Quantitative Phenomena Identification and Ranking Table (QPIRT) for Bayesian Uncertainty Quantification

Abstract

Propagating parameter uncertainty for a nuclear reactor system code is a very challenging problem. Numerous parameters influence the system response in complicated and often non-linear fashions, in addition to sometimes lengthy computational times. Combined with a statistical sampling procedure only compounds this issue since the code must be run many times. The number of parameters sampled must therefore be limited to as few as possible that still accurately characterize the uncertainty in the system response. A Quantitative Phenomena Identification and Ranking Table (QPIRT) was developed to accomplish this goal. The QPIRT consists of two steps: a “Top-Down” step focusing on identifying the dominant physical phenomena controlling the system response, and a “Bottom-Up” step which focuses on determining the parameters from those key physical phenomena that significantly contribute to the response uncertainty. The Top-Down step evaluates phenomena using the governing equations of the system code at nominal parameter values, providing a “fast” screening step. The Bottom-Up step then analyzes the correlations and models for the phenomena identified from the Top-Down step to find which parameters to sample. A statistical screening method is then used to further eliminate those parameters that do not significantly influence the uncertainty of the response. This last screening before performing the full uncertainty propagation provides statistical rigor to the parameter selection process. The QPIRT, through the Top-Down and Bottom-Up steps thus provide a systematic approach to determining the limited set of physically relevant parameters that influence the uncertainty of the system response. This strategy was demonstrated through an application to a Total Loss of main Feedwater Flow (TLOFW) analysis using RELAP5. Ultimately, this work is the first component in a larger task of building a calibrated uncertainty propagation framework. The QPIRT is an essential piece because the uncertainty of those selected parameters will be calibrated to data from both Separate and Integral Effect Tests (SETs and IETs). Therefore the system response uncertainty will incorporate the knowledge gained from the database of past large IETs.