Proposta de utilização de redes neurais feedforward multicamadas para a otimização de padrões de recarga do combustível em um reator PWR
DOI:
https://doi.org/10.15392/bjrs.v7i3A.834Keywords:
Reator PWR, Redes neurais artificiais, Carga de combustívelAbstract
O gerenciamento de recarga de combustível em um reator de potência tem como principal foco o padrão de recarga no núcleo de modo a alcançar melhor rendimento no ciclo observando todos os parâmetros de segurança adotados. Neste artigo apresentamos a estratégia de um algoritmo baseado em redes neurais artificiais que poderia ser usado para otimizar a recarga de combustível num reator nuclear. A ideia é apresentar uma metodologia baseada em rede neural feedforward multicamadas baseada em neurônios multi-valorados, que poderá ser usada para desenvolver uma metodologia capaz de escolher as melhores combinações que satisfaçam o fator de pico de potência radial e maximizem o fator de multiplicação efetivo no início do ciclo, e também satisfaçam a relação de potência crítica mínima e taxa máxima de geração de calor no final do ciclo.
Downloads
References
H. G. KIM, S. H. CHANG, AND B. H. LEE, Optimal Fuel Loading Pattern Design Using an Artificial Neural Network and a Fuzzy Rule-Based System, Nuclear Science and Engineering, v. 115, p. 152–163, 1993.
R. CARMONA, C. MARTIN-DEL-CAMPO, I. OROPEZA, AND J.-L. FRANOIS, Genetic algorithm approach for radial fuel lattice optimization in BWRS, American Nuclear Society, vol. 1, p. 323-333, 2009.
E. F. FARIA AND C. PEREIRA, Nuclear fuel loading pattern optimisation using a neural network, Annals of Nuclear Energy, v. 30, p. 603–613, 2003.
C. LIN, J.-I. YANG, K.-J. LIN, AND Z.-D. WANG, Pressurized Water Reactor Loading Pattern Design Using the Simple Tabu Search, Nuclear Science and Engineering, v. 129, p. 61–71, 1998.
T. ŠMUC, D. PEVEC, AND B. PETROVIC, Annealing strategies for loading pattern optimization, Annals of nuclear energy, v. 21, p. 325–336, 1994.
I. AIZENBERG AND C. MORAGA, Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm, Soft Computing, v. 11, p. 169–183, 2007.
E. M. AZOFF, Neural network time series forecasting of financial markets. John Wiley & Sons, Inc., 1994.
M. T. HAGAN, H. B. DEMUTH, M. H. BEALE, AND O. DE JESUS, Neural network design, v. 20. Pws Pub. Boston, 1996.
J. J. ORTIZ AND I. REQUENA, Using a multi-state recurrent neural network to optimize loading patterns in BWRs, Annals of Nuclear Energy, v. 31, p. 789–803, 2004.
M. HLAVÁČEK, Multilayer feedforward neural networks based on multi-valued neurons, PhD Thesis, Masarykova univerzita, Fakulta informatiky, 2014.
DOWNAR, T. et al., PARCS v2.7 U.S. NRC Core Neutronics Simulator User Manual, School of Nuclear Engineering Purdue University, 2006.
M. HLAVACEK, Machine learning tool: simulator of multi-layered neural network (like MLP) with multi-valued neurons. - MiraHead/mlmvn. 2018.
WIMSD-5B, 2007 - RSICC Computer Code Collection WIMSD-5B.12 – Deterministic
Code System for Reactor-Lattice Calculations – Oak Ridg National Laboratory, 2007.
Published
Issue
Section
License
Copyright (c) 2019 Brazilian Journal of Radiation Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
Licensing: The BJRS articles are licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
How to Cite
