EAST discharge prediction without integrating simulation results

TL;DR

This paper presents a data-driven model for predicting various plasma parameters in EAST tokamak discharges without using simulation data, achieving over 90% similarity with experimental results and aiding experimental planning.

Contribution

The work introduces a novel purely data-driven prediction model for tokamak plasma parameters, eliminating the need for simulation data integration.

Findings

Achieved over 90% similarity in key diagnostic signals.

Can predict multiple plasma parameters accurately.

Assists in experimental planning by checking signal consistency.

Abstract

In this work, a purely data-driven discharge prediction model was developed and tested without integrating any data or results from simulations. The model was developed based on the experimental data from the Experimental Advanced Superconducting Tokamak (EAST) campaign 2010-2020 discharges and can predict the actual plasma current $I_{p}$, normalized beta $\beta_{n}$, toroidal beta $\beta_{t}$, beta poloidal $\beta_{p}$, electron density $n_{e}$, store energy $W_{mhd}$, loop voltage $V_{loop}$, elongation at plasma boundary $\kappa$, internal inductance $l_{i}$, q at magnetic axis $q_{0}$, and q at 95% flux surface $q_{95}$. The average similarities of all the selected key diagnostic signals between prediction results and the experimental data are greater than 90%, except for the $V_{loop}$ and $q_{0}$. Before a tokamak experiment, the values of actuator signals are set in the discharge proposal stage, with the model allowing to check the consistency of expected diagnostic signals. The model can give the estimated values of the diagnostic signals to check the reasonableness of the tokamak experimental proposal.