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

Castro González, Emilio; Ahnert Iglesias, Carolina; Buss, Oliver; García Herranz, Nuria; Hoefer, Axel y 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.

Descripción

Título: Improving PWR core simulations by Monte Carlo uncertainty analysis and Bayesian inference
Autor/es:
  • Castro González, Emilio
  • Ahnert Iglesias, Carolina
  • Buss, Oliver
  • García Herranz, Nuria
  • Hoefer, Axel
  • Porsch, D.
Tipo de Documento: Artículo
Título de Revista/Publicación: Annals of Nuclear Energy
Fecha: 12 Mayo 2016
Volumen: 95
Materias:
Palabras Clave Informales: uncertainty analysis, nuclear data, Monte Carlo methods, PWR core analysis, Bayesian inference
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Ingeniería Energética
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 44290
Identificador DC: http://oa.upm.es/44290/
Identificador OAI: oai:oa.upm.es:44290
Identificador DOI: 10.1016/j.anucene.2016.05.007
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0306454916302547
Depositado por: Memoria Investigacion
Depositado el: 09 Ene 2017 16:04
Ultima Modificación: 09 Ene 2017 16:04
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