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

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • N. Vu-Bac
  • Mohammad Silani
  • T. Lahmer
  • Xiaoying Zhuang
  • Timon Rabczuk

Externe Organisationen

  • Bauhaus-Universität Weimar
  • Isfahan University of Technology
  • Tongji University
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)520-535
Seitenumfang16
FachzeitschriftComputational Materials Science
Jahrgang96
AusgabenummerPB
PublikationsstatusVeröffentlicht - 10 Juli 2014
Extern publiziertJa

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.

Zitieren

A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. / Vu-Bac, N.; Silani, Mohammad; Lahmer, T. et al.
in: Computational Materials Science, Jahrgang 96, Nr. PB, 10.07.2014, S. 520-535.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Vu-Bac N, Silani M, Lahmer T, Zhuang X, Rabczuk T. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science. 2014 Jul 10;96(PB):520-535. doi: 10.1016/j.commatsci.2014.04.066
Vu-Bac, N. ; Silani, Mohammad ; Lahmer, T. et al. / A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. in: Computational Materials Science. 2014 ; Jahrgang 96, Nr. PB. S. 520-535.
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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.",
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note = "Funding information: We gratefully acknowledge the support by the Deutscher Akademischer Austausch Dienst (DAAD), IRSES-MULTIFRAC and the Deutsche Forschungsgemeinschaft (DFG). Xiaoying Zhuang acknowledges the support by National Basic Research Program of China (973 Program: 2011CB013800)",
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TY - JOUR

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

AU - Vu-Bac, N.

AU - Silani, Mohammad

AU - Lahmer, T.

AU - Zhuang, Xiaoying

AU - Rabczuk, Timon

N1 - Funding information: We gratefully acknowledge the support by the Deutscher Akademischer Austausch Dienst (DAAD), IRSES-MULTIFRAC and the Deutsche Forschungsgemeinschaft (DFG). Xiaoying Zhuang acknowledges the support by National Basic Research Program of China (973 Program: 2011CB013800)

PY - 2014/7/10

Y1 - 2014/7/10

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.

KW - Computational modeling

KW - Mechanical properties

KW - Micromechanical modeling

KW - Polymer clay nanocompositest (PCNs)

KW - Stochastic predictions

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U2 - 10.1016/j.commatsci.2014.04.066

DO - 10.1016/j.commatsci.2014.04.066

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EP - 535

JO - Computational Materials Science

JF - Computational Materials Science

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