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Prediction of plasma rotation velocity and ion temperature profiles in EAST Tokamak using artificial neural network models

文献类型：期刊论文

作者Lin, Zichao2,3; Zhang, Hongming2; Wang, Fudi2; Bae, Cheonho2; Fu, Jia2; Shen, Yongcai4; Dai, Shuyu1; Jin, Yifei2,3; Lu, Dian2,3; Fu, Shengyu2,3
刊名NUCLEAR FUSION
出版日期2024-10-01
卷号64
关键词artificial neural networks ion temperature rotation velocity profile modelling
ISSN号0029-5515
DOI10.1088/1741-4326/ad73e8
通讯作者Zhang, Hongming(hmzhang@ipp.ac.cn) ; Wang, Fudi(fdwang@ipp.ac.cn) ; Lyu, Bo(blu@ipp.ac.cn)
英文摘要Artificial neural network models have been developed to predict rotation velocity and ion temperature profiles on the EAST tokamak based on spectral measurements from the x-ray crystal spectrometer. Both Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models have been employed to infer line-integrated ion temperatures. The predicted results from these two models exhibit a strong correlation with the target values, providing an opportunity for cross-validation to enhance prediction accuracy. Notably, the computational speed of these models has been significantly increased, surpassing traditional methods by over tenfold. Furthermore, the investigation of input data range and error prediction serves as the foundation for future automated calculation process. Finally, CNNs have also been employed to predict line-integrated rotation velocity profiles and inverted ion temperature profiles for their robustness in the training process. It is noted that these algorithms are not restricted to any specific physics model and can be readily adapted to various fusion devices.
WOS关键词SPECTRA
资助项目National Natural Science Foundation of China[12175278] ; National Natural Science Foundation of China[U23A2077] ; National Natural Science Foundation of China[12205072] ; National Magnetic Confinement Fusion Science Program of China[2019YFE0304002] ; National Magnetic Confinement Fusion Science Program of China[2018YFE0303103] ; Comprehensive Research Facility for Fusion Technology Program of China[2018-000052-73-01-001228] ; University Synergy Innovation Program of Anhui Province[GXXT2021-029] ; Major Science and Technology Infrastructure Maintenance and Reconstruction Projects of the Chinese Academy of Sciences
WOS研究方向Physics
语种英语
WOS记录号WOS:001311896200001
出版者IOP Publishing Ltd
资助机构National Natural Science Foundation of China ; National Magnetic Confinement Fusion Science Program of China ; Comprehensive Research Facility for Fusion Technology Program of China ; University Synergy Innovation Program of Anhui Province ; Major Science and Technology Infrastructure Maintenance and Reconstruction Projects of the Chinese Academy of Sciences
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/135278]  
专题中国科学院合肥物质科学研究院
通讯作者Zhang, Hongming; Wang, Fudi; Lyu, Bo
作者单位1.Dalian Univ Technol, Sch Phys, Key Lab Mat Modificat Laser Ion & Electron Beams, Minist Educ, Dalian 116024, Peoples R China
2.Chinese Acad Sci, Inst Plasma Phys, HFIPS, Hefei 230031, Peoples R China
3.Grad Sch Univ Sci & Technol China, Sci Isl Branch, Hefei 230026, Peoples R China
4.Hefei Normal Univ, Sch Phys & Mat Engn, Hefei 230601, Peoples R China
推荐引用方式
GB/T 7714		 Lin, Zichao,Zhang, Hongming,Wang, Fudi,et al. Prediction of plasma rotation velocity and ion temperature profiles in EAST Tokamak using artificial neural network models[J]. NUCLEAR FUSION,2024,64.
APA Lin, Zichao.,Zhang, Hongming.,Wang, Fudi.,Bae, Cheonho.,Fu, Jia.,...&Lyu, Bo.(2024).Prediction of plasma rotation velocity and ion temperature profiles in EAST Tokamak using artificial neural network models.NUCLEAR FUSION,64.
MLA Lin, Zichao,et al."Prediction of plasma rotation velocity and ion temperature profiles in EAST Tokamak using artificial neural network models".NUCLEAR FUSION 64(2024).

入库方式： OAI收割

来源：合肥物质科学研究院

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