Stochastic multiscale modeling of heat conductivity of Polymeric clay nanocomposites

verfasst von: Bokai Liu, Nam Vu-Bac, Xiaoying Zhuang, Timon Rabczuk
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).
Organisationseinheit(en): Institut für Kontinuumsmechanik
Externe Organisation(en): Bauhaus-Universität Weimar
Ton Duc Thang University
Typ: Artikel
Journal: Mechanics of Materials
Band: 142
ISSN: 0167-6636
Publikationsdatum: 03.2020
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Werkstoffwissenschaften (insg.), Instrumentierung, Werkstoffmechanik
Elektronische Version(en): https://doi.org/10.1016/j.mechmat.2019.103280 (Zugang: Geschlossen)
: Details im Forschungsportal „Research@Leibniz University“

BibTeX

@article{5fc03cbabae8438dbab19296e4aebd59,
title = "Stochastic multiscale modeling of heat conductivity of Polymeric clay nanocomposites",
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 {\textquoteright}macroscopic{\textquoteright} 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",
author = "Bokai Liu and Nam Vu-Bac and Xiaoying Zhuang and Timon Rabczuk",
note = "Funding information: We gratefully acknowledge the support of the China Scholarship Council (CSC) .",
year = "2020",
month = mar,
doi = "10.1016/j.mechmat.2019.103280",
language = "English",
volume = "142",
journal = "Mechanics of Materials",
issn = "0167-6636",
publisher = "Elsevier",
}