Disruption prediction investigations using Machine Learning tools on DIII-D and Alcator C-Mod

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.

MHD stability and disruptions in the SPARC tokamak

“…Disruption prediction on Alcator C-Mod has proven challenging (Rea et al. 2018; Montes et al. 2019) due to the high fraction of disruptions caused by molybdenum flecks; an event with an inherent time scale of the order of milliseconds.…”

Characterizing magnetized plasmas with dynamic mode decomposition

“…This method fully characterizes a resistive kink instability on a small time window, indicating the ability for robust data-driven identification of MHD instabilities, with implications for real-time control. These methods, with online training and communication with other machine learning techniques with offline training [51,60,61], could prove useful for disruption mitigation. If both the discovery of interpretable dynamics and the accurate characterization of instabilities is desired, the authors recommend a joint use of both the sparsity-promoting and optimized DMD algorithms, as illustrated here.…”

Advancing Fusion with Machine Learning Research Needs Workshop Report

“…Predictions from machine learning models trained on large data sets have been employed in fusion energy research since the early-1990s. For example, Wroblewski et al [74] employed a neural network to predict high beta disruptions in real-time from many axisymmetric-only input signals, Windsor et al [72] produced a multi-machine applicable disruption predictor for JET and ASDEX-UG, Rea et al [61] and Montes et al [48] demonstrated use of time series data and explicit look-ahead time windows for disruption predictability in Alcator C-Mod, DIII-D, and EAST (see Fig. 11), and Kates-Harbeck [33] demonstrated use of extensive profile measurements in multi-machine disruption prediction for JET and DIII-D with convolutional and recurrent neural networks.…”