Details
| Original language | English |
|---|---|
| Pages (from-to) | 520-535 |
| Number of pages | 16 |
| Journal | Computational Materials Science |
| Volume | 96 |
| Issue number | PB |
| Publication status | Published - 10 Jul 2014 |
| Externally published | Yes |
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
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Chemistry(all)
- General Chemistry
- Materials Science(all)
- General Materials Science
- Engineering(all)
- Mechanics of Materials
- Physics and Astronomy(all)
- General Physics and Astronomy
- Mathematics(all)
- Computational Mathematics
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In: Computational Materials Science, Vol. 96, No. PB, 10.07.2014, p. 520-535.
Research output: Contribution to journal › Article › Research › peer review
}
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
UR - http://www.scopus.com/inward/record.url?scp=84908668303&partnerID=8YFLogxK
U2 - 10.1016/j.commatsci.2014.04.066
DO - 10.1016/j.commatsci.2014.04.066
M3 - Article
AN - SCOPUS:84908668303
VL - 96
SP - 520
EP - 535
JO - Computational Materials Science
JF - Computational Materials Science
SN - 0927-0256
IS - PB
ER -