scholarly journals Model Order Reduction and Error Estimation for the Parameter-Dependent Eddy Current Equation

PAMM ◽  
2011 ◽  
Vol 11 (1) ◽  
pp. 815-816
Author(s):  
Nadine Jung
Keyword(s):  
Error Estimation ◽  
Eddy Current ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Model Order ◽  
Parameter Dependent ◽  
Current Equation

Model order reduction techniques applied to magnetodynamic T-Ω-formulation

2020 ◽  
Vol 39 (5) ◽  
pp. 1057-1069
Author(s):  
Fabian Müller ◽  
Lucas Crampen ◽  
Thomas Henneron ◽  
Stephane Clénet ◽  
Kay Hameyer
Keyword(s):  
Eddy Current ◽  
Model Order Reduction ◽  
Scalar Potential ◽  
Order Reduction ◽  
Computational Effort ◽  
Transient Field ◽  
Model Order ◽  
Field Problem ◽  
Content Type ◽  
Reduction Techniques

Purpose The purpose of this paper is to use different model order reduction techniques to cope with the computational effort of solving large systems of equations. By appropriate decomposition of the electromagnetic field problem, the number of degrees of freedom (DOF) can be efficiently reduced. In this contribution, the Proper Generalized Decomposition (PGD) and the Proper Orthogonal Decomposition (POD) are used in the frame of the T-Ω-formulation, and the feasibility is elaborated. Design/methodology/approach The POD and the PGD are two methods to reduce the model order. Particularly in the context of eddy current problems, conventional time-stepping algorithms can lead to many numerical simulations of the studied problem. To simulate the transient field, the T-Ω-formulation is used which couples the magnetic scalar potential and the electric vector potential. In this paper, both methods are studied on an academic example of an induction furnace in terms of accuracy and computational effort. Findings Using the proposed reduction techniques significantly reduces the DOF and subsequently the computational effort. Further, the feasibility of the combination of both methods with the T-Ω-formulation is given, and a fundamental step toward fast simulation of eddy current problems is shown. Originality/value In this paper, the PGD is combined for the first time with the T-Ω-formulation. The application of the PGD and POD and the following comparison illustrate the great potential of these techniques in combination with the T-Ω-formulation in context of eddy current problems.

scholarly journals Cauer Ladder Network With Multiple Expansion Points for Efficient Model Order Reduction of Eddy-Current Field

2019 ◽  
Vol 55 (6) ◽  
pp. 1-4 ◽  
Author(s):  
Kenta Kuriyama ◽  
Akihisa Kameari ◽  
Hassan Ebrahimi ◽  
Takayuki Fujiwara ◽  
Kengo Sugahara ◽  
...  
Keyword(s):  
Eddy Current ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Current Field ◽  
Model Order ◽  
Ladder Network

Model order reduction of random parameter-dependent linear systems

Automatica ◽  
2015 ◽  
Vol 55 ◽  
pp. 95-107 ◽  
Author(s):  
Lyès Nechak ◽  
Henri-François Raynaud ◽  
Caroline Kulcsár
Keyword(s):  
Linear Systems ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Random Parameter ◽  
Model Order ◽  
Parameter Dependent

scholarly journals Model Order Reduction for Parameter Dependent Substructured Systems using Krylov Subspaces

2018 ◽  
Vol 51 (2) ◽  
pp. 553-558 ◽  
Author(s):  
Nadine Walker ◽  
Benjamin Fröhlich ◽  
Peter Eberhard
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Krylov Subspaces ◽  
Model Order ◽  
Parameter Dependent
Sign in / Sign up

Export Citation Format

Share Document