Implementation of a new Disruption Mitigation System into the control system of JET

Jachmich, S.; Kruezi, U.; Card, P.; Deakin, K.; Kinna, D.; Koslowski, H. R.; Lambertz, H.T.; Lehnen, M.

doi:10.1016/j.fusengdes.2015.01.047

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…with M IZ1 = octant 1 first plasma current vertical moment, etc., see figure 9. MGI (see figure 10) of a mixture (90% D 2 + 10% Ar) is routinely used to mitigate disruptions in JET, where the MGI fire criteria for the year 2014 were: I p > 2 MA or total plasma energy (poloidal magnetic + kinetic) is above 5 MJ [37][38][39][40]. The MGI has a profound effect on 3D phenomena during the plasma CQ (see figure 11), hence the MGI shots are specifically labelled on the figures presented below.…”

Section: Jet and Compass Databasementioning

confidence: 99%

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JET and COMPASS asymmetrical disruptions

et al. 2015

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Asymmetrical disruptions may occur during ITER operation and they may be accompanied by large sideways forces and rotation of the asymmetry. This is of particular concern because resonance of the rotating asymmetry with the natural frequencies of the vacuum vessel (and other in-vessel components) could lead to large dynamic amplification of the forces. A significant fraction of non-mitigated JET disruptions have toroidally asymmetric currents that flow partially inside the plasma and partially inside the surrounding vacuum vessel ("wall"). The toroidal asymmetries (otherwise known as appearance of 3-D structures) are clearly visible in the plasma current (I p) and the first plasma current moments. For the first time we present here the asymmetries in toroidal flux measured by the diamagnetic loops and also propose a physical interpretation. The presented data covers the period of JET operation with C-wall (JET-C from 2005 until late 2009) and with ITER-like wall (JET-ILW from 2011 until late 2014), during which pickup coil and saddle loop data at four toroidally orthogonal

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Section: Jet and Compass Databasementioning

confidence: 99%

“…Massive gas injection (MGI) is routinely used to mitigate disruptions in JET, if I p > 2 MA or the total plasma energy (poloidal magnetic + kinetic) is above 5 MJ [37][38][39][40]. It is shown here that MGI can significantly reduce the I p asymmetries during the plasma CQ and sideways forces.…”

Section: Introductionmentioning

confidence: 99%

JET and COMPASS asymmetrical disruptions

et al. 2015

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“…As a result, active mitigation using massive gas injection (MGI) is required for all JET-ILW pulses with a predicted plasma current > 2 MA or total internal (poloidal magnetic + kinetic) above 5 MJ. In practice, this means that such mitigation is required for almost the entire JET programme and, as a result a second, tritium-compatible injector has been installed on JET [12,13]. Disruption mitigation, which is done routinely with 10% Ar/90% D 2 , has been largely successful -of the 143 pulses in the 2014 high current development programme, 35 disrupted and only three were unintentionally unmitigated and all of those at ≤ 2 MA [14].…”

Section: Disruptions and Runaway Electron Beamsmentioning

confidence: 99%

Operation of JET with an ITER-like Wall

Horton

2015

Fusion Engineering and Design

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“…This can be achieved in several ways; for example, by introducing cold massive particles into the plasma via a cold gas injection or pellet injection, which will rapidly absorb and radiate the energy away. Both methods were studied for use in the ITER (International Thermonuclear Experimental Reactor) [6][7][8]. For these mitigation methods to work, it is, however, imperative that these disruptive events can be predicted in advance of a non-trivial task, so that they can be activated in time.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Machine Learning Techniques for Disruption Prediction Using JET Data

Croonen

Amaya

Lapenta

2023

Plasma

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Disruption prediction and mitigation is of key importance in the development of sustainable tokamak reactors. Machine learning has become a key tool in this endeavour. In this paper, multiple machine learning models are tested and compared. A focus has been placed on the analysis of a transition to dimensionless input quantities. The methods used in this paper are the support vector machine, two-tiered support vector machine, random forest, gradient-boosted trees and long-short term memory. The performance between different models is remarkably similar, with the support vector machine attaining a slightly better accuracy score. The similarity could indicate issues with the dataset, but further study is required to confirm this. Both the two-tiered model and long-short term memory performed below expectations. The former could be attributed to an implementation which did not allow error propagation between tiers. The latter could be attributed to high noise and low frequency of the input signals. Dimensionless models experienced an expected decrease in performance, caused by a loss of information in the conversion. However, random forest and gradient boosted trees experienced a significantly lower decrease, making them more suitable for dimensionless predictors. From the disruption detection times, it was concluded that several disruptions could be predicted at more than 600ms in advance. A feature importance study using the random forest indicated the negative impact of high noise and missing data in the database, suggesting improvements in data preparation for future work and the potential reevaluation of some of the selected portable features due to poor performance.

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Implementation of a new Disruption Mitigation System into the control system of JET

Cited by 6 publications

References 4 publications

JET and COMPASS asymmetrical disruptions

JET and COMPASS asymmetrical disruptions

Operation of JET with an ITER-like Wall

Investigation of Machine Learning Techniques for Disruption Prediction Using JET Data

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