Improving PWR core simulations by Monte Carlo uncertainty analysis and Bayesian inference

Castro González, Emilio and Ahnert Iglesias, Carolina and Buss, Oliver and García Herranz, Nuria and Hoefer, Axel and Porsch, D. (2016). Improving PWR core simulations by Monte Carlo uncertainty analysis and Bayesian inference. "Annals of Nuclear Energy", v. 95 ; pp. 148-156. ISSN 0306-4549. https://doi.org/10.1016/j.anucene.2016.05.007.

Description

Title: Improving PWR core simulations by Monte Carlo uncertainty analysis and Bayesian inference
Author/s:
  • Castro González, Emilio
  • Ahnert Iglesias, Carolina
  • Buss, Oliver
  • García Herranz, Nuria
  • Hoefer, Axel
  • Porsch, D.
Item Type: Article
Título de Revista/Publicación: Annals of Nuclear Energy
Date: 12 May 2016
Volume: 95
Subjects:
Freetext Keywords: uncertainty analysis, nuclear data, Monte Carlo methods, PWR core analysis, Bayesian inference
Faculty: E.T.S.I. Industriales (UPM)
Department: Ingeniería Energética
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The Monte Carlo-based Bayesian inference model MOCABA is applied to the prediction of reactor operation parameters of a PWR nuclear power plant. In this non-perturbative framework, high-dimensional covariance information describing the uncertainty of microscopic nuclear data is combined with measured reactor operation data in order to provide statistically sound, well founded uncertainty estimates of integral parameters, such as the boron letdown curve and the burnup-dependent reactor power distribution. The performance of this methodology is assessed in a blind test approach, where we use measurements of a given reactor cycle to improve the prediction of the subsequent cycle. As it turns out, the resulting improvement of the prediction quality is impressive. In particular, the prediction uncertainty of the boron letdown curve, which is of utmost importance for the planning of the reactor cycle length, can be reduced by one order of magnitude by including the boron concentration measurement information of the previous cycle in the analysis. Additionally, we present first results of non-perturbative nuclear-data updating and show that predictions obtained with the updated libraries are consistent with those induced by Bayesian inference applied directly to the integral observables.

More information

Item ID: 44290
DC Identifier: http://oa.upm.es/44290/
OAI Identifier: oai:oa.upm.es:44290
DOI: 10.1016/j.anucene.2016.05.007
Official URL: http://www.sciencedirect.com/science/article/pii/S0306454916302547
Deposited by: Memoria Investigacion
Deposited on: 09 Jan 2017 16:04
Last Modified: 12 May 2018 22:30
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