Inventory calculation and nuclear data uncertainty propagation on light water reactor fuel using ALEPH-2 and SCALE 6.2

Fiorito, Luca; Piedra, D.; Cabellos, Ó.; Díez, C.J.

doi:10.1016/j.anucene.2015.03.046

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…The computational burden of statistical sampling uncertainty propagation through the Monte Carlo models has been reported by many scientific publications (Rochman et al, 2014a;Fiorito et al, 2015;Rochman et al, 2017). A second model, simplified, was therefore designed to reduce the computational time of the uncertainty propagation procedure.…”

Section: Description Of the Assembly Modelsmentioning

confidence: 99%

“…In both cases, the nuclear data-evaluated uncertainty was propagated to the SNF inventory. Nuclear data libraries later than in other studies found in the literature (Fiorito et al, 2015;Rochman et al, 2017) were considered. This is to assess the state of the recent library releases, with particular attention to covariance evaluation for the cross-sections.…”

Section: Introductionmentioning

confidence: 99%

“…The uncertainty given in the form of covariance matrices evaluated by ENDF/B-VIII.0, JEFF-3.3, and JENDL-4.0u was then propagated through the model to SNF composition with the SANDY code (Fiorito et al, 2017), where a stochastic sampling scheme is implemented. This work follows the approach considered in the literature for nuclear data uncertainty propagation to SNF composition (Rochman et al, 2014b;Williams et al, 2014;Díez et al, 2015;Fiorito et al, 2015;Leray et al, 2016;Ilas and Liljenfeldt, 2017;Rochman et al, 2017;Wemple and Zwermann, 2017;Ebiwonjumi et al, 2021;Fiorito et al, 2021;Grimaldi et al, 2022a). The concentration uncertainty results are then thoroughly compared with those reported in Grimaldi et al (2022a).…”

Section: Introductionmentioning

confidence: 99%

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Nuclear data uncertainty quantification on PWR spent nuclear fuel as a function of burnup

et al. 2023

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Nuclear data uncertainty analysis on the spent nuclear fuel inventory was performed on the Takahama-3 NT3G23 assembly, where the sample SF95-4 was irradiated up to a burnup of approximately 36 GWdt according to the SFCOMPO benchmark. The cross-section covariance matrices stored in the ENDF/B-VIII.0, JEFF-3.3 and JENDL-4.0u evaluated nuclear data libraries were propagated with the stochastic sampling algorithms implemented in the SANDY code. A comparison of the concentration uncertainty differences obtained using data from the three libraries is reported. Similarities were found with the fuel composition uncertainty results obtained for the Calvert Cliffs MKP109 sample P SFCOMPO benchmark. Such a similarity was also found when comparing concentration uncertainties along the sample irradiation. Therefore, the main contributors to the concentration uncertainty of a number of nuclides were identified at different burnup levels in the two samples. To complement the similarity analysis, a correlation study of the concentration distributions predicted by the two models was performed. The reported results hint a dominance of the common uncertainty propagation mechanisms over the model differences in the determination of concentration uncertainty.

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Section: Description Of the Assembly Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nuclear data uncertainty quantification on PWR spent nuclear fuel as a function of burnup

et al. 2023

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“…These data are a major interest for the nuclear industry and the study of the fission process. They are used in the calculation and quantification of several observables, in particular for the fuel inventory [1], the burn-up calculation [2] or still for the reactor decay heat after its shutdown. Historically, very important efforts have been made to understand the fission process in a coherent theoretical framework [3].…”

Section: Introductionmentioning

confidence: 99%

Test of models for²³⁵U(n_th,f) charge distributions and their impacts on the covariance analysis

Cheikh

Kessedjian²,

Sérot³

et al. 2023

EPJ Web Conf.

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Fission yields are of major interest for the nuclear industry and the study of the fission process. Fission yields are determined by evaluation process based on the analysis of experimental datasets and completed by phenomenological models. The aim is to provide the best estimation of independent yields of 235U(nth,f) reaction. In this paper, we will focus on the methodology of assessment of the nuclear charge distribution per mass. In the JEFF-3.1.1, the Wahl’s parametrization of the Zp-model is used to complete the experimental knowledge. Nevertheless, the lack of this parametrization consists in the absence of uncertainty information. Consequently, it is impossible to generate the components of independent yield correlation matrix from the Zp-model. In this context, we propose to use the Zp model with a new parametrization to assess the charge distributions. Two different calculations have been developed: the first one is based on a Wahl-like linear approximation of the parameters as a function of mass. The second approach consists to apply a “direct-Zp model” without linear assumptions. This analysis is achieved using the Markov-Chain Monte-Carlo (MCMC) method to adjust the model parameters and then to deduce the covariance of charge distributions. Moreover, with our development, we have directly and consistently described the pre-neutron and post-neutron charge distributions.

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“…Recent works show that 1000 calculations are enough to permit optimal convergence of the statistical moments of significance for any given response function [6][7][8].…”

Section: Cross Section Covariance Librarymentioning

confidence: 99%

Uncertainty Propagation for LWR Burnup Benchmark Using Sampling Based Code Scale/Sampler

Campolina

Frýbort

2018

APP

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Abstract.Sampling based method is adopted in many fields of engineering and it is currently used to propagate uncertainties from physical parameters and from nuclear data, to integral indicators of nuclear systems. The total uncertainty associated with a model simulation is of major importance for safety analysis and to guide vendors about acceptable tolerance limits for nuclear installations parts. This work presents some calculations to propagate uncertainties for a nuclear reactor fuel element modeled in SCALE/TRITON, using the sampling tool SCALE/SAMPLER. Results showed that the influence of input uncertainties on k inf is more pronounced in the fresh core other than the depleted core and the contribution from studied manufacturing uncertainties is smaller than the contribution of nuclear data uncertainties.

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Inventory calculation and nuclear data uncertainty propagation on light water reactor fuel using ALEPH-2 and SCALE 6.2

Cited by 12 publications

References 13 publications

Nuclear data uncertainty quantification on PWR spent nuclear fuel as a function of burnup

Nuclear data uncertainty quantification on PWR spent nuclear fuel as a function of burnup

Test of models for²³⁵U(n_th,f) charge distributions and their impacts on the covariance analysis

Uncertainty Propagation for LWR Burnup Benchmark Using Sampling Based Code Scale/Sampler

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Inventory calculation and nuclear data uncertainty propagation on light water reactor fuel using ALEPH-2 and SCALE 6.2

Cited by 12 publications

References 13 publications

Nuclear data uncertainty quantification on PWR spent nuclear fuel as a function of burnup

Nuclear data uncertainty quantification on PWR spent nuclear fuel as a function of burnup

Test of models for235U(nth,f) charge distributions and their impacts on the covariance analysis

Uncertainty Propagation for LWR Burnup Benchmark Using Sampling Based Code Scale/Sampler

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Product

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Test of models for²³⁵U(n_th,f) charge distributions and their impacts on the covariance analysis