One of the major problems in present tokamaks is the presence of disruptions. If disruptions are not mitigated, they can produce serious damage to th e device. Therefore, disruption predictors are needed in order to apply the mitigation techniques in time. In this paper, the real - time implementation in JET of a new type of disruption predictor is presented. The new predictor, Single signal Predictor bas ed on Anomaly Detection (SPAD), does not require past discharges for training purposes. The implementation is based on the Multi - threaded Application Real - Time executor (MARTe) framework. Analysis over all JET's ITER - like Wall campaigns (C28 - C34) show that SPAD was able to predict 83.57% of the disruptions with enough time to apply mitigation techniques. The average anticipation time was 389 ms. In this paper the real - time implementation will be discussed, as well as the optimizations developed to make the algorithm suitable for real - time processing. Performance results and possible improvements will also be analyzed.

Real-time implementation in JET of the SPAD disruption predictor using MARTe

Murari A;
2016

Abstract

One of the major problems in present tokamaks is the presence of disruptions. If disruptions are not mitigated, they can produce serious damage to th e device. Therefore, disruption predictors are needed in order to apply the mitigation techniques in time. In this paper, the real - time implementation in JET of a new type of disruption predictor is presented. The new predictor, Single signal Predictor bas ed on Anomaly Detection (SPAD), does not require past discharges for training purposes. The implementation is based on the Multi - threaded Application Real - Time executor (MARTe) framework. Analysis over all JET's ITER - like Wall campaigns (C28 - C34) show that SPAD was able to predict 83.57% of the disruptions with enough time to apply mitigation techniques. The average anticipation time was 389 ms. In this paper the real - time implementation will be discussed, as well as the optimizations developed to make the algorithm suitable for real - time processing. Performance results and possible improvements will also be analyzed.
2016
Istituto gas ionizzati - IGI - Sede Padova
Inglese
20th Real Time Conference - RT 2016
2
https://indico.cern.ch/event/390748/contributions/1825167/attachments/1281638/1904245/CR_RTA2_30v2.pdf
5-10 June 2016
Padova, Italy
disruption predictors
fusion experiments
plasma disruptions
real-time processing
This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Projects No EN E2015 - 64914 - C3 - 1 - R, ENE2015 - 64914 - C3 - 2 - R, ENE2015 - 64914 - C3 - 3 - R, predoctoral fellowship BES - 2013 - 064875, and the grant for predoctoral short - term stays in R&D centers (2014). This work has been carried out within the framework of the EUROfusion Consortium a nd has received funding from the Euratom research and training programme 2014 - 2018 under grant agreement No 633053.
10
none
Esquembri, S; Vega, J; Murari, A; Ruiz, M; Barrera, E; Dormidocanto, S; Felton, R; Tsalas, M; Valcarcel, D; Jet, Contributors
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
   Implementation of activities described in the Roadmap to Fusion during Horizon 2020 through a Joint programme of the members of the EUROfusion consortium
   EUROfusion
   H2020
   633053
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/327789
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