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 key to 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}, \beta_{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 how 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. These results provide a possible disruption predictor development strategy for next generation tokamaks, such as ITER and SPARC, and highlight the importance of developing on existing machines baseline scenario discharges of future tokamaks to collect more relevant disruptive data.

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this letter, we present a new disruption prediction algorithm based on Deep Learning that effectively allows knowledge transfer from existing devices to new ones, while predicting disruptions using very limited disruptive data from the new devices. Future fusion reactors will need to run disruption-free or with very few unmitigated disruptions. The algorithm presented in this letter achieves high predictive accuracy on C-Mod, DIII-D and EAST tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that good accuracy (AUC=0.959) is achieved on EAST predictions by including a small number of disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. This holds true for all permutations of the three devices. This cross-machine data-driven study finds that non-disruptive data is machine-specific while disruptions are machine-independent.