Prediction of Radiative Collapse in Large Helical Device Using Feature Extraction by Exhaustive Search
Prediction of Radiative Collapse in Large Helical Device Using Feature Extraction by Exhaustive Search
复制标题
利用穷举搜索特征提取预测大型螺旋装置的辐射塌陷
DOI：
10.1007/s10894-020-00272-3
复制
发表时间：
2020
影响因子：
1.1
通讯作者：
T. Oishi
中科院分区：
文献类型：
--
作者：
T. Yokoyama;H. Yamada;S. Masuzaki;J. Miyazawa;K. Mukai;B.J. Peterson;N. Tamura;R. Sakamoto;G. Motojima;K. Ida;M. Goto;T. Oishi
利用机器学习技术建立了仿星-日射等离子体的辐射坍缩预测模型，并利用稀疏建模提取了辐射坍缩特征。用于训练模型的数据集是基于大型螺旋装置中的密度递增实验构建的。通过特征提取，选择CIV和OV的线平均电子密度、可见线发射以及边缘电子温度作为辐射坍缩的关键参数。利用这些参数对辐射塌陷发生的可能性进行了量化，并根据辐射塌陷发生的预测能力对这种可能性进行了评估。坍缩可能性还暗示了辐射坍缩的潜在物理性质，因此，通过这项数据驱动的研究获得的知识有望有助于阐明辐射坍缩的物理性质。在对数据集之外的放电进行验证时，基于可能性的预测器平均在该事件发生100毫秒之前预测了85%以上的辐射坍缩，而约5%的稳定放电被错误地检测为坍缩放电。讨论了预测器发生故障的放电情况，探讨了故障原因。
A predictor model of radiative collapse of stellarator-heliotron plasmas has been developed by means of a machine learning technique and the feature of radiative collapse has been extracted with sparse modeling. The dataset used for training the model is constructed based on density ramp-up experiments in the Large Helical Device. As a result of feature extraction, the line averaged electron density, visible line emissions of CIV and OV, and the electron temperature at the edge have been selected as key parameters of radiative collapse. The likelihood of occurrence of radiative collapse has been quantified by using these parameters and this likelihood has been assessed in terms of predicting capability of the occurrence of radiative collapse. The collapse likelihood also implies the underlying physics of radiative collapse, therefore, the knowledge obtained by this data-driven study is expected to facilitate elucidation of the physics of the radiative collapse. In validation with discharges outside of the dataset, the predictor based on the likelihood has predicted over 85% of radiative collapse about 100 ms prior to this event on average while about 5% of stable discharges have been detected falsely as collapse discharges. The discharges in which the predictor made faults are discussed in order to investigate the cause of failure.