Probabilistic analysis for mechanical properties of glass/epoxy composites using homogenization method and Monte Carlo simulation
Seung-Pyo Lee,
Ji-Won Jin and
Ki-Weon Kang
Renewable Energy, 2014, vol. 65, issue C, 219-226
Abstract:
In this paper, we used a homogenization method to estimate the equivalent properties of composite material, which depend on a microstructure and behavior of constituents (fiber and matrix). The estimated results conform well to the results of the rule of mixtures, which is a conventional method for estimating equivalent properties of composite materials. In addition, to assess the uncertainty of the equivalent properties of the composite materials according to the variability in the basic properties of the constituents, a probabilistic analysis using a homogenization-based Monte Carlo simulation was carried out. In this way, the variation of basic properties of the constituents was identified to have a significant effect on the uncertainty of the equivalent properties of composite materials. Moreover, a sensitivity of the properties of the constituents on the equivalent properties was assessed through a correlation and regression analysis.
Keywords: Glass/epoxy composites; Homogenization; Mechanical properties; Microstructure; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2014
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148113004795
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:65:y:2014:i:c:p:219-226
DOI: 10.1016/j.renene.2013.09.012
Access Statistics for this article
Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides
More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().