Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks
[EN] 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 th...
| Autores: | , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/193280 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/193280 |
| Access Level: | acceso abierto |
| Palabra clave: | Neutron noise Neutron diffusion Deep learning Convolutional neural networks Pressurized water reactor Perturbation localization VVER-1000 Absorber of variable strength FEMFFUSION FISICA APLICADA MATEMATICA APLICADA |
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Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural NetworksPantera, LaurentStulík, PetrIoannou, GeorgeTasakos, ThanosAlexandridis, GeorgiosStafylopatis, AndreasVidal-Ferràndiz, Antoni|||0000-0001-5449-7356Carreño, Amanda|||0000-0003-2302-1157Ginestar Peiro, Damián|||0000-0003-1243-6648Neutron noiseNeutron diffusionDeep learningConvolutional neural networksPressurized water reactorPerturbation localizationVVER-1000Absorber of variable strengthFEMFFUSIONFISICA APLICADAMATEMATICA APLICADA[EN] 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 Temelín VVER-1000 Power Plant in the Czech Republic. The whole approach is realized in the framework of Euratom¿s CORTEX project.The research conducted was made possible through funding from the Euratom research and training programme 2014-2018 under grant agreement No. 754316 for the "CORe Monitoring Techniques And EXperimental Validation And Demonstration (CORTEX)" Horizon 2020 project, 2017-2021.MDPI AGDepartamento de Física AplicadaDepartamento de Ingeniería Química y NuclearDepartamento de Matemática AplicadaEscuela Técnica Superior de Ingeniería Aeroespacial y Diseño IndustrialInstituto Universitario de Matemática MultidisciplinarEscuela Técnica Superior de Ingeniería IndustrialInstituto Universitario de Seguridad Industrial, Radiofísica y MedioambientalCOMISION DE LAS COMUNIDADES EUROPEARepositorio Institucional de la Universitat Politècnica de València Riunet20222022-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/193280reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengEuropean Commission https://doi.org/10.13039/501100000780 H2020 754316open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1932802026-06-13T07:49:27Z |
| dc.title.none.fl_str_mv |
Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks |
| title |
Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks |
| spellingShingle |
Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks Pantera, Laurent Neutron noise Neutron diffusion Deep learning Convolutional neural networks Pressurized water reactor Perturbation localization VVER-1000 Absorber of variable strength FEMFFUSION FISICA APLICADA MATEMATICA APLICADA |
| title_short |
Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks |
| title_full |
Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks |
| title_fullStr |
Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks |
| title_full_unstemmed |
Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks |
| title_sort |
Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks |
| dc.creator.none.fl_str_mv |
Pantera, Laurent Stulík, Petr Ioannou, George Tasakos, Thanos Alexandridis, Georgios Stafylopatis, Andreas Vidal-Ferràndiz, Antoni|||0000-0001-5449-7356 Carreño, Amanda|||0000-0003-2302-1157 Ginestar Peiro, Damián|||0000-0003-1243-6648 |
| author |
Pantera, Laurent |
| author_facet |
Pantera, Laurent Stulík, Petr Ioannou, George Tasakos, Thanos Alexandridis, Georgios Stafylopatis, Andreas Vidal-Ferràndiz, Antoni|||0000-0001-5449-7356 Carreño, Amanda|||0000-0003-2302-1157 Ginestar Peiro, Damián|||0000-0003-1243-6648 |
| author_role |
author |
| author2 |
Stulík, Petr Ioannou, George Tasakos, Thanos Alexandridis, Georgios Stafylopatis, Andreas Vidal-Ferràndiz, Antoni|||0000-0001-5449-7356 Carreño, Amanda|||0000-0003-2302-1157 Ginestar Peiro, Damián|||0000-0003-1243-6648 |
| author2_role |
author author author author author author author author |
| dc.contributor.none.fl_str_mv |
Departamento de Física Aplicada Departamento de Ingeniería Química y Nuclear Departamento de Matemática Aplicada Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial Instituto Universitario de Matemática Multidisciplinar Escuela Técnica Superior de Ingeniería Industrial Instituto Universitario de Seguridad Industrial, Radiofísica y Medioambiental COMISION DE LAS COMUNIDADES EUROPEA Repositorio Institucional de la Universitat Politècnica de València Riunet |
| dc.subject.none.fl_str_mv |
Neutron noise Neutron diffusion Deep learning Convolutional neural networks Pressurized water reactor Perturbation localization VVER-1000 Absorber of variable strength FEMFFUSION FISICA APLICADA MATEMATICA APLICADA |
| topic |
Neutron noise Neutron diffusion Deep learning Convolutional neural networks Pressurized water reactor Perturbation localization VVER-1000 Absorber of variable strength FEMFFUSION FISICA APLICADA MATEMATICA APLICADA |
| description |
[EN] 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 Temelín VVER-1000 Power Plant in the Czech Republic. The whole approach is realized in the framework of Euratom¿s CORTEX project. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022 2022-01-01 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://riunet.upv.es/handle/10251/193280 |
| url |
https://riunet.upv.es/handle/10251/193280 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.relation.none.fl_str_mv |
European Commission https://doi.org/10.13039/501100000780 H2020 754316 |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Reconocimiento (by) http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 Reconocimiento (by) http://creativecommons.org/licenses/by/4.0/ |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
MDPI AG |
| publisher.none.fl_str_mv |
MDPI AG |
| dc.source.none.fl_str_mv |
reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname:Universitat Politècnica de València (UPV) |
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Universitat Politècnica de València (UPV) |
| reponame_str |
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
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RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
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15,301603 |