Inverse Uncertainty Quantification of Trace Physical Model Parameters Using Bayesian Analysis By

Forward quantification of uncertainties in code responses require knowledge of input model parameter uncertainties. Nuclear thermal-hydraulics codes such as RELAP5 and TRACE do not provide any information on physical model parameter uncertainties. A framework was developed to quantify input model parameter uncertainties based on Maximum Likelihood Estimation (MLE), Bayesian Maximum A Priori (MAP), and Markov Chain Monte Carlo (MCMC) algorithm for physical models using relevant experimental data. The objective of the present work is to perform the sensitivity analysis of the code input (physical model) parameters in TRACE and calculate their uncertainties using an MLE, MAP and MCMC algorithm, with a particular focus on the subcooled boiling model. The OECD/NEA BWR full-size fine-mesh bundle test (BFBT) data will be used to quantify selected physical model uncertainty of the TRACE code. The BFBT is based on a multi-rod assembly with measured data available for single or two-phase pressure drop, axial and radial void fraction distributions, and critical power for a wide range of system conditions. In this thesis, the steady-state cross-sectional averaged void fraction distribution from BFBT experiments is used as the input for inverse uncertainty algorithm, and the selected physical model's Probability Distribution Function (PDF) is the desired output quantity. iii ACKNOWLEDGMENTS First, I want to thank Prof. Tomasz Kozlowski and Prof. Caleb Brooks for making this thesis possible. In the spring of 2014, Prof. Kozlowski took me into his group and started to guide me into a research world. Since then, Prof. Kozlowski has been a wonderful mentor to me, both in respect to research projects and this mater thesis. Prof. Caleb Brooks was the instructor of one important course about the two-phase flow model. This course helped me a lot in understanding the two-phase flow and was one important basis of this thesis. Prof. Caleb Brooks is also one of the committee of this thesis and provides a lot of important comments and suggestion. firstly introduced the inverse uncertainty quantification algorithm in our group and his PhD work is one very important reference in this thesis. Xu is a PhD candidate in our group and helped me a lot in both our daily discussions and other research problems. Travis is a graduate student in our group and he offered great help in the final formatting of the thesis. Stefan is an undergraduate student in our group and helped in the review this thesis.