A locked mode indicator for disruption prediction on JET and ASDEX upgrade
Introduction
Rotating magneto-hydro-dynamic (MHD) modes in tokamaks are often observed to slow down as their amplitude grows up. When a critical threshold is reached, they stop rotating, or, as it is commonly said, they lock in a certain toroidal and poloidal position leading very often to disruption or, in any case, to a degradation of confinement. The slowing down of the mode propagation velocity can be detected directly measuring the voltage induced by the oscillating field perturbation in tangential field pick up coils or Mirnov coils. Before the mode locking, the measured signal exhibits a growing amplitude accompanied by a reduction in frequency until the oscillation disappears, that is when the mode locks. When the mode is locking the amplitude of its radial component can be evaluated through the measure of the voltage induced in the saddle coils. Such amplitude underlies the analysis developed in this work. In a tokamak device, the mode locking is the most frequent precursor of disruptions, even if, in JET-ILW campaigns, an increased rate of disruptions driven by core radiation peaking or impurities has been observed [1], [2]. Usually, mode locking appears at a later stage in the chain of events characterizing the disruptive process. Its process can be caused by intrinsic error fields or by the deceleration of rotating precursor modes [1]. The analysis of these instabilities can be exploited to develop disruption predictors provided that they have an amplitude large enough to be detected before the thermal quench.
As reported in [1,3,4], most disruptions on JET present precursor locked modes that can be due to either error fields or initially rotating modes. However, for a non-negligible number of them, they manifest too late to intervene. On ASDEX Upgrade (AUG), it is more common to see modes that lock prior to the thermal quench, but also modes that are still rotating at the time of the thermal quench. Such difference can be justified by the lower intrinsic error fields in ASDEX Upgrade [5] compared to JET. Also on AUG, in several percent of cases, the instability manifests itself too close to the disruption time to allow any intervention. An analysis of the locking of the mode before the disruption in the databases of JET and AUG is reported in Section 4.
On JET and AUG, protections against locked mode disruptions already exist, which use the amplitude of the locked mode signal as a threshold. Such protection systems are machine dependent: at AUG the raw signal, available in real time for disruption mitigation purposes, provides a measurement in Volt, whereas at JET such signal is in Tesla.
However, the development of portable systems for disruption prediction is becoming of increasingly importance for the next tokamak generations. Referring to JET and AUG, in this paper, a common definition of a locked mode indicator is proposed and the viability to identify a common threshold value is investigated. To this purpose, referring to the JET and AUG experiments, a multi-machine analysis has been performed. In particular, two databases have been built containing about six hundreds of disrupted and non-disrupted discharges in JET with ITER-Like Wall (ILW) from 2012 to 2014 and roughly the same number of shots at AUG from 2009 to 2015. The AUG raw signal has been calibrated in Tesla to be compared with that of JET. Moreover, in order to handle dimensionless quantities both the signals at JET and AUG have been normalized as suggested in [3].
Furthermore, in both machines, the raw signals sometimes show offsets and/or drifts, due to no compensation of the poloidal fluxes generated by the plasma current and the current driving the OH coils. Moreover, active coils for error field correction could introduce off-set that have to be carefully handled. Hence, to avoid the effect of spurious trips in this signal, the trigger level used to request the pulse termination is set to a rather high value. This procedure has the clear drawback of delaying the request to safely shut down the plasma even when, by monitoring the signal itself, the presence of a locked mode could have been detected earlier.
Usually, the roots of drift and off-set are well known, and the correction could be trivial. However, the analysis of their causes could be time consuming and its results do not enhance the disruption prediction capability of the available signal. Hence, removing the drift and the off-set from the point of view of the real time data processing, without getting into the diagnostic limits and machine settings, is sufficient. In this paper, an algorithm for drift and off-set removal has been implemented and applied to the normalized locked mode signal. The algorithm does not require any preliminary analysis, and it can be applied also to those shots not affected by drift and off-set preserving the signal information one is interested in.
Finally, as proposed in the literature [6,7], the resulting locked mode signal has been processed to exploit the content information of both the time and frequency domains resulting in an indicator fitting both devices.
In fact, some algorithms, mostly based on the frequency content of the raw locked mode signal, have been tested at JET for minimizing the effect of these issues so as to provide an earlier alarm than the current locked mode protection system [6]. These algorithms demonstrated to perform better than those that trigger an alarm only when the locked mode amplitude exceeds a prefixed threshold. Also in [7] time-frequency analysis was used to detect anomalies in the locked mode signal that triggers the disruption alarms on JET. Such predictor required a limited number of past experiments to set the alarm threshold.
