Neutron noise-based anomaly classification and localization using machine learning
Paper i proceeding, 2020

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.

core diagnostics

neutron noise

machine learning

core monitoring


Christophe Demaziere

Chalmers, Fysik, Subatomär, högenergi- och plasmafysik

Antonios Mylonakis

Chalmers, Fysik, Subatomär, högenergi- och plasmafysik

Paolo Vinai

Chalmers, Fysik, Subatomär, högenergi- och plasmafysik

Aiden Durrant

University of Lincoln

Fabio De Sousa Ribeiro

University of Lincoln

James Wingate

University of Lincoln

Georgios Leontidis

University of Lincoln

Stefanos Kollias

University of Lincoln

International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future, PHYSOR 2020

2913-2921 1183
9781713827245 (ISBN)

2020 International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future, PHYSOR 2020
Cambridge, United Kingdom,

Core monitoring techniques and experimental validation and demonstration (CORTEX)

Europeiska kommissionen (EU) (EC/H2020/754316), 2017-09-01 -- 2021-08-31.


Data- och informationsvetenskap

Annan teknik

Annan fysik





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