Development of an automated neural network approach for classifying MHD mode formation using KSTAR ECEI data for Disruption Event Characterization and Forecasting (DECAF)

Plasma disruptions are often driven by magnetohydrodynamic (MHD) events that lead to the formation of magnetic islands through tearing mode instabilities. Recent studies using the Electron Cyclotron Emission Imaging (ECEI) system have focused on the role of turbulence near magnetic islands in their growth and potential to trigger disruptions [1]. We develop a neural network model to predict whether MHD mode formation is progressing toward disruption events using KSTAR ECEI data. Coherence between vertically adjacent ECEI channels located near the q=2 surface is calculated and mapped onto the poloidal plane. Increased coherence levels are often observed around the (2,1) mode prior to disruption. The model is trained on the spatiotemporal patterns of mapped coherence from various shots exhibiting global electron temperature collapse events. Leveraging the Disruption Event Characterization and Forecast (DECAF) code [2], this approach aims to uncover the physical mechanisms linking rapid electron temperature changes to mode growth, saturation, and evolution, offering new insights into the event chains that can ultimately lead to disruptions. This offline analysis will be used to attempt disruption avoidance techniques utilizing real-time processing of full 2D ECEI images of MHD modes acquired on KSTAR.

[1] M. J. Choi, et al., Nat. Commun. 12, 375 (2021)

[2] S.A. Sabbagh, et al., Phys. Plasmas 30, 032506 (2023)