This work presents a robust technique, based on Krylov subspace method, for the reduction of large-scale state-space models arising in many electromagnetic applications in fusion machines. The proposed approach, built on the Arnoldi algorithm, 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. A detailed performance study is presented on an ITER-like machine, addressing both 2D and 3D problems.

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

BONOTTO, MATTEO;BETTINI, PAOLO;CENEDESE, ANGELO
2017

Abstract

This work presents a robust technique, based on Krylov subspace method, for the reduction of large-scale state-space models arising in many electromagnetic applications in fusion machines. The proposed approach, built on the Arnoldi algorithm, 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. A detailed performance study is presented on an ITER-like machine, addressing both 2D and 3D problems.
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/11577/3221311
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 3
social impact