Laboratory observation of electron energy distribution near three-dimensional magnetic nulls

The acceleration of electrons near three-dimensional(3D)magnetic nulls is crucial to the energy conversion mechanism in the 3D magnetic reconnection process.To explore electron acceleration in a 3D magnetic null topology,we constructed a pair of 3D magnetic nulls in the PKU Plasma Test(PPT)device and observed acceleration of electrons near magnetic nulls.This study measured the plasma floating potential and ion density profiles around the 3D magnetic null.The potential wells near nulls may be related to the energy variations of electrons,so we measured the electron distribution functions(EDFs)at different spatial positions.The axial variation of EDF shows that the electrons deviate from the Maxwell distribution near magnetic nulls.With scanning probes that can directionally measure and theoretically analyze based on curve fitting,the variations of EDFs are linked to the changes of plasma potential under 3D magnetic null topology.The kinetic energy of electrons accelerated by the electric field is 6 eV(v_(e)~7v_(Alfvén-e))and the scale of the region where accelerating electrons exist is in the order of serval electron skin depths.

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

The diagnostic of poloidal magnetic field(B_(p))in field-reversed configuration(FRC),promising for achieving efficient plasma confinement due to its highβ,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.

Inward particle transport driven by biased endplate in a cylindrical magnetized plasma

The inward particle transport is associated with the formation of peaked density profiles,which contributes to improve the fusion rate and the realization of steady-state discharge.The active control of inward particle transport is considered as one of the most critical issues of magnetic confinement fusion.Recently,it is realized preliminarily by adding a biased endplate in the Peking University Plasma Test(PPT)device.The results reveal that the inward particle flux increases with the bias voltage of the endplate.It is also found that the profile of radial electric field(Er)shear is flattened by the increased bias voltage.Radial velocity fluctuations affect the inward particle more than density fluctuations,and the frequency of the dominant mode driving inward particle flux increases with the biased voltage applied to the endplate.The experimental results in the PPT device provide a method to actively control the inward particle flux using a biased endplate and enrich the understanding of the relationship between E_(r)×B shear and turbulence transport.

Relationship of mode transitions and standing waves in helicon plasmas

Helicon wave plasma sources have the well-known advantages of high efficiency and high plasma density, with broad applications in many areas. The crucial mechanism lies with mode transitions, which has been an outstanding issue for years. We have built a fluid simulation model and further developed the Peking University Helicon Discharge code. The mode transitions, also known as density jumps, of a single-loop antenna discharge are reproduced in simulations for the first time. It is found that large-amplitude standing helicon waves(SHWs) are responsible for the mode transitions, similar to those of a resonant cavity for laser generation.This paper intends to give a complete and quantitative SHW resonance theory to explain the relationship of the mode transitions and the SHWs. The SHW resonance theory reasonably explains several key questions in helicon plasmas, such as mode transition and efficient power absorption, and helps to improve future plasma generation methods.

Experimental observation of the transport induced by ion Bernstein waves near the separatrix of magnetic nulls

The waves in a magnetic null could play important roles during 3D magnetic reconnection.Some preliminary clues in this paper show that the ion Bernstein wave(IBW)may be closely related to transport process in magnetic null region.The magnetic null configuration experiment reported here is set up in a linear helicon plasma device,Peking University plasma test device(PPT).The wave modes with frequencies between the first and third harmonics of local ion cyclotron frequency(w_(ci))are observed in the separatrix of magnetic null,which are identified as the IBW based on the dispersion relation.Further analysis shows that IBW could drive substantial particle flux across the magnetic separatrix.The theoretical radial particle flux driven by IBW and the measured parallel flow in PPT device are almost on the same order,which shows that IBW may play an important role during 3D reconnection process.