Neutron noise-based anomaly classification and localization using machine learning
Paper in proceedings, 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

core monitoring

machine learning

neutron noise

Author

Christophe Demaziere

Subatomic, High Energy and Plasma Physics

Antonios Mylonakis

Subatomic, High Energy and Plasma Physics

Paolo Vinai

Subatomic, High Energy and Plasma Physics

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

PHYSOR 2020: Transition to a Scalable Nuclear Future

1183

International conference on Physics of Reactors
Cambridge, United Kingdom,

Core monitoring techniques and experimental validation and demonstration (CORTEX)

European Commission (Horizon 2020), 2017-09-01 -- 2021-08-31.

Subject Categories

Computer and Information Science

Other Engineering and Technologies

Other Physics Topics

Areas of Advance

Energy

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Latest update

7/17/2020