Power supply for generating frequency-variable resonant magnetic perturbations on the J-TEXT tokamak

To further research the response of the tearing mode(TM) to dynamic resonant magnetic perturbation(DRMP) on the J-TEXT tokamak, a modified series resonant inverter power supply(MSRIPS) with a function of discrete variable frequency is designed for DRMP coils in this study. The MSRIPS is an AC–DC–AC converter, including a phase-controlled rectifier, an LC filter, an insulated gate bipolar transistor(IGBT) full bridge, a matching transformer, three resonant capacitors with different capacitance values, and three corresponding silicon controlled rectifier(SCR) switches. The function of discrete variable frequency is realized by switching over different resonant capacitors with corresponding SCR switches while matching the corresponding driving frequency of the IGBT full bridge. A detailed switching strategy of the SCR switch is put forward to obtain sinusoidal current waveform and realize current waveform smooth transition during frequency conversion. In addition, a resistor and thyristor bleeder is designed to protect the SCR switch from overvoltage. Manufacturing of the MSRIPS is completed, and the MSRIPS equipment can output current with an amplitude of 1.5 kA when its working frequency jumps among different frequencies. Moreover, the current waveform is sinusoidal and can smoothly transition during frequency conversion. Furthermore, the transition time when the current amplitude rises from zero to a steady state is less than 2 ms during frequency conversion. By using the MSRIPS, the expected discrete variable frequency DRMP is generated, and the phenomenon of the TM being locked to the discrete variable frequency DRMP is observed on the J-TEXT tokamak.     

Overview of machine learning applications in fusion plasma experiments on J-TEXT tokamak

Machine learning research and applications in fusion plasma experiments are one of the main subjects on J-TEXT. Since 2013, various kinds of traditional machine learning, as well as deep learning methods have been applied to fusion plasma experiments. Further applications in the real-time experimental environment have proved the feasibility and effectiveness of the methods. For disruption prediction, we started by predicting disruptions of limited classes with a short warning time that could not meet the requirements of the mitigation system. After years of study, nowadays disruption prediction methods on J-TEXT are able to predict all kinds of disruptions with a high success rate and long enough warning time. Furthermore, cross-device disruption prediction methods have obtained promising results. Interpretable analysis of the models are studied. For diagnostics data processing, efforts have been made to reduce manual work in processing and to increase the robustness of the diagnostic system. Models based on both traditional machine learning and deep learning have been applied to real-time experimental environments. The models have been cooperating with the plasma control system and other systems, to make joint decisions to further support the experiments.     

Machine learning application to predict the electron temperature on the J-TEXT tokamak

The reliability of diagnostic systems in tokamak plasma is of great significance for physics researches or fusion reactor. When some diagnostics fail to detect information about the plasma status, such as electron temperature, they can also be obtained by another method: fitted by other diagnostic signals through machine learning. The paper herein is based on a machine learning method to predict electron temperature, in case the diagnostic systems fail to detect plasma temperature. The fully-connected neural network, utilizing back propagation with two hidden layers, is utilized to estimate plasma electron temperature approximately on the J-TEXT. The input parameters consist of soft x-ray emission intensity, electron density, plasma current, loop voltage, and toroidal magnetic field, while the targets are signals of electron temperature from electron cyclotron emission and x-ray imaging crystal spectrometer. Therefore, the temperature profile is reconstructed by other diagnostic signals, and the average errors are within 5%. In addition, generalized regression neural network can also achieve this function to estimate the temperature profile with similar accuracy. Predicting electron temperature by neural network reveals that machine learning can be used as backup means for plasma information so as to enhance the reliability of diagnostics.     

The timing system on the J-TEXT tokamak

This paper describes the timing system designed to control the operation time-sequence and to generate clocks for various sub-systems on J-TEXT tokamak. The J-TEXT timing system is organized as a distributed system which is connected by a tree-structured optical fiber network. It can generate delayed triggers and gate signals (0 μs–4000 s), while providing reference clocks for other sub-systems. Besides, it provides event handling and timestamping functions. It is integrated into the J-TEXT Control, Data Access and Communication (J-TEXT CODAC) system, and it can be monitored and configured by Experimental Physics and Industrial Control System (EPICS). The configuration of this system including tree-structured network is managed in XML files by dedicated management software. This system has already been deployed on J-TEXT tokamak and it is serving J-TEXT in daily experiments.     