In the present paper, the validity of the proposed indicator has been assessed by using it as disruption predictor.
Finally, a deep analysis of the prediction errors has been performed to understand the intrinsic limits of the use of only the locked mode signal as a disruption predictor.
The remainder of this paper is organized as follows. Section 2 reports details of the locked mode diagnostics at JET and AUG. The rationale of the locked mode indicator is presented in Section 3. The statistical analysis of the adopted databases is presented in Section 4. Results of the locked mode indicator as disruption predictor, and a reasoned analysis of the obtained results are discussed in Section 5. In Section 6 conclusions are drawn.
Section snippets
Locked mode diagnostics
On JET, the locked mode amplitude is measured by a set of 2 × 4 saddle flux loops, located at radial positions, above and below the middle plane, and mounted on the outside of the vacuum vessel at the low-field side (LFS) of the plasma. They are positioned at a 90° angle to each other. The locked mode amplitude, measure of the predominant odd n = 1 mode, comes from several elaborations of these flux loops signals, such as integration and the appropriate compensation of the poloidal fluxes
Locked mode indicator
Generally speaking, an indicator can synthesize more signals in order to describe a complex phenomenology, or can be based on a single signal when the signal itself is intrinsically representative of a disruptive behaviour as, for example, in the case of the locked mode. In most circumstances, in order to maximize the information content in the signal, it can be required to remove noise or unwanted spikes, or simply to extract the trend of the signal filtering transient phenomena. In this
Databases
In order to statistically assess the effectiveness and robustness of the proposed indicator, it has been tested as a disruption predictor with both JET and AUG data. For the sake of comparison, the performance of the prediction systems, obtained by optimizing a threshold on the actual locked mode trigger, i.e., the raw signal normalized with respect to the plasma current Ipla on JET (BLM/Ipla) in [T/MA], and the raw signal (LM-raw) in [V] at AUG, have been computed. Moreover, also the
Locked mode signal for disruption prediction
Presently, on JET and AUG, the protection against disruptions uses the amplitude of the locked mode signal as a trigger. In particular, the alarm threshold is set on the BML/Ipla signal in [mT/MA] at JET and on the LM-raw signal in [V] at AUG. Hence, there is no correlation between the two trigger values. In a view of a machine independent disruption prediction approach, dissimilarities between the two locked mode triggers can be overcome taking into account, for both machines, the raw locked
Analysis of prediction errors
This section reports the results of the analysis performed to identify the causes of FAs, MAs, and TDs for both the normalized raw signal and the LM indicator. The following analysis shows that some intrinsic problems in the raw signal such as drifts, offsets, and error field pick up coils (EFCC), can cause false alarms and incongruous alarms, which are not related to physical reasons.
The analysis has been made making reference to the different predictions performed by the two considered
Conclusions
The main aim of the paper was to evaluate the advantages and the limits of the proposed locked mode indicator if used in a disruption predictor instead of the raw signal, highlighting the possibility of improving the mode locking detection through a suitable processing algorithm exploiting both information in time and frequency domain. The paper, not only presents the performance of a locked mode based disruption predictor but also analyses the wrong prediction causes and investigates the
Acknowledgements
This work has been carried out within the framework of the EUROfusion Consortium and received funding from the EURATOM research and training programme 2014–2018 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission.
References (12)
- et al.
Use of the disruption mitigation valve in closed loop for routine protection at JET
Fusion Eng. Des.
(2013) - et al.
Improvements in disruption prediction at ASDEX Upgrade
Nucl. Fusion
(2015) - et al.
Survey of disruption causes at JET
Nucl. Fusion
(2011) The influence of an ITER-like wall on disruptions at JET
Phys. Plasmas
(2014)Scaling of the MHD perturbation amplitude required to trigger a disruption and predictions for ITER
Nucl. Fusion
(2016)Measurement and impact of the n=1 intrinsic error field at ASDEX Upgrade
40th EPS Conference on Plasma Physics
(2018)
Cited by (13)
Tokamak plasma disruption precursor onset time study based on semi-supervised anomaly detection
2023, Nuclear Engineering and Technology
- 1
See the author list “A. Kallenbach et al., Nucl. Fusion 57 102015”
- 2
See the author list “H. Meyer et al 2017 Nucl. Fusion 57 102014”
- 3
See the author list of “X. Litaudon et al 2017 Nucl. Fusion 57 102001”