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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.

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It is shown that concepts from survival analysis (branch of statistics dealing with various types of time-to-event data) are helpful when trying to quantify and understand the onset of tearing modes in tokamaks. It is argued that a probabilistic event prediction problem should be decomposed into (i) dynamical system evolution and (ii) event hazard function integration. Successful machine learning of a hazard (events per time) function from experimental data is demonstrated. The hazard function exhibits statistical properties that are consistent with expectation. A specific tearing delta-prime proxy is found to not contribute to the likelihood of the hazard function for the present case. Although in this paper the event is the onset of a tearing mode in a particular plasma scenario, these ideas should be equally applicable to disruption events.