A model-based, multichannel, real-time capable sawtooth crash detector

TitleA model-based, multichannel, real-time capable sawtooth crash detector
Publication TypeJournal Article
Year of Publication2016
AuthorsH. van den Brand, M.R de Baar, M. van Berkel, T.C Blanken, F. Felici, E. Westerhof, M. Willensdorfer, ASDEX Upgrade Team, EUROfusion MST1 Team
JournalPlasma Physics and Controlled Fusion
Volume58
Issue7
Pagination075002
Abstract

Control of the time between sawtooth crashes, necessary for ITER and DEMO, requires real-time detection of the moment of the sawtooth crash. In this paper, estimation of sawtooth crash times is demonstrated using the model-based interacting multiple model (IMM) estimator, based on simplified models for the sawtooth crash. In contrast to previous detectors, this detector uses the spatial extent of the sawtooth crash as detection characteristic. The IMM estimator is tuned and applied to multiple ECE channels at once. A model for the sawtooth crash is introduced, which is used in the IMM algorithm. The IMM algorithm is applied to seven datasets from the ASDEX Upgrade tokamak. Five crash models with different mixing radii are used. All sawtooth crashes that have been identified beforehand by visual inspection of the data, are detected by the algorithm. A few additional detections are made, which upon closer inspection are seen to be sawtooth crashes, which show a partial reconnection. A closer inspection of the detected normal crashes shows that about 42% are not well fitted by any of the full reconnection models and show some characteristics of a partial reconnection. In some case, the measurement time is during the sawtooth crashes, which also results in an incorrect estimate of the mixing radius. For data provided at a sampling rate of 1 kHz, the run time of the IMM estimator is below 1 ms, thereby fulfilling real-time requirements.

DOI10.1088/0741-3335/58/7/075002
Division

FP

Department

TP

PID

2d0cfa45bbaef23160c58b91be16485d

Alternate TitlePlasma Phys. Control. Fusion
LabelOA
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