Recent J-TEXT research has highlighted the significance of the role that non-axisymmetric magnetic perturbations, so called three-dimensional (3D) magnetic perturbation (MP) fields, play in a fundamentally 2D concept, i.e. tokamaks. This paper presents the J-TEXT results achieved over the last two years, especially on the impacts of 3D MP fields on magnetohydrodynamic instabilities, plasma disruptions and plasma turbulence transport. On J-TEXT, the resonant MP (RMP) system, capable of providing either a static or a high frequency (up to 8 kHz) rotating RMP field, has been upgraded by adding a new set of 12 in-vessel saddle coils. The shattered pellet injection system was built in J-TEXT in the spring of 2018. The new capabilities advance J-TEXT to be at the forefront of international magnetic fusion facilities, allowing flexible study of 3D effects and disruption mitigation in a tokamak. The fast rotating RMP field has been successfully applied for avoidance of mode locking and the prevention of plasma disruption. A new control strategy, which applies pulsed RMP to the tearing mode only during the accelerating phase region, was proved by nonlinear numerical modelling to be efficient in accelerating mode rotation and even completely suppresses the mode. Remarkably, the rotating tearing mode was completely suppressed by the electrode biasing. The impacts of 3D magnetic topology on the turbulence has been investigated on J-TEXT. It is found that the fluctuations of electron density, electron temperature and plasma potential can be significantly modulated by the island structure, and a larger fluctuation level appears at the X-point of islands. The suppression of runaway electrons during disruptions is essential to the operation of ITER, and it has been reached by utilizing the 3D magnetic perturbations on J-TEXT. This may provide an alternative mechanism of runaway suppression for large-scale tokamaks and ITER.

Cr/W multilayer nanocomposites were presented in the paper as potential candidate materials for the plasma facing components in fusion reactors. We used neutron reflectometry to measure the depth profile of helium in the multienergy He ions irradiated [Cr/W (50 nm)]3 multilayers. Results showed that He-rich layers with low neutron scattering potential energy form at the Cr/W interfaces, which is in great agreement with previous modeling results of other multilayers. This phenomenon provided a strong evidence for the He trapping effects of Cr/W interfaces and implied the possibility of using the Cr/W multilayer nanocomposites as great He-tolerant plasma facing materials.

Recommender systems have been widely advocated as a way of coping with the problem of information overload for knowledge workers. Given this, multiple recommendation methods have been developed. However, it has been shown that no one technique is best for all users in all situations. Thus we believe that effective recommender systems should incorporate a wide variety of such techniques and that some form of overarching framework should be put in place to coordinate the various recommendations so that only the best of them (from whatever source) are presented to the user. To this end, we show that a marketplace, in which the various recommendation methods compete to offer their recommendations to the user, can be used in this role. Specifically, this article presents the principled design of such a marketplace (including the auction protocol, the reward mechanism, and the bidding strategies of the individual recommendation agents) and evaluates the market's capability to effectively coordinate multiple methods. Through analysis and simulation, we show that our market is capable of shortlisting recommendations in decreasing order of user perceived quality and of correlating the individual agent's internal quality rating to the user's perceived quality.

Disruption mitigation is essential for the next generation of tokamaks. The prediction of plasma disruption is the key to disruption mitigation. A neural network combining eight input signals has been developed to predict the density limit disruptions on the J-TEXT tokamak. An optimized training method has been proposed which has improved the prediction performance. The network obtained has been tested on 64 disruption shots and 205 non-disruption shots. A successful alarm rate of 82.8% with a false alarm rate of 12.3% can be achieved at 4.8 ms prior to the current spike of the disruption. It indicates that more physical parameters than the current physical scaling should be considered for predicting the density limit. It was also found that the critical density for disruption can be predicted several tens of milliseconds in advance in most cases. Furthermore, if the network is used for real-time density feedback control, more than 95% of the density limit disruptions can be avoided by setting a proper threshold.

Disruptions have the possibility of causing severe wall damage to large tokamaks like ITER. The mitigation of disruption damage is essential to the safe operation of a large-scale tokamak. The shattered pellet injection (SPI) technique, which is regarded as the primary injection method for ITER, presents several advantages relative to massive gas injection, including more rapid particle delivery, higher total particle assimilation, and more centrally peaked particle deposition. A dedicated argon SPI system that focuses on disruption mitigation and runaway current dissipation has been designed for the Joint Texas Experimental Tokamak (J-TEXT). A refrigerator is used to form a single argon pellet at around 64 K. The pellet will be shaped with a 5 mm diameter and a 1.5-10 mm length. Helium gas at room temperature will be used as a propellant gas for pellet acceleration. The pellet can be injected with a speed of 150-300 m/s. The time interval between injection cycles is about 8 min. The pellet will be shattered at the edge of the plasma and then injected into the core of plasma. The first experiments of SPI fast shutdown and runaway current dissipation have been performed.