Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46356088%3A_____%2F22%3AN0000013" target="_blank" >RIV/46356088:_____/22:N0000013 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1424-8220/22/1/113" target="_blank" >https://www.mdpi.com/1424-8220/22/1/113</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s22010113" target="_blank" >10.3390/s22010113</a>

Result language

angličtina

Original language name

Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks

Original language description

This work outlines an approach for localizing anomalies in nuclear reactor cores during their steady state operation, employing deep, one-dimensional, convolutional neural networks. Anomalies are characterized by the application of perturbation diagnostic techniques, based on the analysis of the so-called "neutron-noise" signals: that is, fluctuations of the neutron flux around the mean value observed in a steady-state power level. The proposed methodology is comprised of three steps: initially, certain reactor core perturbations scenarios are simulated in software, creating the respective perturbation datasets, which are specific to a given reactor geometry; then, the said datasets are used to train deep learning models that learn to identify and locate the given perturbations within the nuclear reactor core; lastly, the models are tested on actual plant measurements. The overall methodology is validated on hexagonal, pre-Konvoi, pressurized water, and VVER-1000 type nuclear reactors. The simulated data are generated by the FEMFFUSION code, which is extended in order to deal with the hexagonal geometry in the time and frequency domains. The examined perturbations are absorbers of variable strength, and the trained models are tested on actual plant data acquired by the in-core detectors of the Temelin VVER-1000 Power Plant in the Czech Republic. The whole approach is realized in the framework of Euratom's CORTEX project.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20305 - Nuclear related engineering; (nuclear physics to be 1.3);

Project

—

Continuities

R - Projekt Ramcoveho programu EK

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

1424-8220

Volume of the periodical

22

Issue of the periodical within the volume

1

Country of publishing house

CH - SWITZERLAND

Number of pages

22

Pages from-to

1-22

UT code for WoS article

000742450800001

EID of the result in the Scopus database

2-s2.0-85121641594