Estimation of magnetic island width by the fluctuations of electron cyclotron emission radiometer on J-TEXT

The width of a magnetic island is an important parameter for the quantitative analysis of magnetohydrodynamic-related physics. An electron cyclotron emission radiometer(ECE) is a powerful tool that can be used to obtain this width, which can usually be determined from the flat temperature distribution at the O-point phase or the maxima temperature perturbation. An improved method to estimate the width of a magnetic island is proposed in this paper, and it is independent of calibration. With this method and the existing 24-channel ECE system, the width of a rotation magnetic island can be estimated. Additionally, by filtering the fluctuation ECE signal, the evolution of the magnetic island can be obtained. The results of this method are consistent with those of the integrated magnetic probe signals, which represent the relative change of the magnetic island.     

Quasi-3D electron cyclotron emission imaging on J-TEXT

Electron cyclotron emission imaging(ECEI) can provide measurements of 2D electron temperature fluctuation with high temporal and spatial resolution in magnetic fusion plasma devices. Two ECEI systems located in different toroidal ports with 67.5 degree separation have been implemented on J-TEXT to study the 3D structure of magnetohydrodynamic(MHD) instabilities. Each system consists of 12(vertical) × 16(horizontal) = 192 channels and the image of the 2nd harmonic X-mode electron cyclotron emission can be captured continuously in the core plasma region. The field curvature adjustment lens concept is developed to control the imaging plane for receiving optics of the ECEI systems. Field curvature of the image can be controlled to match the emission layer. Consequently, a quasi-3D image of the MHD instability in the core of the plasma has been achieved.     

Fast Data Processing of a Polarimeter-Interferometer System on J-TEXT

A method of fast data processing has been developed to rapidly obtain evolution of the electron density profile for a multichannel polarimeter-interferometer system(POLARIS)on J-TEXT. Compared with the Abel inversion method, evolution of the density profile analyzed by this method can quickly offer important information. This method has the advantage of fast calculation speed with the order of ten milliseconds per normal shot and it is capable of processing up to 1 MHz sampled data, which is helpful for studying density sawtooth instability and the disruption between shots. In the duration of a flat-top plasma current of usual ohmic discharges on J-TEXT, shape factor u is ranged from 4 to 5. When the disruption of discharge happens, the density profile becomes peaked and the shape factor u typically decreases to 1.     

Plasma Response to Supersonic Molecular Beam Injection in J-TEXT简

Recently, hydrogen fueling experiments with supersonic molecular beam injection （SMBI） system have been performed in the J-TEXT tokamak. To evaluate the effects of the in- jection amount of SMBI on plasma behaviors, moderate and intensive SMBs have been separately injected and compared with each other in Ohmic discharges. With moderate SMBs, electron den- sity increases about twice as before, the size of magnetic island slightly decreases, and the edge toroidal rotation speed in a counter-current direction, measured by a high resolution spectrometer （Carbon V ion, 227.09 nm, r/a-= 0.7-0.8）, is accelerated from 8 km/s to 12 km/s. The state of higher electron density with moderate SMBI can be maintained for a long period, which indicates that plasma confinement is improved. However, with intensive SMBs, the accompanied magne- tohydrodynamic （MHD） activities are triggered, and the electron density increases moderately. The edge toroidal velocity is decreased, in certain cases even reversed in the co-current direction. The statistical result of experimental data for moderate and intensive SMBs suggests a preferred fueling amount （less than 3.2 ~ 1019） to improve the SMBI fueling efficiency in experiments.     

Implementation of Automatic Process of Edge Rotation Diagnostic System on J-TEXT Tokamak

A spectral diagnostic control system （SDCS） is developed to implement automatic process of the edge rotation diagnostic system on the J-TEXT tokamak. The SDCS contains a control module, data operation module, data analysis module, and data upload module. The core of this system is a newly developed software ＂Spectra Assist＂, which completes the whole process by coupling all related subroutines and servers. The results of data correction and calculated rotation are presented. In the daily discharge of J-TEXT, SDCS is proved to have a stable performance and high efficiency in completing the process of data acquisition, operation and results output.     

Design of the Control System of Toroidal Field （TF） Power Supply for J-TEXT Tokamak

J-TEXT, formerly TEXT-U at the University of Texas at Austin in USA, is a medium-sized tokamak at the Huazhong University of Science and Technology. The toroidal field （TF） power supply of this tokamak should provide a current of 160 kA and a flat duration of 500 ms for the toroidal field coils to generate a maximum toroidal field of 3 T at the geometric center of the vaccum vessel. This paper presents a design of a new control system which takes the real-time feedback control option for the TF power supply operation. The system was tested successfully during the commissioning. In the first experimental campaign of J-TEXT, the system effectively controlled the power supply to provide a fiat current up to 92.5 kA and therein the TF produced reached 1.74 T, which enabled the machine to generate the first plasma successfully.