Article (Scientific journals)
Bayesian Identification of Mean-Field Homogenization model parameters and uncertain matrix behavior in non-aligned short fiber composites
Mahamedou, Mohamed; Zulueta Uriondo, Kepa; Chung, Chi Nghia et al.
2019In Composite Structures, 220, p. 64-80
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Keywords :
Multiscale; Stochastic; Composites; Bayesian Inference; Inverse Identification
Abstract :
[en] We present a stochastic approach combining Bayesian Inference (BI) with homogenization theories in order to identify, on the one hand, the parameters inherent to the model assumptions and, on the other hand, the composite material constituents behaviors, including their variability. In particular, we characterize the model parameters of a Mean-Field Homogenization (MFH) model and the elastic matrix behavior, including the inherent dispersion in its Young's modulus, of non-aligned Short Fibers Reinforced Polymer (SFRP) composites. The inference is achieved by considering as observations experimental tests conducted at the SFRP composite coupons level. The inferred model and material law parameters can in turn be used in Mean-Field Homogenization (MFH)-based multi-scale simulations and can predict the confidence range of the composite material responses.
Disciplines :
Materials science & engineering
Mechanical engineering
Aerospace & aeronautics engineering
Author, co-author :
Mahamedou, Mohamed
Zulueta Uriondo, Kepa
Chung, Chi Nghia
RAPPEL, Hussein ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
BEEX, Lars ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Adam, Laurent
Arriaga, Aitor
Major, Zoltan
Wu, Ling
Noels, Ludovic
External co-authors :
yes
Language :
English
Title :
Bayesian Identification of Mean-Field Homogenization model parameters and uncertain matrix behavior in non-aligned short fiber composites
Publication date :
15 July 2019
Journal title :
Composite Structures
ISSN :
0263-8223
eISSN :
1879-1085
Publisher :
Elsevier, Netherlands
Volume :
220
Pages :
64-80
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
FnR Project :
FNR11501927 - A Virtual Lab For Ni/Pu Hybrid Foams: Stochastic Micromechanical Identification And Efficient Numerical Simulations, 2016 (01/03/2018-28/02/2021) - Lars Beex
Name of the research project :
The research has been funded by the Walloon Region under the agreement no 1410246 - STOMMMAC (CT-INT2013-03-28) in the context of the M-ERA.NET Joint Call 2014.
Funders :
Service public de Wallonie : Direction générale opérationnelle de l'économie, de l'emploi et de la recherche - DG06
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since 04 April 2019

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