Detection and Localisation of Multiple In-Core Perturbations with Neutron Noise-Based Self-Supervised Domain Adaptation release_nd45vlpcsnawzopfyn3hz44zji

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

Published by Zenodo.

2021  

Abstract

Problem Case<br> • We aim to unfold reactor transfer function to provide core<br> diagnostics.<br> • Derivation of core perturbation characteristics to classify and locate its<br> origin.<br> • Yet this is challenging due to the limited number of neutron<br> detectors in western type reactors.<br> • We ask, can we use machine learning to successfully approximate the<br> reactor transfer function?<br> • However, to effectively train ML algorithms large quantities of<br> data are required.
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Date   2021-10-03
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