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Science and Technology of Nuclear Installations
Volume 2015, Article ID 839249, 17 pages
http://dx.doi.org/10.1155/2015/839249
Research Article

Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation

1MIT, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
2FPoliSolutions, LLC, 4618 Old William Penn Highway, Murrysville, PA 15668, USA
3INL, P.O. Box 1625, Idaho Falls, ID 83415-3870, USA

Received 16 January 2015; Revised 1 April 2015; Accepted 28 May 2015

Academic Editor: Francesco Di Maio

Copyright © 2015 Joseph P. Yurko et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator) construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. This work uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP) model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process.