A Generalized Constraint Reduction Method for Reduced Order MBS Models

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

In this paper we deal with the problem of ill-conditioned reduced order models in the context of redundant formulated nonlinear multibody system dynamics. Proper Orthogonal Decomposition is applied to reduce the physical coordinates, resulting in an overdetermined system. As the original set of algebraic constraint equations becomes, at least partially, redundant, we propose a generalized constraint reduction method, based on the ideas of Principal Component Analysis, to identify a unique and well-conditioned set of reduced constraint equations. Finally, a combination of reduced physical coordinates and reduced constraint coordinates are applied to one purely rigid and one partly flexible large-scale model, pointing out method strengths but also applicability limitations.
Original languageEnglish
Pages (from-to)259-274
Number of pages16
JournalMultibody System Dynamics
Volume41
Issue number3
DOIs
Publication statusPublished - 1 Nov 2017

Keywords

  • Constraint reduction
  • Galerkin projection
  • Model order reduction
  • Proper Orthogonal Decomposition
  • Redundant coordinates

Fingerprint

Dive into the research topics of 'A Generalized Constraint Reduction Method for Reduced Order MBS Models'. Together they form a unique fingerprint.

Cite this