The thermal and magnetic energies stored in the plasma are released in a very short time during the disruption and deposited on the device, which can cause damages to the integrity of plasma-facing components. In China Fusion Engineering Test Reactor (CFETR) and International Thermonuclear Experimental Reactor (ITER) with high stored energy and large plasma current, damages could be severer.
Recently, a research group from the Institute of Plasma Physics (ASIPP) of the Hefei Institutes of Physical Science (HFIPS) reported a series of new progresses in disruption physics including runaway electron and disruption prediction, which deepened the understanding on Experimental Advanced Superconducting Tokamak (EAST) disruption.
The researchers studied the general characteristics of disruption halo currents in EAST tokamak with ‘W-Like’ graphite divertor and ‘ITER-Like’ tungsten divertor, which provided with more physics information on the ITER divertor. As a result, halo currents decreased with the decrease of vertical displacement, and therefore obtained a lower limit of halo fraction versus Toroidal Peaking Factor.
They further investigated the runaway electron generation and loss in EAST disruption, aiming at elaborating contribution of wave resonant supra-thermal electrons by lower hybrid waves on runaway seeds, and the effects of magnetohydrodynamics instabilities on runaway electron (RE) loss are analyzed in detail.
Besides, experimentally observation demonstrated two threshold electric fields, characterizing a lower field required for significant seed RE generation and sustainment and a higher field required for the RE avalanche onset in the flattop. These results also open a possibility for RE suppression before the RE avalanche onset.
In another experiment, to classify disruptive discharges and distinguish them from non-disruptive discharges, a full convolutional neural network was trained on a large database of experimental EAST data. The true positive rate of the model increases up to 0.875, while the false rate decreases to 0.061. The proposed data-driven predicted model exhibits immense potential for application in long pulse fusion device such as ITER.
They also applied a real-time disruption predictor using a random forest (DPRF) for high-density disruptions to the plasma control system of the EAST tokamak.
The result confirmed the viability of DPRF to trigger the mitigation system, and can be more valuable when its interpretability is preserved to aid physics-based strategies.
The studies serve as solid basis for future development on disruption mitigation and prediction.
The studies were supported by the National Key R&D Program of China, Youth Innovation Promotion Association o Chinese Academy of Science and the National Natural Science Foundation of China.
Featured image: the disruption prediction category implemented in the real-time computer of EAST PCS. (Image by HU Wenhui)
- Real-time prediction of high-density EAST disruptions using random forest
- Runaway electron generation and loss in EAST disruptions
- Characterization of disruption halo current between ‘W-Like’ graphite divertor and ‘ITER-Like’ divertor structure on EAST tokamak
- Disruption prediction using a full convolutional neural network on EAST
- Observation of two threshold fields for runaway-electron generation in tokamaks
Provided by Chinese Academy of Sciences