Machine learning application to predict the electron temperature on the J-TEXT tokamak

Jiaolong DONG (董蛟龙); Jianchao LI (李建超); Yonghua DING (丁永华); Xiaoqing ZHANG (张晓卿); Nengchao WANG (王能超); Da LI (李达); Wei YAN (严伟); Chengshuo SHEN (沈呈硕); Ying HE (何莹); Xiehang REN (任颉颃)

doi:10.1088/2058-6272/ac0685

Plasma Science and Technology > 2021 > 23(8): 85101-085101. > DOI: 10.1088/2058-6272/ac0685

Jiaolong DONG (董蛟龙), Jianchao LI (李建超), Yonghua DING (丁永华), Xiaoqing ZHANG (张晓卿), Nengchao WANG (王能超), Da LI (李达), Wei YAN (严伟), Chengshuo SHEN (沈呈硕), Ying HE (何莹), Xiehang REN (任颉颃). Machine learning application to predict the electron temperature on the J-TEXT tokamak[J]. Plasma Science and Technology, 2021, 23(8): 85101-085101. DOI: 10.1088/2058-6272/ac0685

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Machine learning application to predict the electron temperature on the J-TEXT tokamak

¹ International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics, State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
² Hubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology, Wuhan 430205, People's Republic of China

Funds: This work was supported by the National Magnetic Confinement Fusion Science Program (Nos. 2018YFE0301104 and 2018YFE0301100), State Key Laboratory of Advanced Electromagnetic Engineering and Technology (No. AEET2020KF001) and National Natural Science Foundation of China (Nos. 12075096 and 51821005).

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Received Date: March 30, 2021
Revised Date: May 27, 2021
Accepted Date: May 27, 2021

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Abstract

Abstract

The reliability of diagnostic systems in tokamak plasma is of great significance for physics researches or fusion reactor. When some diagnostics fail to detect information about the plasma status, such as electron temperature, they can also be obtained by another method: fitted by other diagnostic signals through machine learning. The paper herein is based on a machine learning method to predict electron temperature, in case the diagnostic systems fail to detect plasma temperature. The fully-connected neural network, utilizing back propagation with two hidden layers, is utilized to estimate plasma electron temperature approximately on the J-TEXT. The input parameters consist of soft x-ray emission intensity, electron density, plasma current, loop voltage, and toroidal magnetic field, while the targets are signals of electron temperature from electron cyclotron emission and x-ray imaging crystal spectrometer. Therefore, the temperature profile is reconstructed by other diagnostic signals, and the average errors are within 5%. In addition, generalized regression neural network can also achieve this function to estimate the temperature profile with similar accuracy. Predicting electron temperature by neural network reveals that machine learning can be used as backup means for plasma information so as to enhance the reliability of diagnostics.
- neural network,
- plasma,
- electron temperature,
- J-TEXT tokamak

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References (23)

References

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Machine learning application to predict the electron temperature on the J-TEXT tokamak