NEUTRON NOISE-BASED ANOMALY CLASSIFICATION AND LOCALIZATION USING MACHINE LEARNING

¹ Chalmers University of Technology, Department of Physics, Division of Subatomic and Plasma Physics, SE-412 96 Gothenburg, Sweden
² University of Lincoln, School of Computer Science, MLearn Group, Lincoln, United Kingdom

demaz@chalmers.se
antonios.mylonakis@chalmers.se
vinai@chalmers.se
ADurrant@lincoln.ac.uk
fDeSousaRibeiro@lincoln.ac.uk
JWingate@lincoln.ac.uk
GLeontidis@lincoln.ac.uk
SKollias@lincoln.ac.uk

Published online: 22 February 2021

Abstract

A methodology is proposed in this paper allowing the classification of anomalies and subsequently their possible localization in nuclear reactor cores during operation. The method relies on the monitoring of the neutron noise recorded by in-core neutron detectors located at very few discrete locations throughout the core. In order to unfold from the detectors readings the necessary information, a 3-dimensional Convolutional Neural Network is used, with the training and validation of the network based on simulated data. In the reported work, the approach was also tested on simulated data. The simulations were carried out in the frequency domain using the CORE SIM+ diffusion-based two-group core simulator. The different scenarios correspond to the following cases: a generic “absorber of variable strength”, axially travelling perturbations at the velocity of the coolant flow (due to e.g. fluctuations of the coolant temperature at the inlet of the core), fuel assembly vibrations, control rod vibrations, and core barrel vibrations. In all those cases, various frequencies were considered and, when relevant, different locations of the perturbations and different vibration modes were taken into account. The machine learning approach was able to correctly identify the different scenarios with a maximum error of 0.11%. Moreover, the error in localizing anomalies had a mean squared error of 0.3072 in mesh size, corresponding to less than 4 cm. The proposed methodology was also demonstrated to be insensitive to parasitic noise and will be tested on actual plant data in the near future.