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Multi-scale computational homogenisation to predict the long-term durability of composite structures

Ullah, Z.; Kaczmarczyk, Ł.; Grammatikos, S.A.; Evernden, M.C.; Pearce, C.J.

Authors

Ł. Kaczmarczyk

S.A. Grammatikos

M.C. Evernden

C.J. Pearce



Abstract

A coupled hygro-thermo-mechanical computational model is proposed for fibre reinforced polymers, formulated within the framework of Computational Homogenisation (CH). At each macrostructure Gauss point, constitutive matrices for thermal, moisture transport and mechanical responses are calculated from CH of the underlying representative volume element (RVE). A degradation model, developed from experimental data relating evolution of mechanical properties over time for a given exposure temperature and moisture concentration is also developed and incorporated in the proposed computational model. A unified approach is used to impose the RVE boundary conditions, which allows convenient switching between linear Dirichlet, uniform Neumann and periodic boundary conditions. A plain weave textile composite RVE consisting of yarns embedded in a matrix is considered in this case. Matrix and yarns are considered as isotropic and transversely isotropic materials respectively. Furthermore, the computational framework utilises hierarchic basis functions and designed to take advantage of distributed memory high performance computing.

Citation

Ullah, Z., Kaczmarczyk, Ł., Grammatikos, S., Evernden, M., & Pearce, C. (2017). Multi-scale computational homogenisation to predict the long-term durability of composite structures. Computers and Structures, 181, 21-31. https://doi.org/10.1016/j.compstruc.2016.11.002

Journal Article Type Article
Acceptance Date Nov 1, 2016
Online Publication Date Nov 14, 2016
Publication Date 2017-03
Deposit Date Sep 11, 2024
Journal Computers and Structures
Print ISSN 0045-7949
Electronic ISSN 1879-2243
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 181
Pages 21-31
DOI https://doi.org/10.1016/j.compstruc.2016.11.002
Public URL https://durham-repository.worktribe.com/output/2852109