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To support the deployment of DEMO wall protection strategy, the development of comprehensive analyses is essential to understand the implications of transient perturbations on the plasma shape control and on the vertical stability, some of the most critical aspects to be considered in elongated plasmas. Therefore, the design activities of the DEMO limiter structures need the deep understanding of the effects induced by transient plasma perturbations coupled with one of the most severe load conditions occurring in tokamaks, the Vertical Displacement Event (VDE). Since Electromagnetic (EM) loads during VDE phases are among the DEMO limiters design drivers, this study focuses on predictive simulations of the final plasma position and of EM loads following a VDE. For this purpose, a multi-tokamaks study supported by the construction of an inter-machine database containing experimental transient plasma perturbations and VDEs from JET and ASDEX Upgrade (AUG) has been carried out. It aims at the characterization of some transient plasma perturbations that may lead to high control efforts by the vertical stability system in terms of variations of the plasma internal parameters and vertical displacements. Consequently, such experimental transient plasma perturbations have been properly scaled to DEMO reference geometries with different magnetic configurations, to be simulated in terms of plasma dynamical behaviour by means of MAXFEA code. Finally, initial predictive EM loads on DEMO limiter structures will be discussed in the case of VDEs following plasma perturbations.

In this paper, different pattern recognition techniques have been tested in order to implement an automatic tool for disruption classification in a tokamak experiment. The methods considered refer to clustering and classification techniques. In particular, the investigated clustering techniques are self-organizing maps and K-means, while the classification techniques are multi-layer perceptrons, support vector machines, and k- nearest neighbours. Training and testing data have been collected selecting suitable diagnostic signals recorded over 4 years of EFDA-JET experiments. Multi-layer perceptron classifiers exhibited the best performance in classifying mode lock, density limit/high radiated power, H-mode/L-mode transition and internal transport barrier plasma disruptions. This classification performance can be increased using multiple classifiers. In particular the outputs of five multi-layer perceptron classifiers have been combined using multiple classifier techniques in order to obtain a more robust and reliable classification tool, that is presently implemented at JET.

Disruptions remain the biggest threat to the safe operation of tokamaks. To efficiently mitigate the negative effects, it is now considered important not only to predict their occurrence but also to be able to determine, with high probability, the type of disruption about to occur. This paper reports the results obtained using the nonlinear generative topographic map manifold learning technique for the automatic classification of disruption types. It has been tested using an extensive database of JET discharges selected from JET campaigns from C15 (year 2005) up to C27 (year 2009). The success rate of the classification is extremely high, sometimes reaching 100%, and therefore the prospects for the deployment of this tool in real time are very promising.