TY - JOUR

T1 - Uncertainties propagation in metamodel-based probabilistic optimization of CNT/polymer composite structure using stochastic multi-scale modeling

AU - Ghasemi, Hamid

AU - Rafiee, Roham

AU - Zhuang, Xiaoying

AU - Muthu, Jacob

AU - Rabczuk, Timon

N1 - Funding Information: This work was supported partially by Marie Curie Actions under the grant IRSES-MULTIFRAC and German federal ministry of education and research under the Grant BMBF SUA 10/042. Nachwuchsförderprogramm of Ernst Abbe foundation is also acknowledged. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2014/2/1

Y1 - 2014/2/1

N2 - This research focuses on the uncertainties propagation and their effects on reliability of polymeric nanocomposite (PNC) continuum structures, in the framework of the combined geometry and material optimization. Presented model considers material, structural and modeling uncertainties. The material model covers uncertainties at different length scales (from nano-, micro-, meso- to macro-scale) via a stochastic approach. It considers the length, waviness, agglomeration, orientation and dispersion (all as random variables) of Carbon Nano Tubes (CNTs) within the polymer matrix. To increase the computational efficiency, the expensive-to-evaluate stochastic multi-scale material model has been surrogated by a kriging metamodel. This metamodel-based probabilistic optimization has been adopted in order to find the optimum value of the CNT content as well as the optimum geometry of the component as the objective function while the implicit finite element based design constraint is approximated by the first order reliability method. Uncertain input parameters in our model are the CNT waviness, agglomeration, applied load and FE discretization. Illustrative examples are provided to demonstrate the effectiveness and applicability of the present approach.

AB - This research focuses on the uncertainties propagation and their effects on reliability of polymeric nanocomposite (PNC) continuum structures, in the framework of the combined geometry and material optimization. Presented model considers material, structural and modeling uncertainties. The material model covers uncertainties at different length scales (from nano-, micro-, meso- to macro-scale) via a stochastic approach. It considers the length, waviness, agglomeration, orientation and dispersion (all as random variables) of Carbon Nano Tubes (CNTs) within the polymer matrix. To increase the computational efficiency, the expensive-to-evaluate stochastic multi-scale material model has been surrogated by a kriging metamodel. This metamodel-based probabilistic optimization has been adopted in order to find the optimum value of the CNT content as well as the optimum geometry of the component as the objective function while the implicit finite element based design constraint is approximated by the first order reliability method. Uncertain input parameters in our model are the CNT waviness, agglomeration, applied load and FE discretization. Illustrative examples are provided to demonstrate the effectiveness and applicability of the present approach.

KW - Carbon Nano Tube (CNT)

KW - CNT/polymer composite

KW - Multi-scale modeling

KW - Reliability analysis

KW - Reliability Based Design Optimization (RBDO)

UR - http://www.scopus.com/inward/record.url?scp=84893334964&partnerID=8YFLogxK

U2 - 10.1016/j.commatsci.2014.01.020

DO - 10.1016/j.commatsci.2014.01.020

M3 - Article

AN - SCOPUS:84893334964

VL - 85

SP - 295

EP - 305

JO - Computational Materials Science

JF - Computational Materials Science

SN - 0927-0256

ER -