A data-driven approach to nonlinear elasticity

被引:122
|
作者
Nguyen, Lu Trong Khiem [1 ]
Keip, Marc-Andre [1 ]
机构
[1] Univ Stuttgart, Inst Appl Mech CE, Chair Mat Theory, Pfaffenwaldring 7, D-70569 Stuttgart, Germany
关键词
Data-driven computational mechanics; Optimization method; Data science; Geometrical nonlinearity; Model-free; HYPERELASTIC MATERIAL; STRAIN-ENERGY; MODEL; IMPLEMENTATION; ROTATIONS; EXTENSION;
D O I
10.1016/j.compstruc.2017.07.031
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The so-called distance-minimizing data-driven computing method is extended to deal with boundary value problems of continuum mechanics within the finite strain theory. In the merit of a data-driven model the solution process is carried out by using directly the experimental data instead of the conventional constitutive laws. Thus it bypasses the uncertainties in fabricating the stress-strain functional relationships from material data. Consequently, the mathematical formulation involves an optimization problem with equality constraints consisting of the equilibrium equations in continuum mechanics and the compatibility conditions on the displacement field. In the framework of finite element formulation the element tangent stiffness, the generalized internal force and the generalized external force can be computed, which renders it amenable to the implementation of finite element procedures. The proposed scheme is validated through the applications to continuum elements and convergence studies of the data-driven solution in regard to the interpolation order, the mesh size as well as the data size. The variational structure allows to recognize the overall pattern of the system of equations to be solved. This includes the structural tangent stiffness and the generalized force vectors. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:97 / 115
页数:19
相关论文
共 50 条
  • [1] Data-Driven Problems in Elasticity
    Conti, S.
    Mueller, S.
    Ortiz, M.
    [J]. ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS, 2018, 229 (01) : 79 - 123
  • [2] Data-Driven Problems in Elasticity
    S. Conti
    S. Müller
    M. Ortiz
    [J]. Archive for Rational Mechanics and Analysis, 2018, 229 : 79 - 123
  • [3] Data-Driven Finite Elasticity
    S. Conti
    S. Müller
    M. Ortiz
    [J]. Archive for Rational Mechanics and Analysis, 2020, 237 : 1 - 33
  • [4] Data-Driven Finite Elasticity
    Conti, S.
    Mueller, S.
    Ortiz, M.
    [J]. ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS, 2020, 237 (01) : 1 - 33
  • [5] A data-driven approach to nonlinear braking control
    Novara, Carlo
    Formentin, Simone
    Savaresi, Sergio M.
    Milanese, Mario
    [J]. 2015 54TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2015, : 1453 - 1458
  • [6] A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity
    Rubén Ibañez
    Emmanuelle Abisset-Chavanne
    Jose Vicente Aguado
    David Gonzalez
    Elias Cueto
    Francisco Chinesta
    [J]. Archives of Computational Methods in Engineering, 2018, 25 : 47 - 57
  • [7] A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity
    Ibanez, Ruben
    Abisset-Chavanne, Emmanuelle
    Aguado, Jose Vicente
    Gonzalez, David
    Cueto, Elias
    Chinesta, Francisco
    [J]. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2018, 25 (01) : 47 - 57
  • [8] A Convex Data-Driven Approach for Nonlinear Control Synthesis
    Choi, Hyungjin
    Vaidya, Umesh
    Chen, Yongxin
    [J]. MATHEMATICS, 2021, 9 (19)
  • [9] Data-driven approach for fault detection and isolation in nonlinear system
    Kallas, Maya
    Mourot, Gilles
    Maquin, Didier
    Ragot, Jose
    [J]. INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2018, 32 (11) : 1569 - 1590
  • [10] Data-driven control of nonlinear systems: An online sequential approach
    Vu, Minh
    Huang, Yunshen
    Zeng, Shen
    [J]. Systems and Control Letters, 2024, 193