Propagation of Neutron Cross Section, Fission Yield, and Decay Data Uncertainties in Depletion Calculations

Martínez, J.S.; Zwermann, W.; Gallner, L.; Puente-Espel, F.; Cabellos, Ó.; Velkov, K.; Hannstein, Volker

doi:10.1016/j.nds.2014.04.112

Cited by 10 publications

(8 citation statements)

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“…Only for 155 Eu and 155 Gd higher values of up to 35% are obtained. Decay data uncertainties induce additional sizable uncertainties for the 137 Cs, 151 Eu, 151 Sm, and 241 Pu isotopes, and the results for the uncertainties 300 years after shutdown are in good agreement with the ones obtained by Martínez et al (2014).…”

Section: Discussionsupporting

confidence: 85%

Comparison of nuclear data uncertainty propagation methodologies for PWR burn-up simulations

Díez

Buss²,

Hoefer³

et al. 2015

Annals of Nuclear Energy

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Several methodologies using different levels of approximations have been developed for propagating nuclear data uncertainties in nuclear burn-up simulations. Most methods fall into the two broad classes of Monte Carlo approaches, which are exact apart from statistical uncertainties but require additional computation time, and first order perturbation theory approaches, which are efficient for not too large numbers of considered response functions but only applicable for sufficiently small nuclear data uncertainties. Some methods neglect isotopic composition uncertainties induced by the depletion steps of the simulations, others neglect neutron flux uncertainties, and the accuracy of a given approximation is often very hard to quantify. In order to get a better sense of the impact of different approximations, this work aims to compare results obtained based on different approximate methodologies with an exact method, namely the NUDUNA Monte Carlo based approach developed by AREVA GmbH. In addition, the impact of different covariance data is studied by comparing two of the presently most complete nuclear data covariance libraries (ENDF/B-VII.1 and SCALE 6.0), which reveals a high dependency of the uncertainty estimates on the source of covariance data. The burn-up benchmark Exercise I-1b proposed by the OECD expert group "Benchmarks for Uncertainty Analysis in Modeling (UAM) for the Design, Operation and Safety Analysis of LWRs" is studied as an example application. The burn-up simulations are performed with the SCALE 6.0 tool suite.

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Section: Discussionsupporting

confidence: 85%

Comparison of nuclear data uncertainty propagation methodologies for PWR burn-up simulations

Díez

Buss²,

Hoefer³

et al. 2015

Annals of Nuclear Energy

Self Cite

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“…all weights were zero valued. Secondly, we observed that MLO lowered the χ 2 i values and subsequently decreased the spread of the weights, which then led to a greater degree of weight degeneracy (as evidenced by the CDFs). Simply put, BFMC's weight definition, where each χ 2 i is normalized by χ 2 min , changed the weight distribution.…”

Section: Posterior Model Parametersmentioning

confidence: 80%

“…With this prior and the likelihood, the posterior model parameters' distribution, p(σ |E), is given by equation (2). The DA methods used in this study estimate the maximum a posteriori distribution with two different approaches, which are outlined in the subsequent sections.…”

Section: Data Assimilation Theorymentioning

confidence: 99%

“…The MOCABA DA method finds the maximum a posteriori distribution by assuming that the prior and likelihood are multivariate Gaussian PDFs. This creates a conjugate prior and allows for an analytical solution of equation (2). While reference [25] states that an invertible variable transformation could be used to handle non-Gaussian PDFs, this technique was not pursued and the standard methodology of MOCABA most commonly encountered in the literature was used.…”

Section: Monte Carlo Bayesian Analysis (Mocaba)mentioning

confidence: 99%

“…Among nuclear data, fission yields (FYs) are very important for burn-up [1][2][3][4][5], decay heat [6,7], and nuclear waste management simulations [8]. These simulations need to accurately predict the concentration of fission products (FPs) in spent fuel, which requires reliable FY data with high quality covariances.…”

Section: Introductionmentioning

confidence: 99%

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Data assimilation of post-irradiation examination data for fission yields from GEF

Siefman

Hursin

Sjöstrand

et al. 2020

EPJ Nuclear Sci. Technol.

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Nuclear data, especially fission yields, create uncertainties in the predicted concentrations of fission products in spent fuel which can exceed engineering target accuracies. Herein, we present a new framework that extends data assimilation methods to burnup simulations by using post-irradiation examination experiments. The adjusted fission yields lowered the bias and reduced the uncertainty of the simulations. Our approach adjusts the model parameters of the code GEF. We compare the BFMC and MOCABA approaches to data assimilation, focusing especially on the effects of the non-normality of GEF’s fission yields. In the application that we present, the best data assimilation framework decreased the average bias of the simulations from 26% to 14%. The average relative standard deviation decreased from 21% to 14%. The GEF fission yields after data assimilation agreed better with those in JEFF3.3. For Pu-239 thermal fission, the average relative difference from JEFF3.3 was 16% before data assimilation and after it was 12%. For the standard deviations of the fission yields, GEF’s were 100% larger than JEFF3.3’s before data assimilation and after were only 4% larger. The inconsistency of the integral data had an important effect on MOCABA, as shown with the Marginal Likelihood Optimization method. When the method was not applied, MOCABA’s adjusted fission yields worsened the bias of the simulations by 30%. BFMC showed that it inherently accounted for this inconsistency. Applying Marginal Likelihood Optimization with BFMC gave a 2% lower bias compared to not applying it, but the results were more poorly converged.

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Generation of fission yield covariances to correct discrepancies in the nuclear data libraries

Fiorito

Stankovskiy

Eynde

et al. 2016

Annals of Nuclear Energy

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Propagation of Neutron Cross Section, Fission Yield, and Decay Data Uncertainties in Depletion Calculations

Cited by 10 publications

References 0 publications

Comparison of nuclear data uncertainty propagation methodologies for PWR burn-up simulations

Comparison of nuclear data uncertainty propagation methodologies for PWR burn-up simulations

Data assimilation of post-irradiation examination data for fission yields from GEF

Generation of fission yield covariances to correct discrepancies in the nuclear data libraries

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