Deep Learning for the Analysis of Disruption Precursors Based on Plasma Tomography

Abstract

The JET baseline scenario is being developed to achieve high fusion performance and sustained fusion power. However, with higher plasma current and higher input power, an increase in pulse disruptivity is being observed. Although there is a wide range of possible disruption causes, the present disruptions seem to be closely related to radiative phenomena such as impurity accumulation, core radiation, and radiative collapse. In this work, we focus on bolometer tomography to reconstruct the plasma radiation profile, and on top of it, we apply anomaly detection to identify the radiation patterns that precede major disruptions. The approach makes extensive use of machine learning. First, we train a surrogate model for plasma tomography based on matrix multiplication, which provides a fast method to compute the plasma radiation profiles across the full extent of any given pulse. Then, we train a variational autoencoder to reproduce the radiation profiles by encoding them into a latent distribution and subsequently decoding them. As an anomaly detector, the variational autoencoder struggles to reproduce unusual behaviors that include not only the actual disruptions but their precursors as well. These precursors are identified based on an analysis of the anomaly score across all baseline pulses in two recent campaigns at JET.

Keywords:

• Plasma tomography
• machine learning
• anomaly detection

Acknowledgments

This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training program 2014–2018 and 2019–2020 under grant agreement number 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission. Instituto de Plasmas e Fusão Nuclear received financial support from Fundação para a Ciência e Tecnologia through projects UIDB/50010/2020 and UIDP/50010/2020. The authors are thankful for the granted use of computational resources provided by the MARCONI-FUSION HPC facility at CINECA, Italy, and by the Culham Centre for Fusion Energy/United Kingdom Atomic Energy Authority, United Kingdom.