Stochastic modeling of plasma mode forecasting in tokamak

Abstract

AbstractThe 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.