Inverse uncertainty quantification of trace physical model parameters using Bayesian analysis

Uncertainty quantification;TRACE;Maximum likelihood estimator (MLE);Bayesian Maximum A Priori(MAP);Markov Chain Monte Carlo (MCMC)

Hu, Guojun ; Kozlowski ; Tomasz

关键词: Uncertainty quantification;    TRACE;    Maximum likelihood estimator (MLE);    Bayesian Maximum A Priori(MAP);    Markov Chain Monte Carlo (MCMC);   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/89042/HU-THESIS-2015.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

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.