Browse by author
Lookup NU author(s): Dr Xiaoyi Zhou,
Professor Peter Gosling
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This paper investigates two critical issues, namely propagation of multi-scale uncertainty, and selection of failure criteria, related to reliability analysis of composites by using multi-scale methods. Due to the multi-scale architecture of composites, uncertainties exist in both microscale and macroscale parameters. It is necessary, therefore,, to consider random variables at various length scales to ensure accurate estimates of the reliability of composites. Three types of homogenization methods, namely rule of mixtures, Mori-Tanaka and computational homogenization, are adopted to link these two scales, and to propagate uncertainty from micro to macro scales. By integrating these homogenization methods with the stochastic finite element method and structural reliability methods, the reliability of composites can be investigated with a limit state function based on a chosen failure criterion. This multi-scale reliability analysis procedure has been applied to analyse laminated fibre reinforced composites made of AS4/3501 carbon/epoxy. Firstly, a comparative study has been conducted to evaluate the performance of the assumed homogenization methods for the reliability of composites, and to identify advantages compared with a single scale analysis. The results show that multi-scale analysis can provide more accurate reliability estimates. Secondly, several popularly used failure criteria for composites have been compared using multi scale reliability analysis. (C) 2016 The Authors. Published by Elsevier Ltd.
Author(s): Zhou XY, Gosling PD, Ullah Z, Kaczmarczyk Ł, Pearce CJ
Publication type: Article
Publication status: Published
Journal: Composite Structures
Print publication date: 01/11/2016
Online publication date: 09/08/2016
Acceptance date: 08/08/2016
Date deposited: 14/11/2016
ISSN (print): 0263-8223
ISSN (electronic): 1879-1085
Publisher: Elsevier Science Ltd.
Data Source Location: http://dx.doi.org/10.17634/122849-1
Altmetrics provided by Altmetric