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International Journal for Multiscale Computational Engineering
Импакт фактор: 1.016 5-летний Импакт фактор: 1.194 SJR: 0.452 SNIP: 0.68 CiteScore™: 1.18

ISSN Печать: 1543-1649
ISSN Онлайн: 1940-4352

Выпуски:
Том 17, 2019 Том 16, 2018 Том 15, 2017 Том 14, 2016 Том 13, 2015 Том 12, 2014 Том 11, 2013 Том 10, 2012 Том 9, 2011 Том 8, 2010 Том 7, 2009 Том 6, 2008 Том 5, 2007 Том 4, 2006 Том 3, 2005 Том 2, 2004 Том 1, 2003

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.v6.i4.70
pages 361-370

Homogenization of Fiber-Reinforced Composites under the Stochastic Aging Process

Marcin Kaminski
Faculty of Civil Engineering, Architecture and Environmental Engineering, Technical University of Lodz, Poland

Краткое описание

The main aim of this work is to develop a homogenization method for the periodic fiber-reinforced composite materials whose components are subjected to the stochastic aging processes. This homogenization method is entirely based on the effective modules method and the stochastic aging processes are modeled as the linear decay of the Young's moduli for the fiber and the matrix from their initial expectations and standard deviations using the aging velocity defined as the random variable; no stochastic structural or interface defects are considered. Both initial values of those parameters as well as their aging velocities are assumed to be the Gaussian random variables with the specified first two probabilistic moments. Computational implementation of the stochastic composite model is provided using the Monte Carlo simulation method implemented on the homogenization-oriented finite element method code MCCEFF, where the stochastic behavior of the homogenized tensor probabilistic moments are modeled in certain time moments through the aging history. The specific case of glass fibers embedded into epoxy resin shows that the governing influence on this composite effective characteristics has the aging process in the matrix; the fiber aging can be of secondary importance depending only on the particular data.


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