DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, HC | ko |
dc.contributor.author | Chang, Soon-Heung | ko |
dc.date.accessioned | 2013-03-03T04:34:26Z | - |
dc.date.available | 2013-03-03T04:34:26Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 1997 | - |
dc.identifier.citation | ANNALS OF NUCLEAR ENERGY, v.24, no.17, pp.1437 - 1446 | - |
dc.identifier.issn | 0306-4549 | - |
dc.identifier.uri | http://hdl.handle.net/10203/77242 | - |
dc.description.abstract | One of the key safety parameters during the transient of pressurized water reactor is the departure from nucleate boiling ratio (DNBR). In the transient analysis caused by the anticipated operational occurrences or accidents, the DNBR is predicted by three steps: firstly, a system transient analysis code, secondly, a heat flux calculation code and finally a detailed DNBR calculation code should be used. This tandem procedure is time consuming and very cumbersome. In this paper, the back propagation network (BPN) algorithm is incorporated into the system transient analysis code for the one-step transient DNBR calculation, thus, to eliminate the tandem procedure and to increase calculation speed. The BPN is trained with the DNBR training data selected by latin hypercube sampling technique. After the completion of training, performance is evaluated. Results show that the system transient analysis code with a multi-layer BPN algorithm can quickly calculate the transient DNBR with the reasonable accuracy even in case of axial flux shape changes. (C) 1997 Elsevier Science Ltd. | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | NEURAL-NETWORK | - |
dc.title | Development of a back propagation network for one-step transient DNBR calculations | - |
dc.type | Article | - |
dc.identifier.wosid | A1997YB40400005 | - |
dc.identifier.scopusid | 2-s2.0-0031281719 | - |
dc.type.rims | ART | - |
dc.citation.volume | 24 | - |
dc.citation.issue | 17 | - |
dc.citation.beginningpage | 1437 | - |
dc.citation.endingpage | 1446 | - |
dc.citation.publicationname | ANNALS OF NUCLEAR ENERGY | - |
dc.contributor.localauthor | Chang, Soon-Heung | - |
dc.contributor.nonIdAuthor | Kim, HC | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
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