Neutron Noise-based Anomaly Classification and Localization using Machine Learning

Christophe Demaziere*, Antonios Mylonakis, Paolo Vinai, Aiden Durrant, Fabio De Sousa Ribeiro, James Wingate, Georgios Leontidis, Stefanos Kollias

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

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.
Original languageEnglish
Pages1-9
Number of pages10
DOIs
Publication statusPublished - 22 Feb 2021
EventInternational Conference on Physics of Reactors - University of Cambridge, Cambridge, United Kingdom
Duration: 30 Mar 20202 Apr 2020
https://www.physor2020.com

Conference

ConferenceInternational Conference on Physics of Reactors
Abbreviated titlePHYSOR2020
CountryUnited Kingdom
CityCambridge
Period30/03/202/04/20
Internet address

Keywords

  • neutron noise
  • machine learning
  • core diagnostics
  • core monitoring

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