Disruption Event Characterization and Forecasting in Tokamaks

High reliability disruption prediction and avoidance are critical needs for next-step tokamaks such as ITER. The Disruption Event Characterization and Forecasting (DECAF) code was written to automate analysis of tokamak data determining chains of events leading to disruptions and to forecast their evolution. This approach provides a flexible framework to evaluate the proximity of plasma states to disruption events by coupled physics analyses, model criteria, and machine learning techniques. An expansive list of events including MHD modes, density limits, off-normal plasma motion, and mismatch of plasma current feedback target are evaluated. The expanding tokamak database includes KSTAR, NSTX/-U, MAST, DIII-D, and TCV. Automated analysis of rotating MHD modes allows identification of coupling, bifurcation, locking, and potential triggering by other MHD activity. Resistive stability including Delta’ calculation by the Resistive DCON code is evaluated on long pulse KSTAR plasmas using kinetic equilibrium reconstructions with magnetic field pitch angle data to determine capability for instability forecasting. Greenwald density fraction and local island power balance theory are evaluated for disruption forecasting. As density increases towards or surpasses empirical / theoretical limits, the onset of MHD activity and subsequent disruption are observed. Insights are also gained connecting mode activity to density limit models by their coupling through plasma rotation. In an NSTX database exhibiting global MHD, resistive wall mode (RWM) and loss of boundary control events are always found and VDE events are found in over 90% of plasmas. A reduced kinetic RWM stability physics model computes the evolving proximity of discharges to marginal stability. Stringent marginal stability evaluation with a non-optimized model shows high success (greater than 85%) as a disruption predictor.