Monte Carlo-Based Finite Element Method for the Study of Randomly Distributed Vacancy Defects in Graphene Sheets

Chu, Liu; Shi, Jiajia; Cursi, Eduardo Souza de; Xu, Xunqian; Yazhou, QIN; Xiang, Hongliang

doi:10.1155/2018/3037063

Cited by 6 publications

(6 citation statements)

References 44 publications

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“…4 , the displacement vector sum and roation vector sum in different vibration modes have satisfied accuracy and convergence in FEM computaion. Both the resonant frequencies and the vibration modes reach a good aggreement with the previous reported references 2 , 4 . Therefore, the deterministic FEM is verified as the basic model for the following MC-SFEM.…”

Section: Model Validationsupporting

confidence: 90%

“…The Monte Carlo based stochastic finite element model (MC-SFEM) combines the Monte Carlo stochastic sampling method with the FEM to expand applications in uncertainty propagation 3 . For example, the MC-SFEM is an effective method to discuss and analyze the mechanical impacts of random distributed defects in graphene by mapping the stochastic series into the periodic lattice 4 . In addition, the uncertainty propagation of graphene material and geometrical parameters can be implemented by the MC-SFEM 5 .…”

Section: Introductionmentioning

confidence: 99%

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The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model

Chu

Shi

Cursi

2021

Sci Rep

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The 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.

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Section: Model Validationsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model

Chu

Shi

Cursi

2021

Sci Rep

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“…MCS is a classical stochastic sampling method with a solid mathematical foundation [ 32 ]. Each atom in the honeycomb lattice of pristine graphene is marked with different serial numbers in the finite element model.…”

Section: Methodsmentioning

confidence: 99%

“…Third, the general concerns about an independent parameter ignore the correlation and relationships between parameters corresponding to mechanical and physical properties [ 31 ]. For example, resonant frequencies are related with both mass and stiffness of porous graphene [ 32 ]. Therefore, this study is aimed at analyzing the effects of random porosities in the resonant frequencies of graphene.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Random Porosities in Resonant Frequencies of Graphene Based on the Monte Carlo Stochastic Finite Element Model

Chu

Shi

et al. 2021

IJMS

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With the distinguished properties in electronics, thermal conductivity, optical transparence and mechanics, graphene has a powerful potential in nanosensors, nano-resonators, supercapacitors, batteries, etc. The resonant frequency of graphene is an important factor in its application and working environment. However, the random dispersed porosities in graphene evidently change the lattice structure and destroy the integrity and geometrical periodicity. This paper focuses on the effects of random porosities in resonant frequencies of graphene. Monte Carlo simulation is applied to propagate the porosities in the finite element model of pristine graphene. The statistical results and probability density distribution of porous graphene with atomic vacancy defects are computed based on the Monte Carlo finite element model. The results of porous graphene with atomic vacancy defects are compared and discussed with the results of graphene with bond vacancy defects. The enhancement effects of atomic vacancy defects are confirmed in porous graphene. The influences of atomic vacancy defects on displacement and rotation vector sums of porous graphene are more concentrated in local places.

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“…Untuk memperkirakan anggaran tahunan untuk pemeliharaan pabrik [13]. Menghasilkan variabel acak dengan ekspektasi matematis [14]. Distribusi Kumulatif Januari 12 0,10 0,10 12 0,11 0,11 11 0,10 0,10 Februari 10 0,08 0,18 9 0,08 0,19 8 0,07 0,18 Maret 9 0,07 0,25 8 0,07 0,26 8 0,07 0,25 April 10 0,08 0,33 9 0,08 0,34 8 0,07 0,32 Mei 11 0,…”

Section: Pendahuluanunclassified

Simulasi Algoritma Monte Carlo Dalam Memprediksi Tingkat Hafalan Al-Qur’an Santri

Nurcahyo

Yunus

2020

JSisfotek

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Islamic boarding schools are known to be able to give birth to the Qur'an's tahfidz. With the memorization program, the halaqah about the basic text of the religion makes the Islamic boarding schools increasingly known to the community as an educational institution and a strong milestone of religious knowledge that can bring blessings. The purpose of this study was to predict the level of rote learning of the students at the Tahfiz Lan Tabur Pagaralam Islamic Boarding School. Thus, it is easier for Islamic boarding schools to take strategies to improve the quality of students themselves. The data processed in this study are data sourced from the Natural Boarding School of Tahfiz Al-Qur’an Lan Tabur Pagaralam. Based on the number of students who memorized the Al-Qur'an or Tahfidz in the 2017 to 2019 class which is processed by Monte Carlo algorithm simulation. The processing step is to bring up a random number from the sampling data that has been taken. The results of testing on this method found that the system used to predict the level of memorization of the Al-Qur'an with an average accuracy of 84% in 2018 and an average accuracy of 88% in 2019. With a high degree of accuracy, simulations performed using the Monte Carlo algorithm can predict the number of students memorized by the Qur'an, making it easier for Islamic boarding schools to obtain information about the likelihood of future events at the level of memorization of students at Islamic boarding schools Alam Tahfiz Al-Qur’an Lan Tabur Pagaralam.

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Monte Carlo-Based Finite Element Method for the Study of Randomly Distributed Vacancy Defects in Graphene Sheets

Cited by 6 publications

References 44 publications

The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model

The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model

The Effects of Random Porosities in Resonant Frequencies of Graphene Based on the Monte Carlo Stochastic Finite Element Model

Simulasi Algoritma Monte Carlo Dalam Memprediksi Tingkat Hafalan Al-Qur’an Santri

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