Structure-preserving Galerkin POD reduced-order modeling of Hamiltonian systems

被引:33
作者
Gong, Yuezheng [1 ]
Wang, Qi [1 ,2 ,3 ]
Wang, Zhu [2 ]
机构
[1] Beijing Computat Sci Res Ctr, Appl & Computat Math Div, Beijing 100193, Peoples R China
[2] Univ South Carolina, Dept Math, Columbia, SC 29208 USA
[3] Nankai Univ, Sch Mat Sci & Engn, Tianjin 300350, Peoples R China
基金
美国国家科学基金会; 中国博士后科学基金;
关键词
Proper orthogonal decomposition; Model reduction; Hamiltonian systems; Structure-preserving algorithms; FINITE-DIFFERENCE SCHEMES; NUMERICAL-METHODS; TURBULENT FLOWS; REDUCTION; PDES; INTERPOLATION; DISSIPATION; INTEGRATION; STABILITY; EQUATIONS;
D O I
10.1016/j.cma.2016.11.016
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The proper orthogonal decomposition reduced-order model (POD-ROM) has been widely used as a computationally efficient surrogate model in large-scale numerical simulations of complex systems. However, when it is applied to a Hamiltonian system, a naive application of the POD method can destroy the Hamiltonian structure in the reduced-order modelin this paper, we develop a new reduced-order modeling approach for Hamiltonian systems, which modifies the Galerkin projection-based POD -ROM so that the appropriate Hamiltonian structure is preserved. Since the POD truncation can degrade the approximation of the Hamiltonian function, we propose to use a POD basis from shifted snapshots to improve the approximation to the Hamiltonian function. We further derive a rigorous a priori error estimate for the structure-preserving ROM and demonstrate its effectiveness in several numerical examples. This approach can be readily extended to dissipative Hamiltonian systems, port-Hamiltonian systems, etc. Published by Elsevier B.V.
引用
收藏
页码:780 / 798
页数:19
相关论文
共 41 条
  • [1] Attia A., 2016, INT J NUMER METHODS
  • [2] Minimal subspace rotation on the Stiefel manifold for stabilization and enhancement of projection-based reduced order models for the compressible Navier-Stokes equations
    Balajewicz, Maciej
    Tezaur, Irina
    Dowell, Earl
    [J]. JOURNAL OF COMPUTATIONAL PHYSICS, 2016, 321 : 224 - 241
  • [3] Stable Galerkin reduced order models for linearized compressible flow
    Barone, Matthew F.
    Kalashnikova, Irina
    Segalman, Daniel J.
    Thornquist, Heidi K.
    [J]. JOURNAL OF COMPUTATIONAL PHYSICS, 2009, 228 (06) : 1932 - 1946
  • [4] Beattie C, 2011, IEEE DECIS CONTR P, P6564, DOI 10.1109/CDC.2011.6161504
  • [5] Interpolatory projection methods for structure-preserving model reduction
    Beattie, Christopher
    Gugercin, Serkan
    [J]. SYSTEMS & CONTROL LETTERS, 2009, 58 (03) : 225 - 232
  • [6] Numerical methods for Hamiltonian PDEs
    Bridges, Thomas J.
    Reich, Sebastian
    [J]. JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, 2006, 39 (19): : 5287 - 5320
  • [7] Goal-oriented, model-constrained optimization for reduction of large-scale systems
    Bui-Thanh, T.
    Willcox, K.
    Ghattas, O.
    Waanders, B. van Bloemen
    [J]. JOURNAL OF COMPUTATIONAL PHYSICS, 2007, 224 (02) : 880 - 896
  • [8] PRESERVING LAGRANGIAN STRUCTURE IN NONLINEAR MODEL REDUCTION WITH APPLICATION TO STRUCTURAL DYNAMICS
    Carlberg, Kevin
    Tuminaro, Ray
    Boggs, Paul
    [J]. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2015, 37 (02) : B153 - B184
  • [9] The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows
    Carlberg, Kevin
    Farhat, Charbel
    Cortial, Julien
    Amsallem, David
    [J]. JOURNAL OF COMPUTATIONAL PHYSICS, 2013, 242 : 623 - 647
  • [10] A low-cost, goal-oriented 'compact proper orthogonal decomposition' basis for model reduction of static systems
    Carlberg, Kevin
    Farhat, Charbel
    [J]. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2011, 86 (03) : 381 - 402