Model Order Reduction for Parametric Non-linear Mechanical Systems: State of the Art and Future Research

PAMM ◽  
2017 ◽  
Vol 17 (1) ◽  
pp. 37-40 ◽  
Author(s):  
Christian H. Meyer ◽  
Christopher Lerch ◽  
Boris Lohmann ◽  
Daniel J. Rixen
Keyword(s):  
Model Order Reduction ◽  
State Of The Art ◽  
Mechanical Systems ◽  
Order Reduction ◽  
Future Research ◽  
Model Order ◽  
Non Linear

Non-linear model order reduction using input to state Hammerstein structures

2009 ◽  
Vol 42 (11) ◽  
pp. 387-392
Author(s):  
O. Naeem ◽  
A.E.M. Huesman ◽  
O.H. Bosgra
Keyword(s):  
Linear Model ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Model Order ◽  
Non Linear

scholarly journals Some contributions to the model order reduction of large scale non-linear electric circuits

PAMM ◽  
2010 ◽  
Vol 10 (1) ◽  
pp. 639-640 ◽  
Author(s):  
Juan Pablo Amorocho D. ◽  
Heike Faßbender
Keyword(s):  
Model Order Reduction ◽  
Large Scale ◽  
Order Reduction ◽  
Model Order ◽  
Electric Circuits ◽  
Non Linear

scholarly journals Basic principles and aims of model order reduction in compliant mechanisms

2011 ◽  
Vol 2 (2) ◽  
pp. 197-204 ◽  
Author(s):  
M. Rösner ◽  
R. Lammering
Keyword(s):  
Model Order Reduction ◽  
Large Scale ◽  
Krylov Subspace ◽  
Compliant Mechanisms ◽  
Linear Time ◽  
Order Reduction ◽  
Research Direction ◽  
Future Research ◽  
Model Order ◽  
Advantages And Disadvantages

Abstract. Model order reduction appears to be beneficial for the synthesis and simulation of compliant mechanisms due to computational costs. Model order reduction is an established method in many technical fields for the approximation of large-scale linear time-invariant dynamical systems described by ordinary differential equations. Based on system theory, underlying representations of the dynamical system are introduced from which the general reduced order model is derived by projection. During the last years, numerous new procedures were published and investigated appropriate to simulation, optimization and control. Singular value decomposition, condensation-based and Krylov subspace methods representing three order reduction methods are reviewed and their advantages and disadvantages are outlined in this paper. The convenience of applying model order reduction in compliant mechanisms is quoted. Moreover, the requested attributes for order reduction as a future research direction meeting the characteristics of compliant mechanisms are commented.

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