Two stochastic optimization algorithms applied to nuclear reactor core design

Detalhes bibliográficos
Autor(a) principal: SACCO, Wagner Figueiredo
Data de Publicação: 2006
Outros Autores: OLIVEIRA, Cassiano R.E. de, PEREIRA, Cláudio Márcio do Nascimento Abreu, http://lattes.cnpq.br/2341184189645578
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Institucional do IEN
Texto Completo: http://carpedien.ien.gov.br:8080/handle/ien/1667
Resumo: Two stochastic optimization algorithms conceptually similar to Simulated Annealing are presented and applied to a core design optimization problem previously solved with Genetic Algorithms. The two algorithms are the novel Particle Collision Algorithm (PCA), which is introduced in detail, and Dueck’s Great Deluge Algorithm (GDA). The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak factor in a three-enrichment-zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. Results show that the PCA and the GDA perform very well compared to the canonical Genetic Algorithm and its variants, and also to Simulated Annealing, hence demonstrating their potential for other optimization applications.
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spelling SACCO, Wagner FigueiredoOLIVEIRA, Cassiano R.E. dePEREIRA, Cláudio Márcio do Nascimento Abreuhttp://lattes.cnpq.br/23411841896455782016-03-03T13:21:15Z2016-03-03T13:21:15Z2006http://carpedien.ien.gov.br:8080/handle/ien/1667Submitted by Sherillyn Lopes (sherillynmartins@yahoo.com.br) on 2016-03-03T13:21:15Z No. of bitstreams: 1 Two stochastic optimization algorithms applied to nuclear reactor core design. Progress in Nuclear Energy.pdf: 521150 bytes, checksum: 34f20ca2bd798a574d4aae37384f71ed (MD5)Made available in DSpace on 2016-03-03T13:21:15Z (GMT). No. of bitstreams: 1 Two stochastic optimization algorithms applied to nuclear reactor core design. Progress in Nuclear Energy.pdf: 521150 bytes, checksum: 34f20ca2bd798a574d4aae37384f71ed (MD5)Two stochastic optimization algorithms conceptually similar to Simulated Annealing are presented and applied to a core design optimization problem previously solved with Genetic Algorithms. The two algorithms are the novel Particle Collision Algorithm (PCA), which is introduced in detail, and Dueck’s Great Deluge Algorithm (GDA). The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak factor in a three-enrichment-zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. Results show that the PCA and the GDA perform very well compared to the canonical Genetic Algorithm and its variants, and also to Simulated Annealing, hence demonstrating their potential for other optimization applications.porInstituto de Engenharia NuclearIENBrasilMetaheuristicsStochastic optimizationNuclear reactor designTwo stochastic optimization algorithms applied to nuclear reactor core designinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article525539info:eu-repo/semantics/openAccessreponame:Repositório Institucional do IENinstname:Instituto de Engenharia Nuclearinstacron:IENLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/1667/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52ORIGINALTwo stochastic optimization algorithms applied to nuclear reactor core design. Progress in Nuclear Energy.pdfTwo stochastic optimization algorithms applied to nuclear reactor core design. Progress in Nuclear Energy.pdfapplication/pdf521150http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/1667/1/Two+stochastic+optimization+algorithms+applied+to+nuclear+reactor+core+design.+Progress+in+Nuclear+Energy.pdf34f20ca2bd798a574d4aae37384f71edMD51ien/1667oai:carpedien.ien.gov.br:ien/16672016-05-03 13:14:28.709Dspace IENlsales@ien.gov.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
dc.title.pt_BR.fl_str_mv Two stochastic optimization algorithms applied to nuclear reactor core design
title Two stochastic optimization algorithms applied to nuclear reactor core design
spellingShingle Two stochastic optimization algorithms applied to nuclear reactor core design
SACCO, Wagner Figueiredo
Metaheuristics
Stochastic optimization
Nuclear reactor design
title_short Two stochastic optimization algorithms applied to nuclear reactor core design
title_full Two stochastic optimization algorithms applied to nuclear reactor core design
title_fullStr Two stochastic optimization algorithms applied to nuclear reactor core design
title_full_unstemmed Two stochastic optimization algorithms applied to nuclear reactor core design
title_sort Two stochastic optimization algorithms applied to nuclear reactor core design
author SACCO, Wagner Figueiredo
author_facet SACCO, Wagner Figueiredo
OLIVEIRA, Cassiano R.E. de
PEREIRA, Cláudio Márcio do Nascimento Abreu
http://lattes.cnpq.br/2341184189645578
author_role author
author2 OLIVEIRA, Cassiano R.E. de
PEREIRA, Cláudio Márcio do Nascimento Abreu
http://lattes.cnpq.br/2341184189645578
author2_role author
author
author
dc.contributor.author.fl_str_mv SACCO, Wagner Figueiredo
OLIVEIRA, Cassiano R.E. de
PEREIRA, Cláudio Márcio do Nascimento Abreu
http://lattes.cnpq.br/2341184189645578
dc.subject.por.fl_str_mv Metaheuristics
Stochastic optimization
Nuclear reactor design
topic Metaheuristics
Stochastic optimization
Nuclear reactor design
dc.description.abstract.por.fl_txt_mv Two stochastic optimization algorithms conceptually similar to Simulated Annealing are presented and applied to a core design optimization problem previously solved with Genetic Algorithms. The two algorithms are the novel Particle Collision Algorithm (PCA), which is introduced in detail, and Dueck’s Great Deluge Algorithm (GDA). The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak factor in a three-enrichment-zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. Results show that the PCA and the GDA perform very well compared to the canonical Genetic Algorithm and its variants, and also to Simulated Annealing, hence demonstrating their potential for other optimization applications.
description Two stochastic optimization algorithms conceptually similar to Simulated Annealing are presented and applied to a core design optimization problem previously solved with Genetic Algorithms. The two algorithms are the novel Particle Collision Algorithm (PCA), which is introduced in detail, and Dueck’s Great Deluge Algorithm (GDA). The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak factor in a three-enrichment-zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. Results show that the PCA and the GDA perform very well compared to the canonical Genetic Algorithm and its variants, and also to Simulated Annealing, hence demonstrating their potential for other optimization applications.
publishDate 2006
dc.date.issued.fl_str_mv 2006
dc.date.accessioned.fl_str_mv 2016-03-03T13:21:15Z
dc.date.available.fl_str_mv 2016-03-03T13:21:15Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
status_str publishedVersion
format article
dc.identifier.uri.fl_str_mv http://carpedien.ien.gov.br:8080/handle/ien/1667
url http://carpedien.ien.gov.br:8080/handle/ien/1667
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Instituto de Engenharia Nuclear
dc.publisher.initials.fl_str_mv IEN
dc.publisher.country.fl_str_mv Brasil
publisher.none.fl_str_mv Instituto de Engenharia Nuclear
dc.source.none.fl_str_mv reponame:Repositório Institucional do IEN
instname:Instituto de Engenharia Nuclear
instacron:IEN
reponame_str Repositório Institucional do IEN
collection Repositório Institucional do IEN
instname_str Instituto de Engenharia Nuclear
instacron_str IEN
institution IEN
bitstream.url.fl_str_mv http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/1667/2/license.txt
http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/1667/1/Two+stochastic+optimization+algorithms+applied+to+nuclear+reactor+core+design.+Progress+in+Nuclear+Energy.pdf
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repository.name.fl_str_mv Dspace IEN
repository.mail.fl_str_mv lsales@ien.gov.br
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