Abstract:The first comprehensive measurements of plasma flows and fluctuations nearby static magnetic islands driven by resonant magnetic perturbations (RMPs) are presented.These experiments were performed using multiple Langmuir probe arrays in the edge plasmas of the J-TEXT tokamak. The effects of controlled variations of the island size and location are explored. This study aims to understand the interaction between turbulence and magnetic islands, and to elucidate magnetic island effects on edge turbulence and flow intensity profiles, edge electric fields, and thus confinement regime transitions. Turbulence and low frequency flows (LFFs) all drop inside the magnetic island, but increase at its boundary, as island width increases. The geodesic acoustic mode (GAM) is damped in most of the edge area with magnetic islands. The sign of the radial electric field changes from negative to positive within 2 the islands. The gradient of turbulent stresses vanishes at island center, and becomes steeper at the boundary of the islands. The particle transport induced by the turbulence is reduced inside the magnetic islands. The magnetic island effects on flows and turbulence can lead to an increase in LFFs and enhance Reynolds stresses near the last closed flux surface (LCFS). A stronger radial electric field layer can be formed near the LCFS when magnetic islands are present. The results suggest that magnetic islands can be used as a tool to enhance edge turbulence and flows, edge electric fields, and thus to trigger confinement regime transitions.

Increasing the plasma density is one of the key methods in achieving an efficient fusion reaction. High-density operation is one of the hot topics in tokamak plasmas. Density limit disruptions remain an important issue for safe operation. An effective density limit disruption prediction and avoidance system is the key to avoid density limit disruptions for long pulse steady state operations. An artificial neural network has been developed for the prediction of density limit disruptions on the J-TEXT tokamak. The neural network has been improved from a simple multi-layer design to a hybrid two-stage structure. The first stage is a custom network which uses time series diagnostics as inputs to predict plasma density, and the second stage is a three-layer feedforward neural network to predict the probability of density limit disruptions. It is found that hybrid neural network structure, combined with radiation profile information as an input can significantly improve the prediction performance, especially the average warning time (T warn ). In particular, the T warn is eight times better than that in previous work (Wang et al 2016 Plasma Phys. Control. Fusion 58 055014) (from 5 ms to 40 ms). The success rate for density limit disruptive shots is above 90%, while, the false alarm rate for other shots is below 10%. Based on the density limit disruption prediction system and the real-time density feedback control system, the on-line density limit disruption avoidance system has been implemented on the J-TEXT tokamak.

In HL-2A and J-TEXT ohmic confinement regimes, an electrostatic turbulence with quasi-coherent characteristics in spectra of density fluctuations was observed by multi-channel microwave reflectometers. These quasi-coherent modes (QCMs) were detectable in a large plasma region (r/a∼0.3−0.8). The characteristic frequencies of QCMs were in the range of 30–140 kHz. The mode is rotated in the electron diamagnetic direction. In the plasmas with QCMs, trapped electron mode (TEM) was predicted to be unstable by gyrokinetic simulations. The combined experimental results show that the TEM is survived in the linear ohmic confinement regime of plasmas. The quasi-coherent TEM was replaced by broad-band fluctuations when the plasma transits from linear to saturated ohmic confinement regime. The observation was strongly related to the turbulence transition from TEM to ion temperature gradient mode. A critical gradient threshold for TEM excitation in electron temperature gradient was directly found. The effect of TEM on density profile peaking was presented.

The avoidance and suppression of runaway electron (RE) generation during disruptions is of great importance for the safe operation of tokamaks. Massive gas injection is used to suppress the generation of REs, but the poor gas mixing efficiency and extremely high density required to suppress RE generation make the full RE suppression unreliable. The magnetic perturbations provide an alternative RE suppression during disruptions. The use of mode penetration induced by resonant magnetic perturbations (RMPs) to suppress RE generation has been investigated on the J-TEXT tokamak. For a sufficiently long mode penetration duration, robust runaway suppression has been reached during the disruptions. The m/n=2/1 mode RMP with high amplitude excites large magnetic islands inside the plasma and leads to the large-scale destruction of magnetic surfaces during disruptions, which results in RE loss and runaway-free disruptions. The critical island width required for runaway suppression is estimated to be larger than 0.16 as the minor radius. This value might be slightly underestimated because of the misalignment between the electron cyclotron emission diagnostic and the island O-point. NIMROD simulations are used to investigate the effect of magnetic islands on RE generation during disruption, showing that the large magnetic islands have the ability to enhance RE seed loss during disruptions. RMP can excite large magnetic islands in the target plasma without tearing mode and might be a way to prevent RE generation during disruptions.

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