The University of Southampton
University of Southampton Institutional Repository

Probing the multi-physical probabilistic dynamics of a novel functional class of hybrid composite shells

Probing the multi-physical probabilistic dynamics of a novel functional class of hybrid composite shells
Probing the multi-physical probabilistic dynamics of a novel functional class of hybrid composite shells

Functionally graded materials, sandwich structures and composite laminates have been widely investigated individually in last few decades for their respective set of advantages over conventional monolithic structures. However, the tremendous recent advances in manufacturing processes have opened up new frontiers of research specifically to form hybrid structures for multifunctional applications, where the advantages of each of the constituting components could potentially be exploited in a single structure. Such complex hybrid structures are often susceptible to manufacturing uncertainties and different forms of variability. To characterize the stochastic dynamic behaviour of these hybrid structural forms in a comprehensive, practically-relevant, yet efficient framework, here we present a multi-physical probabilistic vibration analysis based on Gaussian Process Regression (GPR) assisted finite element (FE) approach coupled with Monte Carlo Simulation. Integration of the GPR based machine learning model with the physics based simulation model leads to a significant level of computational efficiency in the quantification of system uncertainty. In the stochastic approach for various hybrid structural configurations, the compound effects of source-uncertainties are considered in order to assess the effect of different critical multi-physical parameters systematically involving material, geometric and physical aspects. The numerical results categorically indicate that the source-uncertainty of hybrid shells with different geometries has a significant effect on dynamic behaviour of the structure, which makes it imperative to take into account such probabilistic deviations to ensure adequate operational safety and serviceability.

Composite shell structures, Free vibration analysis, Gaussian process regression, Hybrid functional shells, Monte Carlo simulation, Stochastic natural frequency
0263-8223
Vaishali,
f129101a-6555-4e0e-9912-65b2a7145586
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Kumar, R. R.
e63bf00e-e5fa-4979-9840-8d79cd46e87a
Dey, S.
4f596c80-1feb-4532-9b62-d8d0e4658027
Vaishali,
f129101a-6555-4e0e-9912-65b2a7145586
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Kumar, R. R.
e63bf00e-e5fa-4979-9840-8d79cd46e87a
Dey, S.
4f596c80-1feb-4532-9b62-d8d0e4658027

Vaishali, , Mukhopadhyay, T., Kumar, R. R. and Dey, S. (2021) Probing the multi-physical probabilistic dynamics of a novel functional class of hybrid composite shells. Composite Structures, 262, [113294]. (doi:10.1016/j.compstruct.2020.113294).

Record type: Article

Abstract

Functionally graded materials, sandwich structures and composite laminates have been widely investigated individually in last few decades for their respective set of advantages over conventional monolithic structures. However, the tremendous recent advances in manufacturing processes have opened up new frontiers of research specifically to form hybrid structures for multifunctional applications, where the advantages of each of the constituting components could potentially be exploited in a single structure. Such complex hybrid structures are often susceptible to manufacturing uncertainties and different forms of variability. To characterize the stochastic dynamic behaviour of these hybrid structural forms in a comprehensive, practically-relevant, yet efficient framework, here we present a multi-physical probabilistic vibration analysis based on Gaussian Process Regression (GPR) assisted finite element (FE) approach coupled with Monte Carlo Simulation. Integration of the GPR based machine learning model with the physics based simulation model leads to a significant level of computational efficiency in the quantification of system uncertainty. In the stochastic approach for various hybrid structural configurations, the compound effects of source-uncertainties are considered in order to assess the effect of different critical multi-physical parameters systematically involving material, geometric and physical aspects. The numerical results categorically indicate that the source-uncertainty of hybrid shells with different geometries has a significant effect on dynamic behaviour of the structure, which makes it imperative to take into account such probabilistic deviations to ensure adequate operational safety and serviceability.

This record has no associated files available for download.

More information

Published date: 15 April 2021
Additional Information: Funding Information: The first author acknowledges the Ministry of Human Resource and Development (MHRD), Government of India, for supporting this research work. Publisher Copyright: © 2020 Elsevier Ltd
Keywords: Composite shell structures, Free vibration analysis, Gaussian process regression, Hybrid functional shells, Monte Carlo simulation, Stochastic natural frequency

Identifiers

Local EPrints ID: 483578
URI: http://eprints.soton.ac.uk/id/eprint/483578
ISSN: 0263-8223
PURE UUID: 0e38c073-7268-47f5-be6e-73e41e79ebd0
ORCID for T. Mukhopadhyay: ORCID iD orcid.org/0000-0002-0778-6515

Catalogue record

Date deposited: 01 Nov 2023 18:03
Last modified: 18 Mar 2024 04:10

Export record

Altmetrics

Contributors

Author: Vaishali
Author: T. Mukhopadhyay ORCID iD
Author: R. R. Kumar
Author: S. Dey

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×