Status of the Deep Learning-Based Shattered Pellet Injection Shard Tracking at ASDEX Upgrade

Abstract

Plasma disruptions pose an intolerable risk to large tokamaks, such as ITER. If a disruption can no longer be avoided, ITER’s last line of defense will be the Shattered Pellet Injection. An experimental test bench was created at ASDEX Upgrade to inform the design decisions for controlling the shattering of the pellets and develop the techniques for the generation of the fragment distributions necessary for optimal disruption mitigation. In an effort to analyze the videos resulting from the more than 1000 tests and determine the impact of different settings on the resulting shard cloud, an analysis pipeline, based on traditional computer vision (CV), was created. This pipeline enabled the analysis of 173 of the videos, but at the same time showed the limits of traditional CV when applied in applications with a highly heterogeneous dataset such as this. We created a machine learning-based (ML) alternative as a drop-in replacement to the original image processing code using a semantic segmentation model to exploit the innate adaptability and robustness of deep learning models. This model is capable of labeling the entire dataset quickly, accurately and reliably. This contribution details the implementation of the ML model and the current state and future plans of the project.