A method for epistemic uncertainty quantification and application to uniaxial tension modeling of polymers

被引:1
|
作者
Zhang, Wei [1 ]
Cho, Chongdu [1 ]
机构
[1] Inha Univ, Dept Mech Engn, Inchon 402751, South Korea
关键词
Epistemic uncertainty quantification; Orthogonal experimental design; Range analysis; Interval analysis; Polymer; Nafion membrane; Uniaxial tension modeling; FUEL-CELL; MEMBRANES;
D O I
10.1007/s12206-015-0233-8
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Epistemic uncertainty, whether due to lack of knowledge or data, is often prevalent in practical engineering structures. We present an effective uncertainty quantification method based on orthogonal experimental design, range analysis and interval analysis for analyzing and propagating this type of uncertainty. Orthogonal experimental design and range analysis are first used to investigate the effect of epistemic uncertain factors on different metrics (such as minimum computational cost, maximum computational accuracy and a mixture of two). Interval analysis is then employed to propagate the dominant epistemic uncertainty from input to output through a model. The present method is applied to construct a uniaxial tension model of polymer Nafion membrane with four selected epistemic uncertain factors (mesh method, element type, boundary condition and constitutive model). A good understanding of the influence of these factors on the outputs of the constructed model demonstrates the effectiveness of the present method.
引用
收藏
页码:1199 / 1206
页数:8
相关论文
共 50 条
  • [41] Modeling mesoscale uncertainty for concrete in tension
    Tregger, Nathan
    Corr, David
    Graham-Brady, Lori
    Shah, Surendra
    COMPUTERS AND CONCRETE, 2007, 4 (05): : 347 - 362
  • [42] A practical method for uniaxial tension test of concrete
    Akita, H
    Koide, H
    Tomon, M
    Sohn, D
    MATERIALS AND STRUCTURES, 2003, 36 (260) : 365 - 371
  • [43] A practical method for uniaxial tension test of concrete
    H. Akita
    H. Koide
    M. Tomon
    D. Sohn
    Materials and Structures, 2003, 36 : 365 - 371
  • [44] Research on application method of uncertainty quantification technology in equipment test identification
    Wang, Jiajia
    Chen, Hao
    Ma, Jing
    Zhang, Tong
    2020 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE COMMUNICATION AND NETWORK SECURITY (CSCNS2020), 2021, 336
  • [45] Modeling of nonlinear hysteresis in elastomers under uniaxial tension
    Banks, HT
    Pintér, GA
    Potter, LK
    Gaitens, MJ
    Yanyo, LC
    JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, 1999, 10 (02) : 116 - 134
  • [46] An efficient Bayesian uncertainty quantification approach with application to k-ω-γ transition modeling
    Zhang, Jincheng
    Fu, Song
    COMPUTERS & FLUIDS, 2018, 161 : 211 - 224
  • [47] Novel rough set theory-based method for epistemic uncertainty modeling, analysis and applications
    Wang, Chong
    Fan, Haoran
    Wu, Tao
    APPLIED MATHEMATICAL MODELLING, 2023, 113 : 456 - 474
  • [48] On the quantification of aleatory and epistemic uncertainty using Sliced-Normal distributions
    Crespo, Luis G.
    Colbert, Brendon K.
    Kenny, Sean P.
    Giesy, Daniel P.
    SYSTEMS & CONTROL LETTERS, 2019, 134
  • [49] Mixed aleatory and epistemic uncertainty quantification using fuzzy set theory
    He, Yanyan
    Mirzargar, Mahsa
    Kirby, Robert M.
    INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2015, 66 : 1 - 15
  • [50] Aleatoric and Epistemic Uncertainty Quantification in Bayesian Dirichlet Cost Rules of Thumb
    Fleischer, Sam
    Hooke, Melissa
    2023 IEEE AEROSPACE CONFERENCE, 2023,