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

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

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dc.contributor.author Pantera, Laurent es_ES
dc.contributor.author Stulík, Petr es_ES
dc.contributor.author Vidal-Ferràndiz, Antoni es_ES
dc.contributor.author Carreño, Amanda es_ES
dc.contributor.author Ginestar Peiro, Damián es_ES
dc.contributor.author Ioannou, George es_ES
dc.contributor.author Tasakos, Thanos es_ES
dc.contributor.author Alexandridis, Georgios es_ES
dc.contributor.author Stafylopatis, Andreas es_ES
dc.date.accessioned 2023-05-11T18:02:12Z
dc.date.available 2023-05-11T18:02:12Z
dc.date.issued 2022-01 es_ES
dc.identifier.uri http://hdl.handle.net/10251/193280
dc.description.abstract [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. es_ES
dc.description.sponsorship 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. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Neutron noise es_ES
dc.subject Neutron diffusion es_ES
dc.subject Deep learning es_ES
dc.subject Convolutional neural networks es_ES
dc.subject Pressurized water reactor es_ES
dc.subject Perturbation localization es_ES
dc.subject VVER-1000 es_ES
dc.subject Absorber of variable strength es_ES
dc.subject FEMFFUSION es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s22010113 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/754316/EU es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería del Diseño - Escola Tècnica Superior d'Enginyeria del Disseny es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació es_ES
dc.description.bibliographicCitation Pantera, L.; Stulík, P.; Vidal-Ferràndiz, A.; Carreño, A.; Ginestar Peiro, D.; Ioannou, G.; Tasakos, T.... (2022). Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks. Sensors. 22(1):1-22. https://doi.org/10.3390/s22010113 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s22010113 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 22 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 22 es_ES
dc.description.issue 1 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 35009662 es_ES
dc.identifier.pmcid PMC8747522 es_ES
dc.relation.pasarela S\452586 es_ES
dc.contributor.funder COMISION DE LAS COMUNIDADES EUROPEA es_ES


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