Data di Pubblicazione:
2015
Abstract:
The new full-metal ITER-like wall at JET was found to have a deep impact on the physics of
disruptions at JET. In order to develop disruption classification, the 10D operational space of
JET with the new ITER-like wall has been explored using the generative topographic mapping
method. The 2D map has been exploited to develop an automatic disruption classification of
several disruption classes manually identified. In particular, all the non-intentional disruptions
have been considered, that occurred in JET from 2011 to 2013 with the new wall. A statistical
analysis of the plasma parameters describing the operational spaces of JET with carbon wall
and JET ITER-like wall has been performed and some physical considerations have been
made on the difference between these two operational spaces and the disruption classes which
can be identified. The performance of the JET- ITER-like wall classifier is tested in real-
time in conjunction with a disruption predictor presently operating at JET with good results.
Moreover, to validate and analyse the results, another reference classifier has been developed,
based on the
k
-nearest neighbour technique. Finally, in order to verify the reliability of the
performed classification, a conformal predictor based on non-conformity measures has been
developed.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
disruptions; prediction; classification; machine learning; fusion plasma; real-time control
Elenco autori:
Murari, Andrea
Link alla scheda completa:
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