Model order reduction of flexible multibody systems described by the ANCF

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Abstract

The absolute nodal coordinate formulation (ANCF) is used to model flexible multibody systems with geometrical nonlinearities of both overall motions and large deformations. However, significant computational cost is required when handling the systems with a great number of degrees of freedom. In order to improve efficiency, the Reduced-Order Models (ROMs) are constructed based on the proper orthogonal decomposition and Galerkin projection methods for multiple simulations of multibody systems. A simple approach for the selection of reduced constraint equations is proposed to deal with the singularity of the coefficient matrices of ROMs. After that, the fast-to-slow ROM method is proposed to make the ROM be adaptive to the change of system parameters. The reduced-order basis vectors extracted from the fast dynamic responses are used to simulate slow dynamic responses. Finally, numerical examples are given to validate the effectiveness and efficiency of the proposed model order reduction scheme for flexible multibody systems.

Original languageEnglish
Title of host publicationProceedings of the 8th ECCOMAS Thematic Conference on MULTIBODY DYNAMICS 2017, MBD 2017
EditorsPavel Polach, Jan Zavrel, Michael Valasek, Michal Hajzman, Zdenek Neusser, Zbynek Sika, Tomas Vampola, Petr Benes
PublisherNational Technical University of Athens
Pages105-112
Number of pages8
ISBN (Electronic)9788001061749
Publication statusPublished - 2017
Event8th ECCOMAS Thematic Conference on MULTIBODY DYNAMICS, MBD 2017 - Prague, Czech Republic
Duration: 19 Jun 201722 Jun 2017

Publication series

NameProceedings of the 8th ECCOMAS Thematic Conference on MULTIBODY DYNAMICS 2017, MBD 2017
Volume2017-January

Conference

Conference8th ECCOMAS Thematic Conference on MULTIBODY DYNAMICS, MBD 2017
Country/TerritoryCzech Republic
CityPrague
Period19/06/1722/06/17

Keywords

  • ANCF
  • Fasttoslow ROM
  • Flexible multibody dynamics
  • Proper orthogonal decomposition
  • Reduced-order model

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