Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/56851
Type: Artigo de Evento
Title: Artificial neural networks for spatial distribution of fuel assemblies in reload of PWR reactors
Authors: Edyene Cely Oliveira
Victor Faria de Castro
Carlos Eduardo Velasquez Cabrera
Claubia Pereira
Abstract: An 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.
Subject: Inteligência computacional
Reatores Nucleares
language: por
metadata.dc.publisher.country: Brasil
Publisher: Universidade Federal de Minas Gerais
Publisher Initials: UFMG
metadata.dc.publisher.department: ENG - DEPARTAMENTO DE ENGENHARIA NUCLEAR
Rights: Acesso Aberto
metadata.dc.identifier.doi: https://doi.org/10.15392/bjrs.v7i2B.770
URI: http://hdl.handle.net/1843/56851
Issue Date: 2017
metadata.dc.url.externa: https://www.bjrs.org.br/revista/index.php/REVISTA/article/view/770
metadata.dc.relation.ispartof: International Nuclear Atlantic Conference
Appears in Collections:Artigo de Evento



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.