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(EXP3/06) Forecast of TEXT Plasma Disruptions Using Soft X-Rays as Input Signal in a Neural Network

   
A. Vannucci , K. A. Oliveira 
Instituto de Fisica Universidade de Sao Paulo, SP, Brazil
 
T. Tajima 
Institute for Fusion Studies - University of Texas, Austin, TX, USA
 
Y. J. Tajima 
Department of Physics and Astronomy - Swarthmore College - Swarthmore, PA, USA

Abstract
A feed-forward neural network  is used to forecast major and minor disruptions in TEXT  tokamak discharges. Using the experimental data of soft X-ray signals as input data, the neural net is trained with one disruptive plasma discharge, while a different disruptive discharge is used for validation. After proper training, the net works with the same set of weights, it is then used to forecast disruptions in two other different plasma discharges. It is observed that the neural net is capable of predicting the onset of a disruption up to 3.12 ms in advance. From what we observe in the predictive behavior of our network, speculations are made whether the disruption triggering mechanism is associated with an increase in the m = 2 magnetic island, that disturbs the central part of the plasma column afterwards, or the initial perturbation has first occurred in the central part of the plasma column and then the m = 2 MHD mode is destabilized.

   

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