Development of a prediction method for SAMG entry time in NPPs using the extended group method of data handling (GMDH) model

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dc.contributor.authorNo, Young Gyuko
dc.contributor.authorLee, Chanyoungko
dc.contributor.authorSeong, Poong Hyunko
dc.date.accessioned2018-10-19T00:28:47Z-
dc.date.available2018-10-19T00:28:47Z-
dc.date.created2018-09-27-
dc.date.created2018-09-27-
dc.date.issued2018-11-
dc.identifier.citationANNALS OF NUCLEAR ENERGY, v.121, pp.552 - 566-
dc.identifier.issn0306-4549-
dc.identifier.urihttp://hdl.handle.net/10203/245865-
dc.description.abstractThe importance of severe accident management is being re-examined because severe accident management failed during the Fukushima nuclear accident. Having an accurate estimation of a severe accident is important for coping properly with unfavorable conditions. In this study, the extended group method of data handling (GMDH) with a fuzzy concept is proposed as a means of predicting the major severe accident events that represent the severe accident management guideline (SAMG) entry time, including the time at which the reactor vessel (RV) water level decreases, the time when the core exit temperature (CET) reaches 450 degrees-centigrade, the time when the hydrogen concentration in the containment is over 4%, and the time when the reactor coolant system (RCS) pressure is over 2.86 MPa under loss of coolant accidents (LOCAs) in pressurized water reactors (PWRs). To train the extended GMDH model, it was necessary to acquire the data needed from a number of numerical simulations due to the lack of actual LOCA data. The data was obtained by carrying out simulations using the MAAP5 code. To optimize the developed model, the optimal input selection processes were performed using the clustering analysis including self-organizing feature map (SOFM), hierarchical and non-hierarchical clustering methods. The prediction accuracy of the three types of initiating accident, small break (SB), medium break (MB), and large break (LB) LOCAs, was high enough to predict the SAMG entry time. When compared with other artificial intelligence (AI) methods, the extended GMDH was found to be superior to the original GMDH and support vector regression (SVR). Therefore, it is expected that the extended GMDH model will help operators to properly mitigate severe accidents in PWRs. (C) 2018 Elsevier Ltd. All rights reserved.-
dc.languageEnglish-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.subjectSUPPORT VECTOR MACHINES-
dc.subjectSEVERE ACCIDENTS-
dc.subjectUNCERTAINTY ANALYSIS-
dc.subjectALGORITHMS-
dc.subjectNETWORKS-
dc.subjectREGRESSION-
dc.titleDevelopment of a prediction method for SAMG entry time in NPPs using the extended group method of data handling (GMDH) model-
dc.typeArticle-
dc.identifier.wosid000444668200053-
dc.identifier.scopusid2-s2.0-85051389586-
dc.type.rimsART-
dc.citation.volume121-
dc.citation.beginningpage552-
dc.citation.endingpage566-
dc.citation.publicationnameANNALS OF NUCLEAR ENERGY-
dc.identifier.doi10.1016/j.anucene.2018.08.019-
dc.contributor.localauthorSeong, Poong Hyun-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorSevere accident management guideline (SAMG)-
dc.subject.keywordAuthorEntry time-
dc.subject.keywordAuthorClustering analysis-
dc.subject.keywordAuthorExtended group method of data handling (GMDH)-
dc.subject.keywordAuthorMAAP5 code-
dc.subject.keywordPlusSUPPORT VECTOR MACHINES-
dc.subject.keywordPlusSEVERE ACCIDENTS-
dc.subject.keywordPlusUNCERTAINTY ANALYSIS-
dc.subject.keywordPlusALGORITHMS-
dc.subject.keywordPlusNETWORKS-
dc.subject.keywordPlusREGRESSION-
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