Deep Learning for the Analysis of Disruption Precursors based on Plasma Tomography
This work addresses disruption prediction in fusion energy, which is critical for reactor safety and efficiency, but it is incremental as it applies existing machine learning methods to a specific domain problem.
The authors tackled the problem of predicting plasma disruptions in fusion reactors by analyzing radiation patterns from bolometer tomography, using a variational autoencoder for anomaly detection to identify precursors, achieving identification of disruption precursors across baseline pulses in recent JET campaigns.
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, which includes 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.