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

N1 - Funding information: The authors gratefully acknowledge the support for this research provided by the IRSES-MULTIFRAC and the Alexander von Humboldt Foundation in the framework of the Sofja Kovalevskaja Award endowed by the Federal Ministry of Education and Research, Germany. Dr. Xiaoying Zhuang thanks the National High-end Foreign Experts by State Administration of Foreign Experts Affairs, PRC of Tongji University.

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

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 -