Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Khader M. Hamdia
  • Mohammed A. Msekh
  • Mohammad Silani
  • Nam Vu-Bac
  • Xiaoying Zhuang
  • Trung Nguyen-Thoi
  • Timon Rabczuk

External Research Organisations

  • Bauhaus-Universität Weimar
  • University of Babylon
  • Isfahan University of Technology
  • Tongji University
  • Ton Duc Thang University
  • Korea University
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Details

Original languageEnglish
Pages (from-to)1177-1190
Number of pages14
JournalComposite Structures
Volume133
Early online date18 Aug 2015
Publication statusPublished - 1 Dec 2015
Externally publishedYes

Abstract

A sensitivity analysis (SA) has been conducted to examine the influence of uncertain input parameters on the fracture toughness of polymeric clay nanocomposites (PNCs). In order to predict the macroscopic properties of the composite, a phase-field approach has been employed considering six input parameters. For computationally efficiency, the SA is performed based on a surrogate model. Screening methods of the Standardized Regression Coefficients and the Regionalized Sensitivity Analysis are applied first. Then, quantitative methods, i.e. Sobol', EFAST, and PAWN are employed. Moreover, we have presented an improvement to the PAWN method that reduces the computational cost. The efficiency, robustness, and repeatability are compared and evaluated comprehensively of the five SA methods. The convergence of the sensitivity indices is achieved through the bootstrapping technique. The matrix Young's modulus is the most important input parameter affecting the macroscopic fracture toughness, whereas the volume fraction of the clay and the fracture energy of the matrix have a moderate importance. On the other hand, the aspect ratio, the radius of curvature, and the Young's modulus of the clay have negligible effects. Finally, fixing the uncertainties in the important input parameters reduces the coefficient of variation (COV) from 16.82% to 1.97%.

Keywords

    Fracture toughness, Phase-field modeling, Polymeric nanocomposites, Sensitivity analysis

ASJC Scopus subject areas

Cite this

Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling. / Hamdia, Khader M.; Msekh, Mohammed A.; Silani, Mohammad et al.
In: Composite Structures, Vol. 133, 01.12.2015, p. 1177-1190.

Research output: Contribution to journalArticleResearchpeer review

Hamdia, KM, Msekh, MA, Silani, M, Vu-Bac, N, Zhuang, X, Nguyen-Thoi, T & Rabczuk, T 2015, 'Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling', Composite Structures, vol. 133, pp. 1177-1190. https://doi.org/10.1016/j.compstruct.2015.08.051
Hamdia, K. M., Msekh, M. A., Silani, M., Vu-Bac, N., Zhuang, X., Nguyen-Thoi, T., & Rabczuk, T. (2015). Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling. Composite Structures, 133, 1177-1190. https://doi.org/10.1016/j.compstruct.2015.08.051
Hamdia KM, Msekh MA, Silani M, Vu-Bac N, Zhuang X, Nguyen-Thoi T et al. Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling. Composite Structures. 2015 Dec 1;133:1177-1190. Epub 2015 Aug 18. doi: 10.1016/j.compstruct.2015.08.051
Hamdia, Khader M. ; Msekh, Mohammed A. ; Silani, Mohammad et al. / Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling. In: Composite Structures. 2015 ; Vol. 133. pp. 1177-1190.
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abstract = "A sensitivity analysis (SA) has been conducted to examine the influence of uncertain input parameters on the fracture toughness of polymeric clay nanocomposites (PNCs). In order to predict the macroscopic properties of the composite, a phase-field approach has been employed considering six input parameters. For computationally efficiency, the SA is performed based on a surrogate model. Screening methods of the Standardized Regression Coefficients and the Regionalized Sensitivity Analysis are applied first. Then, quantitative methods, i.e. Sobol', EFAST, and PAWN are employed. Moreover, we have presented an improvement to the PAWN method that reduces the computational cost. The efficiency, robustness, and repeatability are compared and evaluated comprehensively of the five SA methods. The convergence of the sensitivity indices is achieved through the bootstrapping technique. The matrix Young's modulus is the most important input parameter affecting the macroscopic fracture toughness, whereas the volume fraction of the clay and the fracture energy of the matrix have a moderate importance. On the other hand, the aspect ratio, the radius of curvature, and the Young's modulus of the clay have negligible effects. Finally, fixing the uncertainties in the important input parameters reduces the coefficient of variation (COV) from 16.82% to 1.97%.",
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AU - Hamdia, Khader M.

AU - Msekh, Mohammed A.

AU - Silani, Mohammad

AU - Vu-Bac, Nam

AU - Zhuang, Xiaoying

AU - Nguyen-Thoi, Trung

AU - Rabczuk, Timon

N1 - Funding information: The authors gratefully acknowledge the support for this research provided by the IRSES-MULTIFRAC, the Deutsche Forschungsgemeinschaft (DFG), and the Alexander von Humboldt Foundation in the framework of the Sofja Kovalevskaja Award endowed by the Federal Ministry of Education and Research.

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N2 - A sensitivity analysis (SA) has been conducted to examine the influence of uncertain input parameters on the fracture toughness of polymeric clay nanocomposites (PNCs). In order to predict the macroscopic properties of the composite, a phase-field approach has been employed considering six input parameters. For computationally efficiency, the SA is performed based on a surrogate model. Screening methods of the Standardized Regression Coefficients and the Regionalized Sensitivity Analysis are applied first. Then, quantitative methods, i.e. Sobol', EFAST, and PAWN are employed. Moreover, we have presented an improvement to the PAWN method that reduces the computational cost. The efficiency, robustness, and repeatability are compared and evaluated comprehensively of the five SA methods. The convergence of the sensitivity indices is achieved through the bootstrapping technique. The matrix Young's modulus is the most important input parameter affecting the macroscopic fracture toughness, whereas the volume fraction of the clay and the fracture energy of the matrix have a moderate importance. On the other hand, the aspect ratio, the radius of curvature, and the Young's modulus of the clay have negligible effects. Finally, fixing the uncertainties in the important input parameters reduces the coefficient of variation (COV) from 16.82% to 1.97%.

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