Reduced order models using generalized eigenanalysis

被引:12
作者
Ripepi, M. [1 ]
Masarati, P. [1 ]
机构
[1] Politecn Milan, Dipartimento Ingn Aerospaziale, I-20156 Milan, Italy
关键词
eigensolution; model order reduction; generalized Schur decomposition; MULTIBODY; DYNAMICS; REDUCTION; STABILITY;
D O I
10.1177/14644193JMBD254
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
This article discusses the use of generalized eigenanalysis to extract reduced order models from the linearization of structural and aeroelastic problems written in differential-algebraic form. These problems may arise from multi-body analysis and in general from mixed approaches, where a high degree of generality and modelling flexibility are sought. A method based on a shift technique is proposed, that allows to exploit the regularity of the matrix pencil resulting from the linearization of differential-algebraic problems. Alternatively, the generalized Schur decomposition, or QZ decomposition, is directly used to select a cluster of eigenvalues related to the dynamic states. The two approaches are used to reduce the model to ordinary differential in state-space form. The two methods are applied to simple numerical problems, highlighting their robustness and versatility compared to other techniques. They are also applied to numerical models of a high-altitude long endurance aircraft obtained using a free general-purpose multi-body solver and a dedicated mixed variational solver.
引用
收藏
页码:52 / 65
页数:14
相关论文
共 50 条
  • [1] Continuation of nonlinear normal modes using reduced-order models based on generalized characteristic value decomposition
    Stein, Dalton L.
    Chelidze, David
    NONLINEAR DYNAMICS, 2025, 113 (01) : 25 - 45
  • [2] A generalized constraint reduction method for reduced order MBS models
    Daniel Stadlmayr
    Wolfgang Witteveen
    Wolfgang Steiner
    Multibody System Dynamics, 2017, 41 : 259 - 274
  • [3] A generalized constraint reduction method for reduced order MBS models
    Stadlmayr, Daniel
    Witteveen, Wolfgang
    Steiner, Wolfgang
    MULTIBODY SYSTEM DYNAMICS, 2017, 41 (03) : 259 - 274
  • [4] Design optimization using hyper-reduced-order models
    Amsallem, David
    Zahr, Matthew
    Choi, Youngsoo
    Farhat, Charbel
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2015, 51 (04) : 919 - 940
  • [5] Model predictive control using reduced order models: Guaranteed stability for constrained linear systems
    Loehning, Martin
    Reble, Marcus
    Hasenauer, Jan
    Yu, Shuyou
    Allgoewer, Frank
    JOURNAL OF PROCESS CONTROL, 2014, 24 (11) : 1647 - 1659
  • [6] INTERACTIVE THERMO-FLUID SIMULATION BY USING REDUCED ORDER MODELS
    Kobayashi, Sachio
    Kobayashi, Hiroki
    Ikeda, Hiroshi
    Hashima, Masayoshi
    Sato, Yuichi
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2013, VOL 2A, 2014,
  • [7] A conditioning technique for projection-based reduced order models
    Lupini, Andrea
    Epureanu, Bogdan, I
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2019, 349 : 251 - 265
  • [8] From Data to Reduced-Order Models via Generalized Balanced Truncation
    Burohman, Azka Muji
    Besselink, Bart
    Scherpen, Jacquelien M. A.
    Camlibel, M. Kanat
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2023, 68 (10) : 6160 - 6175
  • [9] Reduced-Order Models for Representing Converters in Power System Studies
    Gu, Yunjie
    Bottrell, Nathaniel
    Green, Timothy C.
    IEEE TRANSACTIONS ON POWER ELECTRONICS, 2018, 33 (04) : 3644 - 3654
  • [10] Pressure data-driven variational multiscale reduced order models
    Ivagnes, Anna
    Stabile, Giovanni
    Mola, Andrea
    Iliescu, Traian
    Rozza, Gianluigi
    JOURNAL OF COMPUTATIONAL PHYSICS, 2023, 476