Disruptions are a major operational concern for next generation tokamaks, including ITER. They may generate excessive heat loads on plasma facing components, large electromagnetic forces in the machine structures and several MA of multi-MeV runaway electrons. A more complete understanding of the runaway generation processes and methods to suppress them is necessary to ensure safe and reliable operation of future tokamaks. Runaway electrons were studied at JET-ILW showing that their generation dependencies (accelerating electric field, avalanche critical field, toroidal field, MHD fluctuations) are in agreement with current theories.

The impact of disruptions in JET became even more important with the replacement of the previous Carbon Fiber Composite (CFC) wall with a more fragile full metal ITER-like wall (ILW). The development of robust disruption mitigation systems is crucial for JET (and also for ITER). Moreover, a reliable real-time (RT) disruption predictor is a pre-requisite to any mitigation method. The Advance Predictor Of DISruptions (APODIS) has been installed in the JET Real-Time Data Network (RTDN) for the RT recognition of disruptions. The predictor operates with the new ILW but it has been trained only with discharges belonging to campaigns with the CFC wall. 7 real-time signals are used to characterize the plasma status (disruptive or non-disruptive) at regular intervals of 1ms. After the first 3 JET ILW campaigns (991 discharges), the success rate of the predictor is 98.36% (alarms are triggered in average 426ms before the disruptions). The false alarm and missed alarm rates are 0.92% and 1.64%. IntroductIonDue to the complex and highly non-linear coupling of events that lead to a disruption, predictive physics-driven models of these phenomena have not been established from theoretical considerations so far. As an alternative, data-driven models allow the estimation of useful relationships among several quantities to recognize the signature of an incoming disruption.As mentioned in the abstract, the impact of disruptions in JET is a more serious issue with the ILW [1]. This article shows the results of a disruption predictor at JET, APODIS, that has been in operation in the JET RTDN during the three initial ILW campaigns (C28-C30, between August 2011 and July 2012). The objective has been to assess its prediction capabilities (success rate, missed alarms, false alarms and prediction times) for later use in next campaigns as trigger for mitigation actions. In the above ILW campaigns, the alarm generated by APODIS has been distributed through the RTDN and recorded for off-line analysis, but it has not been used to close any feedback loop.

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Since the installation of an ITER-like wall, the JET programme has focused on the consolidation of ITER design choices and the preparation for ITER operation, with a specific emphasis given to the bulk tungsten melt experiment, which has been crucial for the final decision on the material choice for the day-one tungsten divertor in ITER. Integrated scenarios have been progressed with the re-establishment of long-pulse, high-confinement H-modes by optimizing the magnetic configuration and the use of ICRH to avoid tungsten impurity accumulation. Stationary discharges with detached divertor conditions and small edge localized modes have been demonstrated by nitrogen seeding. The differences in confinement and pedestal behaviour before and after the ITER-like wall installation have been better characterized towards the development of high fusion yield scenarios in DT. Post-mortem analyses of the plasma-facing components have confirmed the previously reported low fuel retention obtained by gas balance and shown that the pattern of deposition within the divertor has changed significantly with respect to the JET carbon wall campaigns due to the absence of thermally activated chemical erosion of beryllium in contrast to carbon. Transport to remote areas is almost absent and two orders of magnitude less material is found in the divertor.