Disruption event characterization and forecasting in tokamaks

Abstract

Disruption prediction and avoidance is a critical need for next-step tokamaks, such as ITER. Disruption Event Characterization and Forecasting (DECAF) research fully automates analysis of tokamak data to determine chains of events that lead to disruptions and to forecast their evolution allowing sufficient time for mitigation or complete avoidance of the disruption. Disruption event chains related to local rotating or global magnetohydrodynamic (MHD) modes and vertical instability are examined with warnings issued for many off-normal physics events, including density limits, plasma dynamics, confinement transitions, and profile variations. Along with Greenwald density limit evaluation, a local radiative island power balance theory is evaluated and compared to the observation of island growth. Automated decomposition and analysis of rotating tearing modes produce physical event chains leading to disruptions. A total MHD state warning model comprised of 15 separate criteria produces a disruption forecast about 180 ms before a standard locked mode detector warning. Single DECAF event analyses have begun on KSTAR, MAST, and NSTX/-U databases with thousands of shot seconds of device operation using from 0.5 to 1 × 106 tested sample times per device. An initial multi-device database comparison illustrates a highly important result that plasma disruptivity does not need to increase as βN increases. Global MHD instabilities, such as resistive wall modes (RWMs), can give the briefest time period of warning before disruption compared to other physics events. In an NSTX database with unstable RWMs, the mode onset, loss of boundary and current control, and disruption event warnings are found in all cases and vertical displacement events are found in 91% of cases. An initial time-dependent reduced physics model of kinetic RWM stabilization created to forecast the disruption chain predicts instability 84% of the time for experimentally unstable cases with a relatively low false positive rate. Instances of the disruption event chain analysis illustrate dynamics including H–L back transitions for rotating MHD and global RWM triggering events. Disruption warnings are issued with sufficient time before the disruption (on transport timescales) to potentially allow active profile control for disruption avoidance, active mode control, or mitigation.

Density limits as disruption forecasters for spherical tokamaks

“…It should be noted that disruptivity diagrams can be misleading, because they indicate the parameter space where the disruption finally occurs, not the point at which an event preceding the disruption where trouble begins occurs [20,22]. However, in the particular case of density limits in NSTX and MAST the results should not be too misleading because density tended to steadily increase in the discharges (see figure 1).…”

“…Of course, the warning level for GWL could be set to a Greenwald fraction below one, which would provide more time, but would limit plasmas to lower densities. Additionally, the strategy of DECAF [20] is to develop disruption forecasters which could provide earlier warning than just using stringent criterion. Rampdown density limit disruptions present a challenge because generally it is more difficult to decrease plasma density in a controlled manner than to increase it.…”

“…While the GWL event, which depends on having measured n e , I p , and a only was straightforward to calculate for large databases, as shown in sections 2 and 3, local limits may not be. Therefore the density event for NSTX was further tested with a limited set of 12 discharges, and an illustrative example was shown in [20]. Discharges were selected that had a long flat-top period of rising density and a period of no MHD activity until low frequency n = 1 activity appears with a clean signature in the spectrogram, that lasts less than 200 ms, and then the discharge terminates.…”

“…Localized tearing mode locking can often be one of the last events before disruption [30] or beta collapse [59] in tokamaks, but locked mode detectors may not give sufficient warning to avoid disruptions. Physics-based models of MHD mode slowing which have the potential for earlier warning of impending disruption have now been implemented in DECAF [20,[22][23][24][25]. One potential complication, however, is that there is some evidence that these models of mode locking may not work optimally when the plasma is already near the density limit [60].…”

“…The second strategy, which is more aligned with the capabilities of the disruption event characterization and forecasting (DECAF TM ) code [20]. DECAF is used for the detection of known dangerous event chains in tokamaks [21][22][23][24][25], and is already carrying out statistical characterizations of current tokamak databases to identify common chains of events which lead to disruption.…”