Uncertainty Quantification in Experimental Structural Dynamics Identification of Composite Material Structures

Marcin Luczak, Bart Peeters, Maciej Kahsin, Simone Manzato, Kim Branner

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearch

Abstract

Aerospace and wind energy structures are extensively using components made of composite materials. Since these structures are subjected to dynamic environments with time-varying loading conditions, it is important to model their dynamic behavior and validate these models by means of vibration experiments. Modal testing results depend on numerous factors introducing uncertainty to the measurement results. Different experimental techniques applied to the same test item or testing numerous nominally identical specimens yields different test results. This paper presents a systematic approach for uncertainty evaluation in experimentally estimated models. Investigated structures are plates, fuselage panels and helicopter main rotor blades as they represent different complexity levels ranging from coupon, through sub-component up to fully assembled structures made of composite materials. To evaluate the variability of the identified parameters, a statistical method is implemented for processing an extensive collection of experimental data.
Original languageEnglish
Title of host publicationModel Validation and Uncertainty Quantification
EditorsH.S. Atamturktur et al.
PublisherSociety for Experimental Mechanics, Inc.
Publication date2014
Pages403-408
Chapter40
DOIs
Publication statusPublished - 2014
Event32nd IMAC Conference and Exposition on Structural Dynamics - Brescia, Italy
Duration: 1 Jul 20125 Jul 2012

Conference

Conference32nd IMAC Conference and Exposition on Structural Dynamics
Country/TerritoryItaly
CityBrescia
Period01/07/201205/07/2012
SeriesConference Proceedings of the Society for Experimental Mechanics Series
Volume3

Keywords

  • Modal analysis
  • Composite material structures
  • Uncertainty quantification
  • Statistical analysis
  • Variability

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