Prediction of multifaceted asymmetric radiation from the edge movement in density-limit disruptive plasmas on Experimental Advanced Superconducting Tokamak using random forest  

作　　者：胡文慧 侯吉磊 罗正平 黄耀 陈大龙 肖炳甲 袁旗平 段艳敏 胡建生 左桂忠 李建刚 Wenhui Hu;Jilei Hou;Zhengping Luo;Yao Huang;Dalong Chen;Bingjia Xiao;Qiping Yuan;Yanmin Duan;Jiansheng Hu;Guizhong Zuo;Jiangang Li(Institute of Plasma Physics,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,China;University of Science&Technology of China,Hefei 230026,China;Key Laboratory of Photovoltaic and Energy Conservation Materials,Chinese Academy of Sciences,Hefei 230031,China)

机构地区：[1]Institute of Plasma Physics,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,China [2]University of Science&Technology of China,Hefei 230026,China [3]Key Laboratory of Photovoltaic and Energy Conservation Materials,Chinese Academy of Sciences,Hefei 230031,China

出　　处：《Chinese Physics B》2023年第7期78-87,共10页中国物理B（英文版）

基　　金：This work is supported by the National MCF Energy R&D Program of China(Grant Nos.2018YFE0302100 and 2019YFE03010003);the National Natural Science Foundation of China(Grant Nos.12005264,12105322,and 12075285);the National Magnetic Confinement Fusion Science Program of China(Grant No.2022YFE03100003);the Natural Science Foundation of Anhui Province of China(Grant No.2108085QA38);the Chinese Postdoctoral Science Found(Grant No.2021000278);the Presidential Foundation of Hefei institutes of Physical Science(Grant No.YZJJ2021QN12).

摘　　要：Multifaceted asymmetric radiation from the edge(MARFE) movement which can cause density limit disruption is often encountered during high density operation on many tokamaks. Therefore, identifying and predicting MARFE movement is meaningful to mitigate or avoid density limit disruption for the steady-state high-density plasma operation. A machine learning method named random forest(RF) has been used to predict the MARFE movement based on the density ramp-up experiment in the 2022’s first campaign of Experimental Advanced Superconducting Tokamak(EAST). The RF model shows that besides Greenwald fraction which is the ratio of plasma density and Greenwald density limit, dβp/dt,H98and d Wmhd/dt are relatively important parameters for MARFE-movement prediction. Applying the RF model on test discharges, the test results show that the successful alarm rate for MARFE movement causing density limit disruption reaches ~ 85% with a minimum alarm time of ~ 40 ms and mean alarm time of ~ 700 ms. At the same time, the false alarm rate for non-disruptive and non-density-limit disruptive discharges can be kept below 5%. These results provide a reference to the prediction of MARFE movement in high density plasmas, which can help the avoidance or mitigation of density limit disruption in future fusion reactors.

关 键 词：multifaceted asymmetric radiation from the edge(MARFE)movement prediction random forest machine learning Experimental Advanced Superconducting Tokamak(EAST) 

分 类 号：TL631.24[核科学技术—核技术及应用] TP181[自动化与计算机技术—控制理论与控制工程]