Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites
Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites
This article presents a non-probabilistic fuzzy based multi-scale uncertainty propagation framework for studying the dynamic and stability characteristics of composite laminates with spatially varying system properties. Most of the studies concerning the uncertainty quantification of composites rely on probabilistic analyses, where the prerequisite is to have the statistical distribution of stochastic input parameters. In many engineering problems, these statistical distributions remain unavailable due to the restriction of performing large number of experiments. In such situations, a fuzzy-based approach could be appropriate to characterize the effect of uncertainty. A novel concept of fuzzy representative volume element (FRVE) is developed here for accounting the spatially varying non-probabilistic source-uncertainties at the input level. Such approach of uncertainty modelling is physically more relevant than the prevalent way of modelling non-probabilistic uncertainty without considering the ply-level spatial variability. An efficient radial basis function based stochastic algorithm coupled with the fuzzy finite element model of composites is developed for the multi-scale uncertainty propagation involving multi-synchronous triggering parameters. The concept of a fuzzy factor of safety (FFoS) is discussed in this paper for evaluation of safety factor in the non-probabilistic regime. The results reveal that the present physically relevant approach of modelling fuzzy uncertainty considering ply-level spatial variability obtains significantly lower fuzzy bounds of the global responses compared to the conventional approach of non-probabilistic modelling neglecting the spatially varying attributes.
940-967
Naskar, Susmita
5f787953-b062-4774-a28b-473bd19254b1
Mukhopadhyay, Tanmoy
2ae18ab0-7477-40ac-ae22-76face7be475
Sriramula, Srinivas
1f89db01-ff5c-4033-a39c-4df483fcba8c
1 February 2019
Naskar, Susmita
5f787953-b062-4774-a28b-473bd19254b1
Mukhopadhyay, Tanmoy
2ae18ab0-7477-40ac-ae22-76face7be475
Sriramula, Srinivas
1f89db01-ff5c-4033-a39c-4df483fcba8c
Naskar, Susmita, Mukhopadhyay, Tanmoy and Sriramula, Srinivas
(2019)
Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites.
Composite Structures, .
(doi:10.1016/j.compstruct.2018.09.090).
Abstract
This article presents a non-probabilistic fuzzy based multi-scale uncertainty propagation framework for studying the dynamic and stability characteristics of composite laminates with spatially varying system properties. Most of the studies concerning the uncertainty quantification of composites rely on probabilistic analyses, where the prerequisite is to have the statistical distribution of stochastic input parameters. In many engineering problems, these statistical distributions remain unavailable due to the restriction of performing large number of experiments. In such situations, a fuzzy-based approach could be appropriate to characterize the effect of uncertainty. A novel concept of fuzzy representative volume element (FRVE) is developed here for accounting the spatially varying non-probabilistic source-uncertainties at the input level. Such approach of uncertainty modelling is physically more relevant than the prevalent way of modelling non-probabilistic uncertainty without considering the ply-level spatial variability. An efficient radial basis function based stochastic algorithm coupled with the fuzzy finite element model of composites is developed for the multi-scale uncertainty propagation involving multi-synchronous triggering parameters. The concept of a fuzzy factor of safety (FFoS) is discussed in this paper for evaluation of safety factor in the non-probabilistic regime. The results reveal that the present physically relevant approach of modelling fuzzy uncertainty considering ply-level spatial variability obtains significantly lower fuzzy bounds of the global responses compared to the conventional approach of non-probabilistic modelling neglecting the spatially varying attributes.
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Accepted/In Press date: 24 September 2018
e-pub ahead of print date: 4 October 2018
Published date: 1 February 2019
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Local EPrints ID: 452058
URI: http://eprints.soton.ac.uk/id/eprint/452058
ISSN: 0263-8223
PURE UUID: 1421a0ec-7d4c-492e-8b7b-8d90355c02c6
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Date deposited: 10 Nov 2021 17:33
Last modified: 17 Mar 2024 04:18
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Author:
Tanmoy Mukhopadhyay
Author:
Srinivas Sriramula
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