Two-phase flow void fraction estimation based on bubble image segmentation using Randomized Hough Transform with Neural Network (RHTN)

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dc.contributor.authorSERRA, PEDRO L.S.pt_BR
dc.contributor.authorMASOTTI, PAULO H.F.pt_BR
dc.contributor.authorROCHA, MARCELO S.pt_BR
dc.contributor.authorANDRADE, DELVONEI A. dept_BR
dc.contributor.authorTORRES, WALMIR M.pt_BR
dc.contributor.authorMESQUITA, ROBERTO N. dept_BR
dc.coverageInternacionalpt_BR
dc.date.accessioned2019-11-27T17:39:36Z
dc.date.available2019-11-27T17:39:36Z
dc.date.issued2020pt_BR
dc.description.abstractThe International Atomic Energy Agency (IAEA) has been encouraging the use of passive cooling systems in new designs of nuclear power plants. Next nuclear reactor generations are intended to have simpler and robust safety resources. Natural Circulation based systems hold an undoubtedly prominent position among these. The study of limiting conditions of these systems has led to instability behavior analysis where many different two-phase flow patterns are present. Void fraction is a key parameter in thermal transfer analysis of these flow instability conditions. This work presents a new method to estimate void fraction from images captured of an experimental two-phase flow circuit. The method integrates a set of Artificial Neural Networks with a modified Randomized Hough Transform to make multiple scans over acquired images, using crescent-sized masks. This method was called Randomized Hough Transform with Neural Network (RHTN). Each different mask size is chosen according with bubble sizes, which are the main ‘objects of interest’ in this image analysis. Images are segmented using fuzzy inference with different parameters adjusted based on acquisition focus. Void fraction calculation considers the volume of the imaged geometrical section of flow inside cylindrical glass tubes considering the acquisition depth-of-field used. The bubble volume is estimated based on geometrical parameters inferred for each detected bubble. The image database is obtained from experiments performed on a vertical two-phase flow circuit made of cylindrical glass where flow-patterns visualization is possible. The results have shown that the estimation method had good agreement with increasing void fraction experimental values. RHTN has been very efficient as bubble detector with very low ‘false-positive’ cases (< 0.004%) due robustness obtained through integration between Artificial Neural Networks with Randomized Hough Transforms.pt_BR
dc.description.sponsorshipFinanciadora de Estudos e Projetos (FINEP)pt_BR
dc.description.sponsorshipIDFINEP: REDETEC-CNEN 01.10.0248.0/2010pt_BR
dc.format.extent1-21pt_BR
dc.identifier.citationSERRA, PEDRO L.S.; MASOTTI, PAULO H.F.; ROCHA, MARCELO S.; ANDRADE, DELVONEI A. de; TORRES, WALMIR M.; MESQUITA, ROBERTO N. de. Two-phase flow void fraction estimation based on bubble image segmentation using Randomized Hough Transform with Neural Network (RHTN). <b>Progress in Nuclear Energy</b>, v. 118, p. 1-21, 2020. DOI: <a href="https://dx.doi.org/10.1016/j.pnucene.2019.103133">10.1016/j.pnucene.2019.103133</a>. Disponível em: http://repositorio.ipen.br/handle/123456789/30353.
dc.identifier.doi10.1016/j.pnucene.2019.103133pt_BR
dc.identifier.issn0149-1970pt_BR
dc.identifier.orcid0000-0002-5355-0925pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-2445-1298
dc.identifier.orcid0000-0002-6689-3011pt_BR
dc.identifier.orcid0000-0003-2445-1298pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-5355-0925
dc.identifier.orcidhttps://orcid.org/0000-0002-6689-3011
dc.identifier.percentilfi80.88pt_BR
dc.identifier.percentilfiCiteScore62.25
dc.identifier.urihttp://repositorio.ipen.br/handle/123456789/30353
dc.identifier.vol118pt_BR
dc.relation.ispartofProgress in Nuclear Energypt_BR
dc.rightsopenAccesspt_BR
dc.subjecttwo-phase flow
dc.subjectvoid fraction
dc.subjectneural networks
dc.subjectimage processing
dc.subjectbubbles
dc.subjectfuzzy logic
dc.subjecttransformations
dc.subjectnuclear power plants
dc.subjectpattern recognition
dc.subjectnatural convection
dc.subjectrandomness
dc.titleTwo-phase flow void fraction estimation based on bubble image segmentation using Randomized Hough Transform with Neural Network (RHTN)pt_BR
dc.typeArtigo de periódicopt_BR
dspace.entity.typePublication
ipen.autorMARCELO DA SILVA ROCHA
ipen.autorROBERTO NAVARRO DE MESQUITA
ipen.autorWALMIR MAXIMO TORRES
ipen.autorDELVONEI ALVES DE ANDRADE
ipen.autorPAULO HENRIQUE FERRAZ MASOTTI
ipen.codigoautor7992
ipen.codigoautor1375
ipen.codigoautor188
ipen.codigoautor1258
ipen.codigoautor219
ipen.contributor.ipenauthorMARCELO DA SILVA ROCHA
ipen.contributor.ipenauthorROBERTO NAVARRO DE MESQUITA
ipen.contributor.ipenauthorWALMIR MAXIMO TORRES
ipen.contributor.ipenauthorDELVONEI ALVES DE ANDRADE
ipen.contributor.ipenauthorPAULO HENRIQUE FERRAZ MASOTTI
ipen.date.recebimento19-11
ipen.identifier.fi2.256pt_BR
ipen.identifier.fiCiteScore3.3
ipen.identifier.ipendoc25565pt_BR
ipen.identifier.iwosWoSpt_BR
ipen.range.fi1.500 - 2.999
ipen.range.percentilfi75.00 - 100.00
ipen.type.genreArtigo
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sigepi.autor.atividadeMESQUITA, ROBERTO N. de:1375:420:Npt_BR
sigepi.autor.atividadeTORRES, WALMIR M.:188:450:Npt_BR
sigepi.autor.atividadeANDRADE, DELVONEI A. de:1258:420:Npt_BR
sigepi.autor.atividadeROCHA, MARCELO S.:7992:420:Npt_BR
sigepi.autor.atividadeMASOTTI, PAULO H.F.:219:420:Npt_BR
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