首页|Reconstruction of poloidal magnetic field profiles in field-reversed configurations with machine learning in laser-driven ion-beam trace probe
Reconstruction of poloidal magnetic field profiles in field-reversed configurations with machine learning in laser-driven ion-beam trace probe
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The diagnostic of poloidal magnetic field(Bp)in field-reversed configuration(FRC),promising for achieving efficient plasma confinement due to its high β,is a huge challenge because Bp is small and reverses around the core region.The laser-driven ion-beam trace probe(LITP)has been proven to diagnose the Bp profile in FRCs recently,whereas the existing iterative reconstruction approach cannot handle the measurement errors well.In this work,the machine learning approach,a fast-growing and powerful technology in automation and control,is applied to Bp reconstruction in FRCs based on LITP principles and it has a better performance than the previous approach.The machine learning approach achieves a more accurate reconstruction of Bp profile when 20％detector errors are considered,15％Bp fluctuation is introduced and the size of the detector is remarkably reduced.Therefore,machine learning could be a powerful support for LITP diagnosis of the magnetic field in magnetic confinement fusion devices.
FRCLITPpoloidal magnetic field diagnosticsmachine learning
徐栩涛、徐田超、肖池阶、张祖煜、何任川、袁瑞鑫、许平
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State Key Laboratory of Nuclear Physics and Technology,School of Physics,Peking University,Beijing 100871,People's Republic of China
School of Physics,Xihua University,Chengdu 610039,People's Republic of China
National MCF Energy Research and Development Program of China国家自然科学基金
NETL
NSTL
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