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EnglishThis 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.
| Titolo: | Model order reduction of large-scale state-space models in fusion machines via Krylov methods |
| Autori: | |
| Data di pubblicazione: | 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. |
| Handle: | http://hdl.handle.net/11577/3232140 |
| ISBN: | 9781509010325 |
| Appare nelle tipologie: | 04.01 - Contributo in atti di convegno |
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