TY - JOUR
T1 - Probabilistic stability analysis of functionally graded graphene reinforced porous beams
AU - Gao, Kang
AU - Do, Duy Minh
AU - Li, Ruilong
AU - Kitipornchai, Sritawat
AU - Yang, Jie
N1 - Funding Information:
The work described in the present paper is fully funded by a research grant from the Australian Research Council under Discovery Project scheme ( DP160101978 ). The authors are grateful for the financial support.
Publisher Copyright:
© 2020 Elsevier Masson SAS
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/3
Y1 - 2020/3
N2 - This paper presents the first attempt to study the probabilistic stability characteristics of functionally graded (FG) graphene platelets (GPLs) reinforced beams by taking into account the multidimensional probability distributions, such as stochastic porosity and GPL distribution patterns as well as random material properties. For this purpose, a non-inclusive Chebyshev metamodel (CMM), which is implemented on deterministic analysis using discrete singular convolution (DSC) method with excellent computational efficiency and accuracy, is proposed and used to obtain both deterministic and probabilistic results including probability density functions (PDFs), cumulative density functions (CDFs), means and standard deviations of the critical buckling load. The present analysis is rigorously validated through direct comparisons against the results obtained by a direct quasi-Monte Carlo simulation (QMCS) method and those available in open literature. The influences of material properties, porosity distribution, GPL dispersion pattern and boundary condition on probabilistic buckling behaviour of the FG-GPL beam are comprehensively investigated. The global sensitivity analysis is also conducted. The results suggest that the critical buckling load of the FG-GPL beam is most sensitive to porosity distribution, followed by porosity coefficient and GPL weight fraction.
AB - This paper presents the first attempt to study the probabilistic stability characteristics of functionally graded (FG) graphene platelets (GPLs) reinforced beams by taking into account the multidimensional probability distributions, such as stochastic porosity and GPL distribution patterns as well as random material properties. For this purpose, a non-inclusive Chebyshev metamodel (CMM), which is implemented on deterministic analysis using discrete singular convolution (DSC) method with excellent computational efficiency and accuracy, is proposed and used to obtain both deterministic and probabilistic results including probability density functions (PDFs), cumulative density functions (CDFs), means and standard deviations of the critical buckling load. The present analysis is rigorously validated through direct comparisons against the results obtained by a direct quasi-Monte Carlo simulation (QMCS) method and those available in open literature. The influences of material properties, porosity distribution, GPL dispersion pattern and boundary condition on probabilistic buckling behaviour of the FG-GPL beam are comprehensively investigated. The global sensitivity analysis is also conducted. The results suggest that the critical buckling load of the FG-GPL beam is most sensitive to porosity distribution, followed by porosity coefficient and GPL weight fraction.
KW - Chebyshev metamodel
KW - Functionally graded porous structures
KW - Graphene platelet
KW - Sensitivity analysis
KW - Stochastic stability analysis
UR - http://www.scopus.com/inward/record.url?scp=85078247897&partnerID=8YFLogxK
U2 - 10.1016/j.ast.2020.105738
DO - 10.1016/j.ast.2020.105738
M3 - Journal article
AN - SCOPUS:85078247897
SN - 1270-9638
VL - 98
JO - Aerospace Science and Technology
JF - Aerospace Science and Technology
M1 - 105738
ER -