Disruption prediction investigations using Machine Learning tools on DIII-D and Alcator C-Mod. (18th June 2018)

• Record Type: Journal Article
• Authors: Rea, C; Granetz, R S; Montes, K; Tinguely, R A; Eidietis, N; Hanson, J M; Sammuli, B
• Is Part Of: Plasma physics and controlled fusion. Volume 60:Number 8(2018:Aug.)
• Publication Date: 2018-06-18
• Subjects: cross-device study -- Machine Learning -- disruptions; Plasma (Ionized gases) -- Periodicals; Controlled fusion -- Periodicals; 530.44
• DOI: 10.1088/1361-6587/aac7fe
• Languages: English
• ISSNs: 0741-3335

Abstract

Abstract: 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.