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Exploiting the benefits of multi-scale analysis in reliability analysis for composite structures

Lookup NU author(s): Dr Xiaoyi Zhou, Professor Peter Gosling

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

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.


Publication metadata

Author(s): Zhou XY, Gosling PD, Ullah Z, Kaczmarczyk Ł, Pearce CJ

Publication type: Article

Publication status: Published

Journal: Composite Structures

Year: 2016

Volume: 155

Pages: 197-212

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.

URL: http://dx.doi.org/10.1016/j.compstruct.2016.08.015

DOI: 10.1016/j.compstruct.2016.08.015

Data Source Location: http://dx.doi.org/10.17634/122849-1


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