Abstract
Disruptions in tokamaks are instabilities events which can damage the machine components. The avoidance and mitigation of these events is desirable in present machines as well as in Next Step devices (such as ITER). A neural network has been developed to predict the occurrence of disruptions caused by edge cooling mechanisms in ASDEX Upgrade. The network works reliably and is able to predict the majority (85%) of the disruptions. The neural network has been trained to predict the time interval up to the disruption and this makes it suitable to be used on-line either to avoid disruptions (by means of auxiliary heating and reduction of gas puffing) or to mitigate the unavoidable ones. For this last purpose, a solid pellet injector has been developed and tested; the injected impurity pellets have been shown to reduce the vertical forces and the conductive fluxes to the divertor.
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