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 journalArticlepeer-review

143 Citations (Scopus)


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 languageEnglish
Pages (from-to)520-535
Number of pages16
JournalComputational Materials Science
Issue numberPB
Publication statusPublished - 2015 Jan
Externally publishedYes

Bibliographical 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)


  • 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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