Advancing Tokamak Disruption Event Characterization and Forecasting Research and Expansion of Real-time Application on KSTAR

Disruption event characterization and forecasting (DECAF**) research determines the relation of events leading to disruption providing event onset forecasts with high accuracy and sufficiently early warning to allow disruption avoidance [1]. Real-time application of DECAF was made on the KSTAR superconducting tokamak including initial connection to feed-forward control actuators producing over 50 plasma shots with nearly equal disrupted / non-disrupted cases that were forecast with 100% accuracy. A multi-device study conducted for plasma vertical instability produced real-time capable modelling with prediction accuracy of 100% for KSTAR. High bandwidth T_e profile measurements are now used to reconstruct real-time capable ‘crash profiles’ to computationally identify sawteeth, ELMs, and more global MHD as NTM triggers and as direct disruption precursors. With highly successful prediction performance numbers established both in database analysis and real-time, hardware and software for real-time diagnostic acquisition and associated DECAF analysis continue to advance. Real-time electron temperature, T_e, profiles from electron cyclotron emission (ECE) are being included in new real-time DECAF modules being installed. The new real-time velocity profile diagnostic measured data during the 2023 run campaign and aims to support real-time DECAF modules in the 2024 run. Research also advances past feed-forward control by connecting DECAF Events in feedback control for disruption avoidance on KSTAR. **U.S. and international patents pending.