Return to search

Quantified PIRT and uncertainty quantification for computer code validation

This study is intended to investigate and propose a systematic method for uncertainty quantification for the computer code validation application. Uncertainty quantification has gained more and more attentions in recent years. U.S. Nuclear Regulatory Commission (NRC) requires the use of realistic best estimate (BE) computer code to follow the rigorous Code Scaling, Application and Uncertainty (CSAU) methodology. In CSAU, the Phenomena Identification and Ranking Table (PIRT) was developed to identify important code uncertainty contributors. To support and examine the traditional PIRT with quantified judgments, this study proposes a novel approach, the Quantified PIRT (QPIRT), to identify important code models and parameters for uncertainty quantification. Dimensionless analysis to code field equations to generate dimensionless groups (�� groups) using code simulation results serves as the foundation for QPIRT. Uncertainty quantification using DAKOTA code is proposed in this study based on the sampling approach. Nonparametric statistical theory identifies the fixed number of code run to assure the 95 percent probability and 95 percent confidence in the code uncertainty intervals. / Graduation date: 2013 / Access restricted to the OSU Community, at author's request, from Dec. 5, 2012 - Dec. 5, 2013

Identiferoai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/35475
Date05 December 2013
CreatorsLuo, Hu
ContributorsWu, Qiao
Source SetsOregon State University
Languageen_US
Detected LanguageEnglish
TypeThesis/Dissertation

Page generated in 0.0014 seconds