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An advanced plasma current tomography method based on Bayesian inference and neural networks for real-time application

文献类型：期刊论文

作者Liu, Zijie1,2,3; Luo, Zhengping2; Wang, Tianbo3; Huang, Yao2; Wang, Yuehang2; Yu, Qingze2; Rui, Wangyi2; Yuan, Qiping2; Xiao, Bingjia1,2; Li, Jiangang1,2
刊名PLASMA PHYSICS AND CONTROLLED FUSION
出版日期2022-12-01
卷号64
ISSN号0741-3335
关键词plasma current tomography Bayesian inference neural network
DOI10.1088/1361-6587/ac978a
通讯作者Luo, Zhengping(zhengping.luo@ipp.ac.cn) ; Wang, Tianbo(wangtianbo@swip.ac.cn)
英文摘要An advanced plasma current tomography method is established for the Experimental Advanced Superconducting Tokamak (EAST), which combines Bayesian probability theory and neural networks. It is different from the existing current tomography method based on a conditional autoregressive (CAR) prior. Specifically, the CAR prior is replaced with an advanced squared exponential (ASE) kernel function prior. Therefore, the proposed method can overcome the deficiencies of the CAR prior, where the calculated core current is lower than the reference current and the uncertainty becomes severe after introducing noise in the diagnostics. The ASE kernel prior is developed from the squared exponential kernel function by integrating the useful information from the reference discharge. The ASE kernel prior adopts nonstationary hyperparameters and introduces the current profile into the hyperparameters, which can make the shape of the current profile more flexible in space. To provide a suitable reference discharge, a neural network model is also trained. The execution time is less than 1 ms for each time slice, which indicates its potential for application in future real-time plasma feedback control.
WOS关键词EQUILIBRIUM RECONSTRUCTION ; MHD EQUILIBRIUM
资助项目National MCF Energy R&D Program of China[2018YFE0302100] ; National MCF Energy R&D Program of China[2018YFE0301105] ; National Natural Science Foundation of China[11875291] ; National Natural Science Foundation of China[11805236] ; National Natural Science Foundation of China[11905256] ; National Natural Science Foundation of China[12075285]
WOS研究方向Physics
语种英语
出版者IOP Publishing Ltd
WOS记录号WOS:000874229300001
资助机构National MCF Energy R&D Program of China ; National Natural Science Foundation of China
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/129865]  
专题中国科学院合肥物质科学研究院
通讯作者Luo, Zhengping; Wang, Tianbo
作者单位1.Univ Sci & Technol China, Hefei 230031, 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
推荐引用方式
GB/T 7714		 Liu, Zijie,Luo, Zhengping,Wang, Tianbo,et al. An advanced plasma current tomography method based on Bayesian inference and neural networks for real-time application[J]. PLASMA PHYSICS AND CONTROLLED FUSION,2022,64.
APA Liu, Zijie.,Luo, Zhengping.,Wang, Tianbo.,Huang, Yao.,Wang, Yuehang.,...&Li, Jiangang.(2022).An advanced plasma current tomography method based on Bayesian inference and neural networks for real-time application.PLASMA PHYSICS AND CONTROLLED FUSION,64.
MLA Liu, Zijie,et al."An advanced plasma current tomography method based on Bayesian inference and neural networks for real-time application".PLASMA PHYSICS AND CONTROLLED FUSION 64(2022).

入库方式： OAI收割

来源：合肥物质科学研究院

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