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

Aiden Durrant; Georgios Leontidis; Stefanos Kollias; Luis Torres; Cristina Montalvo; Antonios Mylonakis; Christophe Demazière; Paolo Vinai

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.

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