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Model‐order nonlinear subspace reduction of electric machines by means of POD and DEI methods for copper losses calculation
Author(s) -
Al Eit M.,
Bouillault F.,
Marchand C.,
Krebs G.
Publication year - 2017
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2274
Subject(s) - reduction (mathematics) , model order reduction , nonlinear system , subspace topology , transient (computer programming) , finite element method , interpolation (computer graphics) , mathematics , control theory (sociology) , mathematical optimization , computer science , algorithm , mathematical analysis , engineering , physics , structural engineering , computer graphics (images) , animation , projection (relational algebra) , geometry , control (management) , quantum mechanics , artificial intelligence , operating system
The simulation of electric machines in order to calculate the copper losses is about a time‐dependent electromagnetic problem. When the finite element method associated with a time stepping scheme is used to solve the problem, the solution is strongly linked to initial conditions, among which the most important is the solution at the initial time. Because it is practically chosen as an arbitrary solution, several time‐consuming electrical excitation periods must be simulated therefore to reach finally the steady‐state conditions. The copper losses can be calculated now without any transient components that can affect the credibility of the copper losses amount. This article suggests a model‐order reduction method that benefits from the complete model finite element solution of the first transient electrical period, to calculate the reduced model solution in the subsequent periods using the proper orthogonal decomposition approach combined with the discrete empirical interpolation method. Nevertheless, in case of relatively high frequency excitation, the full reduction of the problem leads to significant imprecision in the amount of copper losses. To improve the accuracy, therefore, a nonlinear subspace model‐order reduction is adopted. It ensures concurrently higher precision and a reduced computational time.