Author:
Chu Liu,Shi Jiajia,de Cursi Eduardo Souza
Abstract
AbstractThe uncertainty and fluctuations in graphene characteristic parameters are inevitable issues in both of experimental measurements and numerical investigations. In this paper, the correlations between characteristic parameters (Young’s modulus, Poisson’s ratio and thickness of graphene) and resonant frequencies are analyzed by the Monte Carlo based stochastic finite element model. Based on the Monte Carlo stochastic sampling procedure, the uncertainty in the characteristic parameters are properly propagated and quantified. The displacements and rotation modes of graphene under the resonant vibration computed by the finite element method are verified. Furthermore, the result robustness of stochastic samples is discussed based on the statistic records and probability density distributions. In addition, both the Pearson and Spearman correlation coefficients of the corresponding characteristic parameters are calculated and compared. The work in this paper provides a feasible and highly efficient method for the characteristic parameter correlation discussion by taking uncertainty into consideration.
Funder
Natural Science Foundation of Jiangsu Province
Natural Science Research of Jiangsu Higher Education Institutions of China
Large Instruments Open Foundation of Nantong University
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Reference80 articles.
1. Chu, L., Shi, J. & Braun, R. The equivalent Young’s modulus prediction for vacancy defected graphene under shear stress. Phys. E 110, 115–122 (2019).
2. Chu, L., Shi, J. & Souza de Cursi, E. Vibration analysis of vacancy defected graphene sheets by Monte Carlo based finite element method. Nanomaterials 8(7), 489 (2018).
3. Chu, L., Shi, J. & Ben, S. Buckling analysis of vacancy-defected graphene sheets by the stochastic finite element method. Materials 11(9), 1545 (2018).
4. Chu, L. et al. Monte Carlo-based finite element method for the study of randomly distributed vacancy defects in graphene sheets. J. Nanomater. 3037063, 1–11 (2018).
5. Shi, J., Chu, L. & Braun, R. A Kriging surrogate model for uncertainty analysis of graphene based on a finite element method. Int. J. Mol. Sci. 20(9), 2355 (2019).
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