Articlegold2019

A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method

18 citations0/0 files storedInternational Journal of Molecular Sciences

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Probabilistic and Robust Engineering Design Graphene research and applications Composite Structure Analysis and Optimization

Abstract

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.

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References

2003Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems 2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation 1990The origins of kriging 2007Kriging metamodeling in simulation: A review 2005Use of Kriging Models to Approximate Deterministic Computer Models 2012DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization 2017Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data 2015Application of Latin Hypercube Sampling Based Kriging Surrogate Models in Reliability Assessment

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2020A sequential reliability assessment and optimization strategy for multidisciplinary problems with active learning kriging model 2021The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model 2022Real-world engineering problems: Two surrogate methods for robust vibration control of moving mass-beam coupling systems with epistemic uncertainty

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