A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method
Due to the inevitable presence of random defects, unpredictable grain boundaries in macroscopic samples, stress concentration at clamping points, and unknown load distribution in the investigation of graphene sheets, uncertainties are crucial and challenging issues that require more exploration. The...
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doaj-f0541378ae8948fab24f5e73757416662020-11-25T02:07:59ZengMDPI AGInternational Journal of Molecular Sciences1422-00672019-05-01209235510.3390/ijms20092355ijms20092355A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element MethodJiajia Shi0Liu Chu1Robin Braun2School of Transportation and Civil Engineering, Nantong University, Nantong 226019, ChinaSchool of Transportation and Civil Engineering, Nantong University, Nantong 226019, ChinaFaculty of Engineering and Information Technology, University of Technology, Sydney, Ultimo, NSW 2007, AustraliaDue to the inevitable presence of random defects, unpredictable grain boundaries in macroscopic samples, stress concentration at clamping points, and unknown load distribution in the investigation of graphene sheets, uncertainties are crucial and challenging issues that require more exploration. The application of the Kriging surrogate model in vibration analysis of graphene sheets is proposed in this study. The Latin hypercube sampling method effectively propagates the uncertainties in geometrical and material properties of the finite element model. The accuracy and convergence of the Kriging surrogate model are confirmed by a comparison with the reported references. The uncertainty analysis for both Zigzag and Armchair graphene sheets are compared and discussed.https://www.mdpi.com/1422-0067/20/9/2355Kriging surrogate modelgraphene sheetsLatin hypercube samplingfinite element method |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiajia Shi Liu Chu Robin Braun |
spellingShingle |
Jiajia Shi Liu Chu Robin Braun A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method International Journal of Molecular Sciences Kriging surrogate model graphene sheets Latin hypercube sampling finite element method |
author_facet |
Jiajia Shi Liu Chu Robin Braun |
author_sort |
Jiajia Shi |
title |
A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method |
title_short |
A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method |
title_full |
A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method |
title_fullStr |
A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method |
title_full_unstemmed |
A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method |
title_sort |
kriging surrogate model for uncertainty analysis of graphene based on a finite element method |
publisher |
MDPI AG |
series |
International Journal of Molecular Sciences |
issn |
1422-0067 |
publishDate |
2019-05-01 |
description |
Due to the inevitable presence of random defects, unpredictable grain boundaries in macroscopic samples, stress concentration at clamping points, and unknown load distribution in the investigation of graphene sheets, uncertainties are crucial and challenging issues that require more exploration. The application of the Kriging surrogate model in vibration analysis of graphene sheets is proposed in this study. The Latin hypercube sampling method effectively propagates the uncertainties in geometrical and material properties of the finite element model. The accuracy and convergence of the Kriging surrogate model are confirmed by a comparison with the reported references. The uncertainty analysis for both Zigzag and Armchair graphene sheets are compared and discussed. |
topic |
Kriging surrogate model graphene sheets Latin hypercube sampling finite element method |
url |
https://www.mdpi.com/1422-0067/20/9/2355 |
work_keys_str_mv |
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1724928293620154368 |