Rotating MHD analysis for disruption event characterization and forecasting

Steady plasma operation in reactor-scale tokamaks such as ITER can be maintained through disruption forecasting and avoidance tools. Automated identification of events relevant to disruptions is a required step in developing such tools. Significant physical events are the presence of rotating MHD modes inside the plasma. An analysis of their rotational frequency behavior can be used to identify the event chain where they are slowed by resonant field drag mechanisms and lock in the lab reference frame leading in many cases to a disruption. An algorithm has been developed that automatically detects bifurcation of the mode toroidal rotation frequency due to loss of torque balance under resonant braking and mode locking using spectral decomposition. Criteria are examined to determine the severity of the mode state in progressing toward a potential disruption. Low and medium frequency magnetic probes, plasma rotation profiles from charge exchange recombination spectroscopy, and equilibrium quantities including normalized beta and internal inductance are utilized to provide warning levels to a disruption. A variety of mode conditions are examined using these criteria for NSTX-/U and KSTAR plasmas.