Experiment data-driven modeling of tokamak discharge in EAST

Abstract

A model for tokamak discharge through deep learning has been done on a superconducting long-pulse tokamak (EAST). This model can use the control signals (i.e. Neutral Beam Injection (NBI), Ion Cyclotron Resonance Heating (ICRH), etc) to model normal discharge without the need for doing real experiments. By using the data-driven methodology, we exploit the temporal sequence of control signals for a large set of EAST discharges to develop a deep learning model for modeling discharge diagnostic signals, such as electron density $n_e$, store energy $W_{mhd}$ and loop voltage $V_{loop}$. Comparing the similar methodology, we use Machine Learning techniques to develop the data-driven model for discharge modeling rather than disruption prediction. Up to 95% similarity was achieved for $W_{mhd}$. The first try showed promising results for modeling of tokamak discharge by using the data-driven methodology. The data-driven methodology provides an alternative to physical-driven modeling for tokamak discharge modeling.

arXiv comment19 pages, 7 figures

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Cited by in corpus (5)

• 2022 Review of Data-Driven Plasma Science
• A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks
• EAST discharge prediction without integrating simulation results
• A data management system for machine learning research of tokamak
• Hyperparameter Optimization in the Estimation of PDE and Delay-PDE models from data