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A large number of runaway electrons (REs) with energies as high as several tens of mega-electron volt (MeV) may be generated during disruptions on a large-scale tokamak. The kinetic energy carried by REs is eventually deposited on the plasma-facing components, causing damage and posing a threat on the operation of the tokamak. The remaining magnetic energy following a thermal quench is significant on a large-scale tokamak. The conversion of magnetic energy to runaway kinetic energy will increase the threat of runaway electrons on the first wall. The magnetic energy dissipated inside the vacuum vessel (VV) equals the decrease of initial magnetic energy inside the VV plus the magnetic energy flowing into the VV during a disruption. Based on the estimated magnetic energy, the evolution of magnetic-kinetic energy conversion are analyzed through three periods in disruptions with a runaway current plateau.

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 m2 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.

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.