Details
| Original language | English |
|---|---|
| Article number | 103280 |
| Journal | Mechanics of Materials |
| Volume | 142 |
| Early online date | 14 Dec 2019 |
| Publication status | Published - Mar 2020 |
Abstract
We propose a stochastic multi-scale method to quantify the most significant input parameters influencing the heat conductivity of polymeric nano-composites (PNCs) with clay reinforcement. Therefore, a surrogate based global sensitivity analysis is coupled with a hierarchical multi-scale method employing computational homogenization. The effect of the conductivity of the fibers and the matrix, the Kapitza resistance, volume fraction and aspect ratio on the ’macroscopic’ conductivity of the composite is systematically studied. We show that all selected surrogate models yield consistently the conclusions that the most influential input parameters are the aspect ratio followed by the volume fraction. The Kapitza Resistance has no significant effect on the thermal conductivity of the PNCs. The most accurate surrogate model in terms of the R2 value is the moving least square (MLS).
Keywords
- Heat conductivity, Multi-scale modeling, Polymeric nano-composites(PNCs), Stochastic modeling, Uncertainty quantification
ASJC Scopus subject areas
- Materials Science(all)
- General Materials Science
- Physics and Astronomy(all)
- Instrumentation
- Engineering(all)
- Mechanics of Materials
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In: Mechanics of Materials, Vol. 142, 103280, 03.2020.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Stochastic multiscale modeling of heat conductivity of Polymeric clay nanocomposites
AU - Liu, Bokai
AU - Vu-Bac, Nam
AU - Zhuang, Xiaoying
AU - Rabczuk, Timon
N1 - Funding information: We gratefully acknowledge the support of the China Scholarship Council (CSC) .
PY - 2020/3
Y1 - 2020/3
N2 - We propose a stochastic multi-scale method to quantify the most significant input parameters influencing the heat conductivity of polymeric nano-composites (PNCs) with clay reinforcement. Therefore, a surrogate based global sensitivity analysis is coupled with a hierarchical multi-scale method employing computational homogenization. The effect of the conductivity of the fibers and the matrix, the Kapitza resistance, volume fraction and aspect ratio on the ’macroscopic’ conductivity of the composite is systematically studied. We show that all selected surrogate models yield consistently the conclusions that the most influential input parameters are the aspect ratio followed by the volume fraction. The Kapitza Resistance has no significant effect on the thermal conductivity of the PNCs. The most accurate surrogate model in terms of the R2 value is the moving least square (MLS).
AB - We propose a stochastic multi-scale method to quantify the most significant input parameters influencing the heat conductivity of polymeric nano-composites (PNCs) with clay reinforcement. Therefore, a surrogate based global sensitivity analysis is coupled with a hierarchical multi-scale method employing computational homogenization. The effect of the conductivity of the fibers and the matrix, the Kapitza resistance, volume fraction and aspect ratio on the ’macroscopic’ conductivity of the composite is systematically studied. We show that all selected surrogate models yield consistently the conclusions that the most influential input parameters are the aspect ratio followed by the volume fraction. The Kapitza Resistance has no significant effect on the thermal conductivity of the PNCs. The most accurate surrogate model in terms of the R2 value is the moving least square (MLS).
KW - Heat conductivity
KW - Multi-scale modeling
KW - Polymeric nano-composites(PNCs)
KW - Stochastic modeling
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85076682008&partnerID=8YFLogxK
U2 - 10.1016/j.mechmat.2019.103280
DO - 10.1016/j.mechmat.2019.103280
M3 - Article
AN - SCOPUS:85076682008
VL - 142
JO - Mechanics of Materials
JF - Mechanics of Materials
SN - 0167-6636
M1 - 103280
ER -