Inverse Uncertainty Quantification of Reactor Simulation with Polynomial Chaos Surrogate Model

Journal Article · Wed Jun 15 04:00:00 EDT 2016 · Transactions of the American Nuclear Society

OSTI ID:22991923

Wu, Xu; Kozlowski, Tomasz ^[1]

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1. Department of Nuclear, Plasma and Radiological Engineering, University of Illinois at Urbana-Champaign, 224 Talbot Lab., 104 South Wright Street, Urbana, Illinois, 61801 (United States)

Reliable uncertainty quantification (UQ) and sensitivity analysis (SA) is significant part of modelling and simulation. This is particularly important in the nuclear community due to the fact that the licensing of nuclear installations has shifted from conservative to Best Estimate plus Uncertainty (BEPU) methodologies. UQ can be broadly classified as forward UQ and inverse UQ. Forward UQ is the process of quantifying the uncertainties in simulation outputs by propagating the uncertainties in input parameters. It requires knowledge in the input uncertainty, which is often specified by ad-hoc expert judgment which lacks mathematical rigor. The inverse UQ is the process to determine the uncertainty in input parameters given observations of the output. The purpose of this paper is to replace such ad-hoc expert judgment of the statistical properties of input model parameters in nuclear reactor simulation. This summary presents work on inverse UQ of a nonlinear Point Reactor Kinetics Equations - lumped thermal-hydraulics parameter coupled problem. Bayesian inference and Markov Chain Monte Carlo (MCMC) are used for the inverse UQ. Because MCMC requires a large number of simulations which is impractical for models that are computationally expensive, surrogate models is constructed by generalized Polynomial Chaos (PC) expansion technique. (authors)

• OSTI ID: 22991923
• Journal Information: Transactions of the American Nuclear Society, Journal Name: Transactions of the American Nuclear Society Journal Issue: 1 Vol. 114; ISSN 0003-018X
• Country of Publication: United States
• Language: English

Related Subjects

22 GENERAL STUDIES OF NUCLEAR REACTORS
97 MATHEMATICS AND COMPUTING
CHAOS THEORY
LICENSING
MARKOV PROCESS
MONTE CARLO METHOD
NUCLEAR FACILITIES
POLYNOMIALS
REACTOR KINETICS EQUATIONS
REACTORS
SENSITIVITY ANALYSIS
SIMULATION
THERMAL HYDRAULICS