Reconstruction of poloidal magnetic field profiles in field-reversed configurations with machine learning in laser-driven ion-beam trace probe

【摘要】 The diagnostic of poloidal magnetic field(B_p) in field-reversed configuration(FRC),promising for achieving efficient plasma confinement due to its high β,is a huge challenge because B_p is small and reverses around the core region.The laser-driven ion-beam trace probe(LITP) has been proven to diagnose the B_p 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 B_p 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 B_p profile when 20% detector errors are considered,15% B_p 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.更多还原

【Abstract】 The diagnostic of poloidal magnetic field(B_p) in field-reversed configuration(FRC),promising for achieving efficient plasma confinement due to its high β,is a huge challenge because B_p is small and reverses around the core region.The laser-driven ion-beam trace probe(LITP) has been proven to diagnose the B_p 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 B_p 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 B_p profile when 20% detector errors are considered,15% B_p 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.更多还原