Electron temperature profile evolution analysis in preparation for real-time disruption prediction and avoidance in KSTAR using DECAF

The present talk presents progress in the implementation of real-time detection and categorization of electron temperature collapse (TEC) events in KSTAR. Prior experiments have shown that TEC-triggered disruption chains can be used for early disruption forecasting [1]. Extensive database analysis using the DECAF code [2] identified key TEC causes, including MHD precursors in high- plasmas, field line stochasticity, and asymmetric core cooling from high-Z impurity accumulation. Correctly identifying the dynamics behind each TEC event is critical for robust disruption forecasting, as the planned actuator response varies by event type. Notably, the implemented algorithm is based on the reconstruction of a ‘crash profile’, which contains the features that allow the categorization of the event through a set of self-adapting basis functions that decompose the crash profile into components, allowing to uniquely tie the crash to a physics event. This framework, developed offline in DECAF, is being adapted for real-time implementation within the KSTAR Plasma Control System as part of a multi-event, multi-feedback disruption prediction and avoidance strategy. Initial real-time feasibility studies were carried out during the latest KSTAR campaign, where a performance-degrading TEC event was successfully avoided by reducing ECH power just before onset. This demonstration sets the foundation for full real-time disruption avoidance using TEC detection in the upcoming campaign.

References

[1] S.A. Sabbagh, et al., Phys. Plasmas 30, 032506 (2023); https://doi.org/10.1063/5.0133825

[2] G. Bustos-Ramirez et al, ‘Exploring connections between electron temperature profile evolution and disruptions in KSTAR through DECAF analysis’ iFPC 2024, Seoul, South Korea.