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dc.contributor.advisorJacopo Buongiorno.en_US
dc.contributor.authorYurko, Joseph Paulen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Nuclear Science and Engineering.en_US
dc.date.accessioned2014-12-08T18:48:31Z
dc.date.available2014-12-08T18:48:31Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/92095
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2014.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 359-366).en_US
dc.description.abstractUncertainty quantification in thermal-hydraulic safety codes is a very challenging and computationally expensive endeavor. Methods are therefore needed to reduce that computational burden, while still providing a reasonable estimate for uncertainty. To do so, a Quantitative Phenomena Identification and Ranking Table (QPIRT) is implemented to screen down to key parameters that influence a figure of merit. From there, a surrogate model is built to approximate the complex input-output relationship of the safety code. The surrogate model type chosen is that of a probabilistic response surface following the Gaussian Process (GP) model framework. A GP prior is placed on the input/output functional relationship, which ultimately leads to a Bayesian non-parametric non-linear model of the safety code. The surrogate emulates the behavior of the long running computer code and thanks to the GP, provides a simple estimate to the additional uncertainty in making a prediction. In addition, for emulating multiple outputs together, which is difficult to do with standard GP models, Gaussian Process Factor Analysis (GPFA) models also known as Function Factorization with Gaussian Process Priors (FFGP) models were applied. The FFGP models are far more complicated than the standard GP model and so various simplifying approximations were made to enable fast yet accurate emulation of the safety code. All together a suite of surrogate models with varying levels of complexity and thus flexibility were developed for emulating the complex response from a safety code. These very computationally cheap surrogates are then used to propagate the uncertainty in the key parameters onto the FOM. Information from previous Separate and Integral Effect Tests is then used to calibrate those key parameter distributions with Markov Chain Monte Carlo (MCMC). This allows the ultimate uncertainty of the figure of merit to be found conditioned on the knowledge gained from those past experiments.en_US
dc.description.statementofresponsibilityby Joseph Paul Yurko.en_US
dc.format.extent366 pagesen_US
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/1721.1/7582en_US
dc.subjectNuclear Science and Engineering.en_US
dc.titleUncertainty quantification in safety codes using a Bayesian approach with data from separate and integral effect testsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Nuclear Science and Engineering.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Nuclear Science and Engineering
dc.identifier.oclc895775682en_US


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