Disruptions are sudden and unavoidable losses of confinement that may put at risk the integrity of a tokamak. However, the physical phenomena leading to disruptions are very complex and non-linear and therefore no satisfactory model has been devised so far either for their avoidance or their prediction. For this reason, machine learning techniques have been extensively pursued in the last years. In this paper a real-time predictor specifically developed for JET and based on support vector machines is presented. The main aim of the present investigation is to obtain high recognition rates in a real-time simulated environment. To this end the predictor has been tested on the time slices of entire discharges exactly as in real world operation. Since the year 2000, the experiments at JET have been organized in campaigns named sequentially beginning with campaign C1. In this paper results from campaign C1 (year 2000) and up to C19 (year 2007) are reported. The predictor has been trained with data from JET's campaigns up to C7 with particular attention to reducing the number of missed alarms, which are less than 1%, for a test set of discharges from the same campaigns used for the training. The false alarms plus premature alarms are of the order of 6.4%, for a total success rate of more than 92%. The robustness of the predictor has been proven by testing it with a wide subset of shots of more recent campaigns (from C8 to C19) without any retraining. The success rate over the period between C8 and C14 is on average 88% and never falls below 82%, confirming the good generalization capabilities of the developed technique. After C14, significant modifications were implemented on JET and its diagnostics and consequently the success rates of the predictor between C15 and C19 decays to an average of 79%. Finally, the performance of the developed detection system has been compared with the predictions of the JET protection system (JPS). The new predictor clearly outperforms JPS up to about 180 ms before the disruptions.

The first experimental campaign of the TJ-II stellarator has been conducted using electron cyclotron resonance heating (, ) with a pulse length of . The flexibility of the device has been used to study five different configurations varying plasma volume and rotational transform. In this paper, the main results of this campaign are presented and, in particular, the influence of plasma-wall interaction phenomena on TJ-II confinement is briefly discussed.

This paper presents the latest results on confinement studies in the TJ-II stellarator. The inherently strong plasma–wall interaction of TJ-II has been successfully reduced after lithium coating by vacuum evaporation. Besides H retention and low Z, Li was chosen because there exists a reactor-oriented interest in this element, thus giving special relevance to the investigation of its properties. The Li-coating has led to important changes in plasma performance. Particularly, the effective density limit in NBI plasmas has been extended reaching central values of 8 × 10¹⁹ m⁻³ and T _e ≈ 250–300 eV, with peaked density, rather flat T _e profiles and higher ion temperatures. Due to the achieved density control, a second type of transition has been added to the low density ones previously observed in ECRH plasmas: higher density transitions characterized by the fall in Hα emission, the onset of steep density gradient and the reduction in the turbulence; which are characteristics of transition to the H mode. Confinement studies in ECH plasmas indicate that lowest order magnetic resonances, even in a low shear environment, locally reduce the effective electron heat diffusivities, while Alfven eigenmodes destabilized in NBI plasmas can influence fast ion confinement.

In order to preserve the integrity of large tokamaks such as ITER, the number of disruptions has to be limited. JET has operated previously with a low frequency of disruptions (i.e., disruption rate) of 3.4% [P. C. de Vries et al., Nucl. Fusion 51, 053018 (2011)]. The start of operations with the new full-metal ITER-like wall at JET showed a marked rise in the disruption rate to 10%. A full survey was carried out to identify the root causes, the chain-of-events and classifying each disruption, similar to a previous analysis for carbon-wall operations. It showed the improvements made to avoid various disruption classes, but also indicated those disruption types responsible for the enhanced disruption rate. The latter can be mainly attributed to disruptions due to too high core radiation but also due to density control issues and error field locked modes. Detailed technical and physics understanding of disruption causes is essential for devising optimized strategies to avoid or mitigate these events.