Fracture toughness of polymeric particle nanocomposites: Evaluation of models performance using Bayesian method

Khader M. Hamdia, Xiaoying Zhuang, Pengfei He, Timon Rabczuk

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

This study presents a methodology to evaluate the performance of different models used in predicting the fracture toughness of polymeric particles nanocomposites. Three analytical models are considered: the model of Huang and Kinloch, the model of Williams, and the model of Quaresimin et al. The purpose behind this study is not to recommend which of the three models to be adopted, but to evaluate their performance with respect to experimental data. The Bayesian method is exploited for this purpose based on different reference measurements gained from the literature. The models' performance is compared and evaluated comprehensively accounting for the parameter and model uncertainties. Based on the approximated optimal parameter sets, the coefficients of variation of the model predictions to the measurements are compared for the three models. Finally, the model selection probability is obtained with respect to the different reference data.

Original languageEnglish
Pages (from-to)122-129
Number of pages8
JournalComposites Science and Technology
Volume126
DOIs
Publication statusPublished - 2016 Apr 1

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Fracture toughness
Nanocomposites
Analytical models

Keywords

  • Fracture toughness
  • Modelling
  • Nano particles

ASJC Scopus subject areas

  • Engineering(all)
  • Ceramics and Composites

Cite this

Fracture toughness of polymeric particle nanocomposites : Evaluation of models performance using Bayesian method. / Hamdia, Khader M.; Zhuang, Xiaoying; He, Pengfei; Rabczuk, Timon.

In: Composites Science and Technology, Vol. 126, 01.04.2016, p. 122-129.

Research output: Contribution to journalArticle

Hamdia, Khader M. ; Zhuang, Xiaoying ; He, Pengfei ; Rabczuk, Timon. / Fracture toughness of polymeric particle nanocomposites : Evaluation of models performance using Bayesian method. In: Composites Science and Technology. 2016 ; Vol. 126. pp. 122-129.
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