Inverse uncertainty quantification of trace physical model parameters using Bayesian analysis

Hu, Guojun

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https://hdl.handle.net/2142/89042 Copy

Description

Title

Inverse uncertainty quantification of trace physical model parameters using Bayesian analysis

Author(s)

Hu, Guojun

Issue Date

2015-12-03

Director of Research (if dissertation) or Advisor (if thesis)

Kozlowski, Tomasz

Department of Study

Nuclear, Plasma, & Radiological Engineering

Discipline

Nuclear, Plasma, & Radiological Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

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

Abstract

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.

Graduation Semester

2015-12

Type of Resource

text

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http://hdl.handle.net/2142/89042

Copyright and License Information

Copyright 2015 Guojun Hu

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