Using data-driven methodology, we exploit the time series of relevant plasma parameters for a large set of disrupted and non-disrupted discharges to develop a classification algorithm for detecting disruptive phases in shots that eventually disrupt. Comparing the same methodology on different devices is crucial in order to have information on the portability of the developed algorithm and the possible extrapolation to ITER. Therefore, we use data from two very different tokamaks, DIII-D and Alcator C-Mod. We focus on a subset of disruption predictors, most of which are dimensionless and/or machine-independent parameters, coming from both plasma diagnostics and equilibrium reconstructions, such as the normalized plasma internal inductance ℓ i and the n=1 mode amplitude normalized to the toroidal magnetic field. Using such dimensionless indicators facilitates a more direct comparison between DIII-D and C-Mod. We then choose a shallow Machine Learning technique, called Random Forests, to explore the databases available for the two devices. We show results from the classification task, where we introduce a time dependency through the definition of class labels on the basis of the elapsed time before the disruption (i.e. 'far from a disruption' and 'close to a disruption'). The performances of the different Random Forest classifiers are discussed in terms of several metrics, by showing the number of successfully detected samples, as well as the misclassifications. The overall model accuracies are above 97% when identifying a 'far from disruption' and a 'disruptive' phase for disrupted discharges. Nevertheless, the Forests are intrinsically different in their capability of predicting a disruptive behavior, with C-Mod predictions comparable to random guesses. Indeed, we show that C-Mod recall index, i.e. the sensitivity to a disruptive behavior, is as low as 0.47, while DIII-D recall is ∼0.72. The portability of the developed algorithm is also tested across the two devices, by using DIII-D data for training the forests and C-Mod for testing and vice versa.

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Abstract. A finite-state off-normal and fault response (ONFR) system is presented that provides the supervisory logic for comprehensive disruption avoidance and machine protection in tokamaks. Robust event handling is critical for ITER and future large tokamaks, where plasma parameters will necessarily approach stability limits and many systems will operate near their engineering limits. Events can be classified as off-normal plasmas events, e.g. neoclassical tearing modes or vertical displacements events, or faults, e.g. coil power supply failures. The ONFR system presented provides four critical features of a robust event handling system: sequential responses to cascading events, event recovery, simultaneous handling of multiple events and actuator prioritization. The finite-state logic is implemented in Matlab R /Stateflow R to allow rapid development and testing in an easily understood graphical format before automated export to the real-time plasma control system code. Experimental demonstrations of the ONFR algorithm on the DIII-D and KSTAR tokamaks are presented. In the most complex demonstration, the ONFR algorithm asynchronously applies "catch and subdue" electron cyclotron current drive (ECCD) injection scheme to suppress a virulent 2/1 neoclassical tearing mode, subsequently shuts down ECCD for machine protection when the plasma becomes over-dense, and enables rotating 3D field entrainment of the ensuing locked mode to allow a safe rampdown, all in the same discharge without user intervention. When multiple ONFR states are active simultaneously and requesting the same actuator (e.g. neutral beam injection or gyrotrons), actuator prioritization is accomplished by sorting the pre-assigned priority values of each active ONFR state and giving complete control of the actuator to the state with highest priority. This early experience makes evident that additional research is required to develop an improved actuator sharing protocol, as well as a methodology to minimize the number and topological complexity of states as the finite-state ONFR system is scaled to a large, highly constrained device like ITER.

As the yield on implosion shots increases it is expected that the peak x-ray emission reduces to a duration with a FWHM as short as 20 ps for ∼7 × 10(18) neutron yield. However, the temporal resolution of currently used gated x-ray imagers on the NIF is 40-100 ps. We discuss the benefits of the higher temporal resolution for the NIF and present performance measurements for dilation x-ray imager, which utilizes pulse-dilation technology [T. J. Hilsabeck et al., Rev. Sci. Instrum. 81, 10E317 (2010)] to achieve x-ray imaging with temporal gate times below 10 ps. The measurements were conducted using the COMET laser, which is part of the Jupiter Laser Facility at the Lawrence Livermore National Laboratory.

Novel disruption prevention solutions spanning a range of control regimes are being developed and tested on DIII-D to enable ITER success. First, a new real-time control algorithm has been developed and tested for regulating nearness to stability limits and maintaining safety-margins. Its first application has been for reliable prevention of vertical displacement events (VDEs) by adjusting plasma elongation (κ) and the inner-gap between the plasma and inner-wall in response to real-time open-loop VDE growth rate (γ) estimators. VDEs were robustly prevented up to average open-loop growth rates of 800 rad s−1 with initial tunings, with only applying shape modification when near safety limits. Second, the disruption risk during fast, emergency shutdown after large tearing and locked modes can be significantly improved by transitioning to a limited topology during shutdown. More than 50% of emergency limited shutdowns after locked modes reach a final normalized current I N < 0.3 before terminating, scaling to the 3 MA ITER requirement. This is in contrast to diverted shutdowns, the majority of which disrupt at I N > 0.8. Despite improvements, these results highlight the critical importance of early prevention. Third, a novel emergency shut down method has been developed which excites instabilities to form a warm, helical core post-thermal quench. The current quench extends to ∼100 ms and avoids VDEs and runaway electron generation. Novel real-time machine learning disruption prediction has been integrated with the DIII-D proximity controller, and a real-time compatible multi-mode MHD spectroscopy technique has been developed. Results presented here were enabled by a focused effort, the disruption free protocol, in DIII-D’s 2019–20 campaign to complement disruption prevention experiments with a large piggy-back program. In addition to testing novel techniques, it is estimated to have helped avoid 32 potential disruptions in piggyback operations with rapid, early shutdowns after large rotating n = 1 or locked modes.