Inverse uncertainty quantification of trace physical model parameters using BFBT benchmark data

Guojun Hu, Tomasz Kozlowski

Research output: Contribution to journalArticle

Abstract

Forward quantification of simulation (code) response uncertainties requires knowledge of physical model parameter uncertainties. Nuclear thermal-hydraulics codes, such as RELAP5 and TRACE, do not provide any information on uncertainties of physical model parameters. A framework is developed to quantify uncertainties of physical model parameters using Maximum Likelihood Estimation (MLE), Bayesian Maximum A Priori (MAP), and Markov Chain Monte Carlo (MCMC) algorithms. The objective of the present work is to perform the sensitivity analysis of the physical model parameters in code TRACE and calculate their uncertainties using MLE, MAP, and MCMC algorithms. The OECD/NEA BWR Full-size fine-mesh Bundle Test (BFBT) data is used to quantify uncertainty of selected physical models of 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 work, the steady-state cross-sectional averaged void fraction distribution is used as the input data for inverse uncertainty quantification (IUQ) algorithms, and the selected physical model's probability distribution function (PDF) is the desired output quantity.

Original languageEnglish (US)
Pages (from-to)197-203
Number of pages7
JournalAnnals of Nuclear Energy
Volume96
DOIs
StatePublished - Oct 1 2016

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Void fraction
Maximum likelihood estimation
Markov processes
Uncertainty
Probability distributions
Sensitivity analysis
Pressure drop
Distribution functions
Hydraulics
Hot Temperature

Keywords

  • BFBT
  • Inverse uncertainty quantification
  • MAP
  • MCMC
  • MLE
  • TRACE

ASJC Scopus subject areas

  • Nuclear Energy and Engineering

Cite this

Inverse uncertainty quantification of trace physical model parameters using BFBT benchmark data. / Hu, Guojun; Kozlowski, Tomasz.

In: Annals of Nuclear Energy, Vol. 96, 01.10.2016, p. 197-203.

Research output: Contribution to journalArticle

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