首页|Plasma current tomography for HL-2A based on Bayesian inference

Plasma current tomography for HL-2A based on Bayesian inference

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An accurate plasma current profile has irreplaceable value for the steady-state operation of the plasma.In this study,plasma current tomography based on Bayesian inference is applied to an HL-2A device and used to reconstruct the plasma current profile.Two different Bayesian probability priors are tried,namely the Conditional AutoRegressive(CAR)prior and the Advanced Squared Exponential(ASE)kernel prior.Compared to the CAR prior,the ASE kernel prior adopts non-stationary hyperparameters and introduces the current profile of the reference discharge into the hyperparameters,which can make the shape of the current profile more flexible in space.The results indicate that the ASE prior couples more information,reduces the probability of unreasonable solutions,and achieves higher reconstruction accuracy.

plasma current tomographyBayesian inferencemachine learningGaussian distribution

刘自结、王天博、吴木泉、罗正平、王硕、孙腾飞、肖炳甲、李建刚

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College of Physics and Optoelectronic Engineering,Shenzhen University,Shenzhen 518060,People's Republic of China

Southwestern Institute for Physics,Chengdu 610200,People's Republic of China

Institute of Plasma Physics,Chinese Academy of Sciences,Hefei 230031,People's Republic of China

University of Science and Technology of China,Hefei 230026,People's Republic of China

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National MCF Energy Research and Development Program of ChinaNational MCF Energy Research and Development Program of ChinaNational MCF Energy Research and Development Program of China国家重点研发计划国家重点研发计划国家自然科学基金国家自然科学基金国家自然科学基金

2018 YFE03011052022YFE030100022018YFE03021002022YFE030700042022YFE03070000122051951207515511975277

2024

10.1088/2058-6272/ad1980

等离子体科学和技术(英文版)
中国科学院合肥物质科学研究所 中国力学学会

等离子体科学和技术(英文版)

EI
影响因子：0.297
ISSN：1009-0630
年,卷(期)：2024.26(5)
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