ERROR CONTROLLED USE OF THE TAYLOR ASSUMPTION IN ADAPTIVE HIERARCHICAL MODELING OF DSS

被引:0
|
作者
Lillbacka, Robert [1 ]
Larsson, Fredrik [2 ]
Runesson, Kenneth [2 ]
机构
[1] FS Dynam, SE-41263 Gothenburg, Sweden
[2] Chalmers Univ Technol, Dept Appl Mech, SE-41296 Gothenburg, Sweden
关键词
adaptive modeling; goal-oriented adaptivity; computational homogenization; duplex stainless steel; HETEROGENEOUS MATERIALS; HOMOGENIZATION; DISCRETIZATION; PLASTICITY; MICROSTRUCTURE; SIMULATION; FAILURE; SCALES; DAMAGE;
D O I
10.1615/IntJMultCompEng.2014000539
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A strategy for macroscale modeling adaptivity in fully nested two-scale computational (first-order) homogenization based on assumed scale separation is proposed. The representative volume element (RVE) for a substructure pertinent to duplex stainless steel is considered with its typical phase morphology, whereby crystal plasticity with hardening is adopted for the subscale material modeling. The quality of the macroscale constitutive response depends on, among the various assumptions regarding the modeling and discretization, the choice of a prolongation condition defining the deformation mapping from the macro- to the subscale This is the sole source of model error discussed in the present contribution. Two common choices are (in hierarchical order) (1) a "simplified" model based on homogeneous (macroscale) deformation within the RVE, that is the Taylor assumption, and (2) a "reference" model employing Dirichlet boundary conditions on the RVE, which is taken as the exact model in the present context. These errors are assessed via computation of the pertinent dual problem. The results show that both the location and the number of qudrature points where the reference model is employed depend on the chosen goal function.
引用
收藏
页码:163 / 180
页数:18
相关论文
共 50 条
  • [31] Preface to the special issue on modeling error estimation and adaptive modeling in computational mechanics
    Prudhomme, Serge
    Oden, J. Tinsley
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2011, 200 (37-40) : 2625 - 2625
  • [32] Hierarchical Sensor Placement Using Joint Entropy and the Effect of Modeling Error
    Papadopoulou, Maria
    Raphael, Benny
    Smith, Ian F. C.
    Sekhar, Chandra
    ENTROPY, 2014, 16 (09): : 5078 - 5101
  • [33] MODELING PITCH TRAJECTORY BY HIERARCHICAL HMM WITH MINIMUM GENERATION ERROR TRAINING
    Wu, Yi-Jian
    Soong, Frank
    2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2012, : 4017 - 4020
  • [34] Trial and error: A hierarchical modeling approach to test-retest reliability
    Chen, Gang
    Pine, Daniel S.
    Brotman, Melissa A.
    Smith, Ashley R.
    Cox, Robert W.
    Haller, Simone P.
    NEUROIMAGE, 2021, 245
  • [35] Adaptive Seabed Characterization With Hierarchical Bayesian Modeling of SAS Imagery
    Brandes, T. Scott
    Ballard, Brett
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (03): : 1278 - 1290
  • [36] HIERARCHICAL ADAPTIVE MODELING OF STRUCTURAL SANDWICHES AND MULTILAYERED COMPOSITE PANELS
    NOOR, AK
    BURTON, WS
    PETERS, JM
    APPLIED NUMERICAL MATHEMATICS, 1994, 14 (1-3) : 69 - 90
  • [37] HIERARCHICAL ADAPTIVE MODELING OF STRUCTURAL SANDWICHES AND MULTILAYERED COMPOSITE PANELS
    NOOR, AK
    BURTON, WS
    PETERS, JM
    ENGINEERING FRACTURE MECHANICS, 1995, 50 (5-6) : 801 - 817
  • [38] Cyclical Hierarchical Modeling for Water Quality Model-Based DSS Module in an Urban River System
    Roy, Samapriya
    Katpatal, Y. B.
    JOURNAL OF ENVIRONMENTAL ENGINEERING, 2011, 137 (12) : 1176 - 1184
  • [39] Adaptive hierarchical upscaling of flow in heterogeneous reservoirs based on an a posteriori error estimate
    K.J. Hersvik
    M.S. Espedal
    Computational Geosciences, 1998, 2 : 311 - 336
  • [40] An adaptive remeshing technique based on hierarchical error estimates for simulation of semiconductor devices
    Yang, G
    Wang, RC
    Wang, SD
    INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS, 2004, 17 (01) : 17 - 28