An Application of Machine Learning for Plasma Current Quench Studies via Synthetic Data Generation

Abstract

Electromagnetic�forces, thermal loads, and radiation loads experienced by the in-vessel components or vacuum vessels at the time of the�tokamak�plasma current�quench (CQ) significantly affect the overall plasma device�s health. Thus the mitigation of plasma CQ is of paramount importance, which requires a proper identification of the disruption precursors. Using new Machine Learning (ML) and Artificial Intelligence (AI) approaches, it is possible to identify disruption precursors; however, such approaches require training the ML models. This training of models requires a massive amount of experimental data, which sometimes may not be available for different tokamaks. This necessitates the need for accurate synthetic disruption data generation presenting different types of the CQ profiles observed experimentally. A novel approach for synthetic CQ data generation, considering the experimental aspect of the CQ profile shape for a wide range of�tokamak�plasma discharges, is designed to train ML/AI models. The trained model results are also elaborated here, which includes identifying current before disruption and classification of CQ profile types in time-space.