Disruption Prediction by Support Vector Machine and Neural Network with Exhaustive Search
Disruption Prediction by Support Vector Machine and Neural Network with Exhaustive Search
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DOI：
10.1585/pfr.13.3405021
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发表时间：
2018-04
影响因子：
0.8
通讯作者：
T. Yokoyama;T. Sueyoshi;Y. Miyoshi;R. Hiwatari;Y. Igarashi;M. Okada;Yuichi Ogawa
中科院分区：
文献类型：
--
作者：
T. Yokoyama;T. Sueyoshi;Y. Miyoshi;R. Hiwatari;Y. Igarashi;M. Okada;Yuichi Ogawa
中断是托卡马克反应堆中等离子体电流突然关闭的事件。建立预测、减轻和避免中断的方法对于实现托卡马克反应堆是必不可少的。在本研究中，我们使用了JT-60 U高β实验的大数据集来开发一种预测中断发生的方法。该方法基于稀疏建模，利用所有高维数据共同的固有稀疏性，使我们能够有效地从数据中提取最大量的信息。为了进行稀疏建模，我们使用了支持向量机和神经网络的穷举搜索。在这项研究中，我们在改变血浆参数组合的同时重复了预测器的训练和评估。经过彻底搜查，我们发现|Bn=1 r|和d| Bn=1 r|/dt是中断预测的主要参数。这并不奇怪，因为MHD不稳定性被认为是破坏的直接触发因素。此外，我们还成功地确定了几个可能与中断密切相关的重要参数，即，βN、βP、q95、δ、fGW和frad.
A disruption is an event in which the plasma current suddenly shuts down in a tokamak reactor. Establishing methods to predict, mitigate, and avoid disruptions may be indispensable for realizing a tokamak reactor. In the present study, we have used the large dataset of high-beta experiments at JT-60U to develop a method for predicting the occurrence of disruptions. The method is based on sparse modeling that exploits the inherent sparseness common to all high-dimensional data, and it enables us to extract the maximum amount of information from the data efficiently. To carry out the sparse modeling, we have used exhaustive searches with a support vector machine and a neural network. In this research, we repeated the training and evaluation of the predictor while changing the combination of plasma parameters. As a result of the exhaustive search, we found |Bn=1 r | and d|Bn=1 r |/dt to be the dominant parameters for disruption predictions. This is not surprising, because MHD instabilities are considered to be the direct triggers of disruption. In addition, we have succeeded in identifying several important parameters that may also be strongly related to disruptions, i.e., βN, βP, q95, δ, fGW, and frad.