A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites

•A stochastic modeling approach for predicting the Young’s modulus of PCNs.•Global SA methods to quantify the influences of the input parameters on the model response.•A bootstrap technique employed to assess robustness of the presented SA methods. We propose a stochastic framework based on sensitiv...

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Bibliographic Details
Published in:Computational materials science 2015-01, Vol.96, p.520-535
Main Authors: Vu-Bac, N., Silani, M., Lahmer, T., Zhuang, X., Rabczuk, T.
Format: Article
Language:English
Subjects:
Clay (material)
Computational modeling
Mathematical analysis
Mechanical properties
Micromechanical modeling
Modulus of elasticity
Nanocomposites
PCN
Polymer clay nanocomposites (PCNs)
Stiffness
Stochastic predictions
Stochasticity
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Summary:•A stochastic modeling approach for predicting the Young’s modulus of PCNs.•Global SA methods to quantify the influences of the input parameters on the model response.•A bootstrap technique employed to assess robustness of the presented SA methods. We propose a stochastic framework based on sensitivity analysis (SA) methods to quantify the key-input parameters influencing the Young’s modulus of polymer (epoxy) clay nanocomposites (PCNs). The input parameters include the clay volume fraction, clay aspect ratio, clay curvature, clay stiffness and epoxy stiffness. All stochastic methods predict that the key parameters for the Young’s modulus are the epoxy stiffness followed by the clay volume fraction. On the other hand, the clay aspect ratio, clay curvature and the clay stiffness have an insignificant effect on the Young’s modulus of PCNs. Besides the results on the sensitivity of the input parameters, this work includes a comparative study of a series of stochastic methods to predict mechanical properties of PCNs with respect to their performance.
ISSN:0927-0256
1879-0801
DOI:10.1016/j.commatsci.2014.04.066