Prediction of density limit disruption boundaries from diagnostic signals using neural networks

Abstract

An attempt is made to make a prediction of the disruption boundaries for the density limit disruption case using a neural network. Using experimental signals as input, the network should, in the long run, be able to provide information to the real time control systems about the density limit at which a discharge is likely to disrupt, so that the density can be kept below that limit. Several diagnostic signals are used from the ADITYA tokamak and arepresented at selected time instants to the neural network inputs, in order to predict, at each of these instants, the density boundary. A disruption threshold has been established in order to examine the possibility of using the network as a real time disruption alarm. For most of the discharges this threshold is reached much before the actual disruption. The neural network is also used to make an optimization of the particular set of diagnostics in order to obtain the ones most crucial for predicting the density limit. The results of optimization have some of the features of the scaling laws of Murakami and Hugill. The optimized network compares well with the original one.