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

TL;DR

This study investigates how operational regimes affect disruption prediction in tokamaks, emphasizing the importance of data matching and transfer learning for reliable predictions in next-generation devices like ITER and SPARC.

Contribution

It demonstrates the impact of operational regimes on disruption predictor performance and proposes a data-driven strategy for developing reliable predictors for future high-performance tokamaks.

Findings

LP-trained predictors perform poorly on HP regimes within the same tokamak.

Matching operational parameters across tokamaks improves cross-machine prediction accuracy.

Combining LP data from the target with HP data from other machines enhances prediction of HP regimes.

Abstract

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.