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Special Issue:
SPECIAL TOPIC — Plasma disruption
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| SPECIAL TOPIC—Plasma disruption |
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Prediction of multifaceted asymmetric radiation from the edge movement in density-limit disruptive plasmas on Experimental Advanced Superconducting Tokamak using random forest |
| Wenhui Hu(胡文慧)1, Jilei Hou(侯吉磊)1,†, Zhengping Luo(罗正平)1, Yao Huang(黄耀)1, Dalong Chen(陈大龙)1, Bingjia Xiao(肖炳甲)1,2, Qiping Yuan(袁旗平)1, Yanmin Duan(段艳敏)1, Jiansheng Hu(胡建生)1,3, Guizhong Zuo(左桂忠)1, and Jiangang Li(李建刚)1 |
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 |
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Abstract 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, d$\beta_{\rm p}/$d$ t$, $H_{98}$ and d $W_{\rm mhd}/$d$t$ 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 $\sim 85%$ with a minimum alarm time of $\sim 40 $ ms and mean alarm time of $\sim 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.
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Received: 30 December 2022
Revised: 04 April 2023
Accepted manuscript online: 05 May 2023
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PACS:
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52.55.Fa
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(Tokamaks, spherical tokamaks)
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28.52.-s
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(Fusion reactors)
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84.35.+i
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(Neural networks)
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| Fund: 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), and the Presidential Foundation of Hefei institutes of Physical Science (Grant No. YZJJ2021QN12). |
Corresponding Authors:
Jilei Hou
E-mail: jlhou@ipp.ac.cn
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Cite this article:
Wenhui Hu(胡文慧), Jilei Hou(侯吉磊), Zhengping Luo(罗正平), Yao Huang(黄耀), Dalong Chen(陈大龙),Bingjia Xiao(肖炳甲), Qiping Yuan(袁旗平), Yanmin Duan(段艳敏), Jiansheng Hu(胡建生),Guizhong Zuo(左桂忠), and Jiangang Li(李建刚) Prediction of multifaceted asymmetric radiation from the edge movement in density-limit disruptive plasmas on Experimental Advanced Superconducting Tokamak using random forest 2023 Chin. Phys. B 32 075211
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