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 language | English |
|---|---|
| Pages (from-to) | 122-129 |
| Number of pages | 8 |
| Journal | Composites Science and Technology |
| Volume | 126 |
| DOIs | |
| Publication status | Published - 2016 Apr 1 |
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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 journal › Article
}
TY - JOUR
T1 - Fracture toughness of polymeric particle nanocomposites
T2 - Evaluation of models performance using Bayesian method
AU - Hamdia, Khader M.
AU - Zhuang, Xiaoying
AU - He, Pengfei
AU - Rabczuk, Timon
PY - 2016/4/1
Y1 - 2016/4/1
N2 - 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.
AB - 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.
KW - Fracture toughness
KW - Modelling
KW - Nano particles
UR - http://www.scopus.com/inward/record.url?scp=84959233205&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959233205&partnerID=8YFLogxK
U2 - 10.1016/j.compscitech.2016.02.012
DO - 10.1016/j.compscitech.2016.02.012
M3 - Article
AN - SCOPUS:84959233205
VL - 126
SP - 122
EP - 129
JO - Composites Science and Technology
JF - Composites Science and Technology
SN - 0266-3538
ER -