Probabilistic safety assessment in nuclear power plants by fuzzy numbers

Purba, JH; Lu, J; Ruan, D; Zhang, G

Probabilistic safety assessment in nuclear power plants by fuzzy numbers

Purba, JH Lu, J

Ruan, D Zhang, G

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Publication Type:: Conference Proceeding
Citation:: Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010, 2010, pp. 256 - 262
Issue Date:: 2010-01-01

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Field	Value	Language
dc.contributor.author	Purba, JH	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Ruan, D	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010, 2010, pp. 256 - 262	en_US
dc.identifier.isbn	9814324698	en_US
dc.identifier.isbn	9789814324694	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16236
dc.description.abstract	© 2010 by World Scientific Publishing Co. Pte. Ltd. Probabilistic safety assessment in nuclear power plants (NPPs) greatly considers plant safety and optimal plant design. Plant specific data are usually recommended to analyze safety in NPPs. However, such NPP specific data are not always available in practice. This paper presents an approach by combining fuzzy numbers and expert justification to assess an NPP probabilistic failure rate in the absence of statistical data. The proposed approach illustrates a case study for high pressure core spray systems of boiling water reactors.	en_US
dc.relation.ispartof	Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010	en_US
dc.relation.isbasedon	10.1142/9789814324700_0037	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Probabilistic safety assessment in nuclear power plants by fuzzy numbers	en_US
dc.type	Conference Proceeding
utslib.for	080199 Artificial Intelligence and Image Processing not elsewhere classified	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Chengdu, China	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2010 by World Scientific Publishing Co. Pte. Ltd. Probabilistic safety assessment in nuclear power plants (NPPs) greatly considers plant safety and optimal plant design. Plant specific data are usually recommended to analyze safety in NPPs. However, such NPP specific data are not always available in practice. This paper presents an approach by combining fuzzy numbers and expert justification to assess an NPP probabilistic failure rate in the absence of statistical data. The proposed approach illustrates a case study for high pressure core spray systems of boiling water reactors.

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http://hdl.handle.net/10453/16236