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

N. Vu-Bac; M. Silani; T. Lahmer; X. Zhuang; T. Rabczuk

doi:10.1016/j.commatsci.2014.04.066

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

N. Vu-Bac, M. Silani, T. Lahmer, X. Zhuang, T. Rabczuk

Research output: Contribution to journal › Article › peer-review

156 Citations (Scopus)

Abstract

We propose a stochastic framework based on sensitivity analysist (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.

Original language	English
Pages (from-to)	520-535
Number of pages	16
Journal	Computational Materials Science
Volume	96
Issue number	PB
DOIs	https://doi.org/10.1016/j.commatsci.2014.04.066
Publication status	Published - 2015 Jan

Bibliographical note

Publisher Copyright:
© 2014 Elsevier B.V.

Keywords

Computational modeling
Mechanical properties
Micromechanical modeling
Polymer clay nanocompositest (PCNs)
Stochastic predictions

ASJC Scopus subject areas

General Computer Science
General Chemistry
General Materials Science
Mechanics of Materials
General Physics and Astronomy
Computational Mathematics

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10.1016/j.commatsci.2014.04.066

Cite this

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title = "A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites",

abstract = "We propose a stochastic framework based on sensitivity analysist (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.",

keywords = "Computational modeling, Mechanical properties, Micromechanical modeling, Polymer clay nanocompositest (PCNs), Stochastic predictions",

author = "N. Vu-Bac and M. Silani and T. Lahmer and X. Zhuang and T. Rabczuk",

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doi = "10.1016/j.commatsci.2014.04.066",

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T1 - A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites

AU - Vu-Bac, N.

AU - Silani, M.

AU - Lahmer, T.

AU - Zhuang, X.

AU - Rabczuk, T.

PY - 2015/1

Y1 - 2015/1

N2 - We propose a stochastic framework based on sensitivity analysist (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.

AB - We propose a stochastic framework based on sensitivity analysist (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.

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KW - Mechanical properties

KW - Micromechanical modeling

KW - Polymer clay nanocompositest (PCNs)

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DO - 10.1016/j.commatsci.2014.04.066

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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