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dc.contributor.advisorGeorge E. Apostolakis.en_US
dc.contributor.authorReinert, Joshua Men_US
dc.contributor.otherMassachusetts Institute of Technology. Technology and Policy Program.en_US
dc.date.accessioned2006-11-07T12:47:06Z
dc.date.available2006-11-07T12:47:06Z
dc.date.copyright2005en_US
dc.date.issued2005en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/34536
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2005.en_US
dc.descriptionIncludes bibliographical references (p. 66-68).en_US
dc.description.abstractModel uncertainties can have a significant impact on decisions regarding licensing basis changes. We present a methodology to identify basic events in the risk assessment that have the potential to change the decision and are known to have significant model uncertainties. Because we work with basic event probabilities, this methodology is not appropriate for analyzing uncertainties that cause a structural change to the model, such as success criteria. We use the Risk Achievement Worth (RAW) importance measure with respect to both the core damage frequency (CDF) and the change in core damage frequency (ACDF) to identify potentially important basic events. We cross-check these with generically important model uncertainties. Then, sensitivity analysis is performed on the basic event probabilities, which are used as a proxy for the model parameters, to determine how much error in these probabilities would need to be present in order to impact the decision. A previously submitted licensing basis change is used as a case study. Analysis using the SAPHIRE program identifies 20 basic events as important, four of which have model uncertainties that have been identified in the literature as generally important.en_US
dc.description.abstract(cont.) The decision is fairly insensitive to uncertainties in these basic events. In three of these cases, one would need to show that model uncertainties would lead to basic event probabilities that would be between two and four orders of magnitude larger than modeled in the risk assessment before they would become important to the decision. More detailed analysis would be required to determine whether these higher probabilities are reasonable. Methods to perform this analysis from the literature are reviewed and an example is demonstrated using the case study. We then look at policy issues surrounding the effects of uncertainty in decision making related to nuclear power generation.en_US
dc.description.statementofresponsibilityby Joshua M. Reinert.en_US
dc.format.extent111 p.en_US
dc.format.extent5910197 bytes
dc.format.extent5914808 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectTechnology and Policy Program.en_US
dc.titleIncluding model uncertainty in risk-informed decision-makingen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Technology and Policy Program.en_US
dc.identifier.oclc70962672en_US


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