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.

The suppression of disruption-generated runaway electrons (REs) by supersonic molecular beam injection (SMBI) has been investigated on the J-TEXT tokamak. Experimental results demonstrate that the hydrogen injected by SMBI during plasma current flattop phase can provoke magnetic perturbations, which increase RE losses rapidly. The effective radial diffusion coefficient of REs due to SMBI is estimated as D r ≈ 16 m 2 s −1 . Based on this benefit, the SMBI has been used to explore the suppression of disruption-generated REs. In J-TEXT, RE current is created with rapid argon injection by a massive gas injection valve. It is found that hydrogen SMBI before disruption efficiently suppresses the generation of RE current.

In order to study the collapse deformation mechanism of metal liner under coupling of multi-physics field, the input current was obtained through circuit design and simulation by Multisim. The results show that the peak input current is determined by the capacitor voltage. The coupling simulation of electromagnetic-thermal-mechanical field was carried out by using LS-DYNA software. The influence mechanism of liner on the collapse deformation of the metal liner is analysed. The collapse deformation of liner with cylindrical tip is different from that of traditional liner. The tip of the cylinder is beneficial to utilizing the high pressure in the centre of the cylinder. But the micro-element at the top of the cone collapse to the axis with decreasing velocity along the outer contour of the tip. For the conical metal liner with cylindrical end, the current peak needs to reach more than 2 MA to make the liner obtain sufficient collapse velocity. The collapse velocity of conical metal liner element increases with the decrease of wall thickness, while the collapse velocity of bottom liner element decreases with the increase of cone diameter and cone height.