Chen, D. L. | Search NASA

DOE OSTI2023

First results of Ne shattered pellet injection for mitigating plasma disruption with full metal wall in EAST tokamak

Disruption mitigation poses a significant and unresolved challenge for ITER and future fusion reactor devices. To mitigate the effect of plasma disruption, a Shattered Pellet Injection (SPI) system has been developed and the first rapid shutdown experiments have been successfully performed in the Experimental Advanced Superconducting Tokamak with full metal wall. The experiments confirmed the system’s capability to inject 5 mm diameter neon (Ne) pellets at velocities ranging from 100 to 400 m s −1 , with a maximum injected quantity of 13.2 Pa m 3 . Compared to unmitigated disruptions, the total radiation power was significantly higher with the respective use of SPI and Massive Gas Injection (MGI). Additionally, the radiation distribution and divertor heat flux were compared between SPI and MGI methods. The results demonstrated that SPI exhibited shorter cooling time, stronger core radiation and more uniform poloidal radiation distribution compared to MGI, indicating deeper deposition using SPI. Furthermore, during plasma shutdown, the electron temperature and peak heat flux near outer divertor strike points were reduced by 40% and 50% respectively, with SPI, in comparison to MGI. These findings serve as a valuable reference for implementing SPI technology as the baseline approach for disruption mitigation in ITER.

EAST↗

DOE OSTI2021

Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks

Next generation High Performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a device's HP operation based on its Low Performance (LP) data is a key to its success. In this letter, through explorative data analysis and dedicated numerical experiments on multiple existing tokamaks, we demonstrate how the operational regimes of tokamaks can affect the power of a trained disruption predictor. First, our results suggest data-driven disruption predictors trained on abundant LP discharges work poorly on the HP regime of the same tokamak, which is a consequence of the distinct distributions of the tightly correlated signals related to disruptions in these two regimes. Second, we find that matching operational parameters among tokamaks strongly improves cross-machine accuracy which implies our model learns from the underlying scalings of dimensionless physics parameters like q 95 , β p and confirms the importance of these parameters in disruption physics and cross machine domain matching from the data-driven perspective. Finally, our results show in the absence of HP data from the target devices, the best predictivity of the HP regime for the target machine can be achieved by combining LP data from the target with HP data from other machines. Furthermore, these results provide a possible disruption predictor development strategy for next generation tokamaks, such as ITER and SPARC, and highlight the importance of developing baseline scenario discharges of future tokamaks on existing machines to collect more relevant disruptive data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