Artificial neural networks for spatial distribution of fuel assemblies in reload of PWR reactors

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dc.creatorEdyene Cely Oliveira
dc.creatorVictor Faria de Castro
dc.creatorCarlos Eduardo Velasquez Cabrera
dc.creatorClaubia Pereira
dc.date.accessioned2023-07-21T17:48:26Z
dc.date.accessioned2025-09-08T23:14:07Z
dc.date.available2023-07-21T17:48:26Z
dc.date.issued2017
dc.identifier.doihttps://doi.org/10.15392/bjrs.v7i2B.770
dc.identifier.issn2319-0612
dc.identifier.urihttps://hdl.handle.net/1843/56851
dc.languagepor
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInternational Nuclear Atlantic Conference
dc.rightsAcesso Aberto
dc.subjectInteligência computacional
dc.subjectReatores Nucleares
dc.subject.otherArtificial Neural Networks
dc.subject.otherComputational Intelligence
dc.subject.otherNuclear Reactors
dc.subject.otherPWR
dc.titleArtificial neural networks for spatial distribution of fuel assemblies in reload of PWR reactors
dc.typeArtigo de evento
local.citation.epage14
local.citation.spage1
local.description.resumoAn artificial neural network methodology is being developed in order to find an optimum spatial distribution of the fuel assemblies in a nuclear reactor core during reloading. The main bounding parameter of the modeling was the neutron multiplication factor, keff. The characteristics of the network are defined by the nuclear parame ters: cycle, burnup, enrichment, fuel type, and average power peak of each element. As for the artificial neural network, the ANN Feedforward Multi_Layer_Perceptron with various layers and neurons were constructed. Three algorithms were used and tested: LM (Levenberg-Marquardt), SCG (Scaled Conjugate Gradient) and BayR (Bayesian Regularization). The artificial neural network has implemented using MATLAB 2015a version. As preliminary results, the spatial distribution of the fuel assemblies in the core using a neural network was slightly better than the standard core.
local.identifier.orcidhttps://orcid.org/0000-0001-5742-6828
local.identifier.orcidhttps://orcid.org/0000-0002-2960-3150
local.identifier.orcidhttps://orcid.org/0000-0001-5999-9961
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA NUCLEAR
local.publisher.initialsUFMG
local.url.externahttps://www.bjrs.org.br/revista/index.php/REVISTA/article/view/770

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