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Stochastic modeling of plasma mode forecasting in tokamak

Published online by Cambridge University Press:  11 November 2011

SH. SAADAT ,
M. SALEM ,
M. GHORANNEVISS  and
P. KHORSHID
SH. SAADAT
Affiliation:
Faculty of Science, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran (shsaadat@iauahvaz.ac.ir)
M. SALEM
Affiliation:
Plasma Physics Research Center, Tehran Science & Research Branch, Islamic Azad University, P.O. Box 14665-678, Tehran, Iran
M. GHORANNEVISS
Affiliation:
Plasma Physics Research Center, Tehran Science & Research Branch, Islamic Azad University, P.O. Box 14665-678, Tehran, Iran
P. KHORSHID
Affiliation:
Group Physics, Mashhad Branch, Islamic Azad University, Mashhad, Iran
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Abstract

The structure of magnetohydrodynamic (MHD) modes has always been an interesting study in tokamaks. The mode number of tokamak plasma is the most important parameter, which plays a vital role in MHD instabilities. If it could be predicted, then the time of exerting external fields, such as feedback fields and Resonance Helical Field, could be obtained. Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average are useful models to predict stochastic processes. In this paper, we suggest using ARIMA model to forecast mode number. The ARIMA model shows correct mode number (m = 4) about 0.5 ms in IR-T1 tokamak and equations of Mirnov coil fluctuations are obtained. It is found that the recursive estimates of the ARIMA model parameters change as the plasma mode changes. A discriminator function has been proposed to determine plasma mode based on the recursive estimates of model parameters.

Type
Papers
Information
Journal of Plasma Physics , Volume 78 , Issue 2 , April 2012 , pp. 99 - 104
Copyright
Copyright © Cambridge University Press 2011

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