that were traditionally made with metallic materials are gradually being replaced by composites. To support these systems, simulations can play a crucial role; however, in the case of low energy impacts, the structural damping becomes a very significant element in the simulation. This damping is usually not well known, or is based in estimates with very low reliability. Therefore, in order to improve the reliability of these values this paper presents an attempt to estimate the Rayleigh damping parameters with the application of time-frequency analysis methods to transient signals, specifically, the reduced interference distribution. These parameters are estimated based in the results of a large number of structural tests done in a square carbon-fiber reinforced plastic panel, and are then validated by correlation of an explicit Finite Element Method (FEM) simulation. A discussion on how to apply the reduced interference distribution to the signals measured by piezoelectric sensors under an impact scenario and other possible usages of these results is also presented in this document. (C) 2019 Elsevier Ltd. All rights reserved.

Ask AI
Helpful
Bookmark
Share

Cite This Study

Iglesias et al. (2020) studied this question.

synapsesocial.com/papers/69a75d5cc6e9836116a27536
Ask AI
Helpful
Bookmark
Share