Stochastic damage evolution modeling in laminates

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A stochastic mesomechanics model has been developed for damage accumulation analysis in advanced laminated composites. The model is based on a theory of excursions of random process beyond the bounds. Stochastic strains in the laminate subjected to random Gaussian in-plane loading are calculated using lamination theory and random functions theory. Probabilistic variation of stiffness and strength characteristics of plies is used in the analysis. A stochastic version of the maximum strain failure criterion is applied for damage probability calculation. A mesovolume concept is utilized in modeling stiffness degradation. The capabilities of the model are illustrated by predictions of damage accumulation and failure in a Kevlar®/epoxy [0/±30/90], laminate under quasistationary, long-term stationary, and cyclic loading. Effects of loading rate, deviation, stationary level, and cyclic amplitude on damage evolution are discussed. Inversion of the influence of loading deviation on laminate strength with varying loading rate is revealed and interpreted. High-cycle fatigue behavior of the laminate is calculated utilizing the observed stages in failure accumulation under cyclic loading.

Original languageEnglish (US)
Pages (from-to)21-34
Number of pages14
JournalJournal of Thermoplastic Composite Materials
Volume9
Issue number1
DOIs
StatePublished - Jan 1996

Fingerprint

laminates
Laminates
damage
loading rate
Stiffness
stiffness
Kevlar (trademark)
Laminated composites
deviation
Stochastic models
Random processes
random processes
Fatigue of materials
Degradation
inversions
degradation
cycles
composite materials
predictions

ASJC Scopus subject areas

  • Ceramics and Composites
  • Condensed Matter Physics

Cite this

Stochastic damage evolution modeling in laminates. / Dzenis, Y. A.

In: Journal of Thermoplastic Composite Materials, Vol. 9, No. 1, 01.1996, p. 21-34.

Research output: Contribution to journalArticle

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