Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • N. Vu-Bac
  • Roham Rafiee
  • Xiaoying Zhuang
  • T. Lahmer
  • Timon Rabczuk

Externe Organisationen

  • Bauhaus-Universität Weimar
  • University of Tehran
  • Tongji University
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)446-464
Seitenumfang19
FachzeitschriftComposites Part B: Engineering
Jahrgang68
PublikationsstatusVeröffentlicht - 22 Sept. 2015
Extern publiziertJa

Abstract

We propose a stochastic multiscale method to quantify the correlated key-input parameters influencing the mechanical properties of polymer nanocomposites (PNCs). The variations of parameters at nano-, micro-, meso- and macro-scales are connected by a hierarchical multiscale approach. The first-order and total-effect sensitivity indices are determined first. The input parameters include the single-walled carbon nanotube (SWNT) length, the SWNT waviness, the agglomeration and volume fraction of SWNTs. Stochastic methods consistently predict that the key parameters for the Young's modulus of the composite are the volume fraction followed by the averaged longitudinal modulus of equivalent fiber (EF), the SWNT length, and the averaged transverse modulus of the EF, respectively. The averaged longitudinal modulus of the EF is estimated to be the most important parameter with respect to the Poisson's ratio followed by the volume fraction, the SWNT length, and the averaged transverse modulus of the EF, respectively. On the other hand, the agglomeration parameters have insignificant effect on both Young's modulus and Poisson's ratio compared to other parameters. The sensitivity analysis (SA) also reveals the correlation between the input parameters and its effect on the mechanical properties.

ASJC Scopus Sachgebiete

Zitieren

Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. / Vu-Bac, N.; Rafiee, Roham; Zhuang, Xiaoying et al.
in: Composites Part B: Engineering, Jahrgang 68, 22.09.2015, S. 446-464.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Vu-Bac N, Rafiee R, Zhuang X, Lahmer T, Rabczuk T. Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. Composites Part B: Engineering. 2015 Sep 22;68:446-464. doi: 10.1016/j.compositesb.2014.09.008
Vu-Bac, N. ; Rafiee, Roham ; Zhuang, Xiaoying et al. / Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. in: Composites Part B: Engineering. 2015 ; Jahrgang 68. S. 446-464.
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abstract = "We propose a stochastic multiscale method to quantify the correlated key-input parameters influencing the mechanical properties of polymer nanocomposites (PNCs). The variations of parameters at nano-, micro-, meso- and macro-scales are connected by a hierarchical multiscale approach. The first-order and total-effect sensitivity indices are determined first. The input parameters include the single-walled carbon nanotube (SWNT) length, the SWNT waviness, the agglomeration and volume fraction of SWNTs. Stochastic methods consistently predict that the key parameters for the Young's modulus of the composite are the volume fraction followed by the averaged longitudinal modulus of equivalent fiber (EF), the SWNT length, and the averaged transverse modulus of the EF, respectively. The averaged longitudinal modulus of the EF is estimated to be the most important parameter with respect to the Poisson's ratio followed by the volume fraction, the SWNT length, and the averaged transverse modulus of the EF, respectively. On the other hand, the agglomeration parameters have insignificant effect on both Young's modulus and Poisson's ratio compared to other parameters. The sensitivity analysis (SA) also reveals the correlation between the input parameters and its effect on the mechanical properties.",
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note = "Funding information: We gratefully acknowledge the support by the Deutscher Akademischer Austausch Dienst (DAAD) and Alexander von Humboldt Foundation . Xiaoying Zhuang acknowledges the support of Natural Science Foundation of China ( NSFC 41130751 ) and National Basic Research Program of China (973 Program: 2011CB013800 ).",
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AU - Vu-Bac, N.

AU - Rafiee, Roham

AU - Zhuang, Xiaoying

AU - Lahmer, T.

AU - Rabczuk, Timon

N1 - Funding information: We gratefully acknowledge the support by the Deutscher Akademischer Austausch Dienst (DAAD) and Alexander von Humboldt Foundation . Xiaoying Zhuang acknowledges the support of Natural Science Foundation of China ( NSFC 41130751 ) and National Basic Research Program of China (973 Program: 2011CB013800 ).

PY - 2015/9/22

Y1 - 2015/9/22

N2 - We propose a stochastic multiscale method to quantify the correlated key-input parameters influencing the mechanical properties of polymer nanocomposites (PNCs). The variations of parameters at nano-, micro-, meso- and macro-scales are connected by a hierarchical multiscale approach. The first-order and total-effect sensitivity indices are determined first. The input parameters include the single-walled carbon nanotube (SWNT) length, the SWNT waviness, the agglomeration and volume fraction of SWNTs. Stochastic methods consistently predict that the key parameters for the Young's modulus of the composite are the volume fraction followed by the averaged longitudinal modulus of equivalent fiber (EF), the SWNT length, and the averaged transverse modulus of the EF, respectively. The averaged longitudinal modulus of the EF is estimated to be the most important parameter with respect to the Poisson's ratio followed by the volume fraction, the SWNT length, and the averaged transverse modulus of the EF, respectively. On the other hand, the agglomeration parameters have insignificant effect on both Young's modulus and Poisson's ratio compared to other parameters. The sensitivity analysis (SA) also reveals the correlation between the input parameters and its effect on the mechanical properties.

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