Disruption prediction investigations using Machine Learning tools on DIII-D and Alcator C-Mod
Disruption prediction investigations using Machine Learning tools on DIII-D and Alcator C-Mod
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DOI：
10.1088/1361-6587/aac7fe
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发表时间：
2018-06
影响因子：
2.2
通讯作者：
Alcator C-Mod;C. Rea;R. Granetz;K. Montes;R. A. Tinguely;N. Eidietis;J. Hanson;B. Sammuli
中科院分区：
文献类型：
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作者：
Alcator C-Mod;C. Rea;R. Granetz;K. Montes;R. A. Tinguely;N. Eidietis;J. Hanson;B. Sammuli
使用数据驱动的方法，我们利用一个大的一组中断和非中断放电的相关等离子体参数的时间序列，开发一个分类算法，用于检测最终中断的镜头中的中断阶段。为了了解所开发算法的可移植性和可能外推到热核实验堆的情况，在不同设备上比较相同的方法至关重要。因此，我们使用的数据从两个非常不同的托卡马克，DIII-D和Alcator C-Mod。我们专注于中断预测的子集，其中大部分是无量纲和/或机器无关的参数，来自等离子体诊断和平衡重建，如归一化的等离子体内部电感Eki和n = 1模式振幅归一化的环形磁场。使用这样的无量纲指标有助于DIII-D和C-Mod之间更直接的比较。然后，我们选择一种称为随机森林的浅层机器学习技术来探索两种设备可用的数据库。我们展示了分类任务的结果，在那里我们通过定义类标签的基础上中断（即“远离中断”和“接近中断”）之前的时间来引入时间依赖性。不同的随机森林分类器的性能进行了讨论，在几个指标方面，通过显示成功检测到的样本的数量，以及误分类。整体模型的准确性高于97%，当确定一个“远离中断”和“中断”阶段中断放电。然而，森林在预测破坏性行为的能力上有本质的不同，C-Mod预测与随机猜测相当。事实上，我们发现C-Mod回忆指数，即对破坏性行为的敏感性，低至0.47，而DIII-D回忆指数为0.72。所开发的算法的可移植性也在两个设备上进行了测试，通过使用DIII-D数据来训练森林和C-Mod进行测试，反之亦然。
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 ℓi 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.