Detection and Localisation of Multiple In-Core Perturbations with Neutron Noise-Based Self-Supervised Domain Adaptation

Problem Case
• We aim to unfold reactor transfer function to provide core
diagnostics.
• Derivation of core perturbation characteristics to classify and locate its
origin.
• Yet this is challenging due to the limited number of neutron
detectors in western type reactors.
• We ask, can we use machine learning to successfully approximate the
reactor transfer function?
• However, to effectively train ML algorithms large quantities of
data are required.