This work presents a robust technique, based on the Krylov subspace method, for the reduction of large-scale state-space models arising in many electromagnetic problems in fusion machines. The proposed approach aims at reducing the number of states of the system and lowering the computational effort, with a negligible loss of accuracy in the numerical solution. It is built on the Arnoldi algorithm, which allows to avoid numerical instabilities when computing the reduced model, and exploits both input/output Krylov methods. In the full paper a detail performance study will be presented on an ITER-like machine.

Model order reduction of large-scale state-space models in fusion machines via Krylov methods

BONOTTO, MATTEO;BETTINI, PAOLO;CENEDESE, ANGELO
2016

Abstract

This work presents a robust technique, based on the Krylov subspace method, for the reduction of large-scale state-space models arising in many electromagnetic problems in fusion machines. The proposed approach aims at reducing the number of states of the system and lowering the computational effort, with a negligible loss of accuracy in the numerical solution. It is built on the Arnoldi algorithm, which allows to avoid numerical instabilities when computing the reduced model, and exploits both input/output Krylov methods. In the full paper a detail performance study will be presented on an ITER-like machine.
IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation
9781509010325
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3232140
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact