Plasma current profile reconstruction for EAST based on Bayesian inference

被引:10
|
作者
Liu, Zijie [1 ,2 ,3 ]
Luo, Zhengping [2 ]
Wang, Tianbo [3 ]
Huang, Yao [2 ]
Wang, Yuehang [2 ]
Yu, Qingze [2 ]
Yuan, Qiping [2 ]
Xiao, Bingjia [1 ,2 ]
Li, Jiangang [1 ,2 ]
机构
[1] Univ Sci & Technol China, Hefei 230026, Peoples R China
[2] Chinese Acad Sci, Inst Plasma Phys, POB 1126, Hefei 230031, Peoples R China
[3] CNNC, Southwestern Inst Phys, Chengdu 610200, Peoples R China
基金
中国国家自然科学基金;
关键词
Machine learning; Bayesian inference; Plasma equilibrium reconstruction; Tomography; EAST; EQUILIBRIUM RECONSTRUCTION; MHD EQUILIBRIUM; TOKAMAKS;
D O I
10.1016/j.fusengdes.2021.112722
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
Determining the distribution of plasma current in the equilibrium state is one of the most important steps to realize effective and safe operation of tokamak. In this study, a novel reconstruction code based on Bayesian inference is developed to infer the plasma current distribution for experiment analysis of EAST. Without iteratively solving Grad-Shafranov (G-S) equation to find an optimal fit for the external magnetic diagnostic measurements, the distribution of toroidal current density and poloidal flux can be rapidly derived in a probabilistic manner. The reconstructed results are consistent with the results based on equilibrium fitting (EFIT) code, and the execution time is less than 1 ms for each time slice, which indicates its potential for application in future realtime plasma feedback control.
引用
收藏
页数:13
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