An application of sensitivity and representativity approach for the design of a 100%MOX BWR experimental program in ZPR

Blaise, Patrick; Fougeras, Philippe; Cathalau, S.

doi:10.1016/j.anucene.2021.108566

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…where S are the nuclide-reaction-energy sensitivity vectors (S V : VENUS-F assemblies; S M : MYRRHA assembly) and Σ nd is the nuclear data covariance matrix associated with the evaluated nuclear data library. This equation was already used in the frame of nuclear reactor representativity study [3] and is in principle applicable to any parameter for which sensitivities can be computed (e.g. : k ∞ , β eff ).…”

Section: Representativitymentioning

confidence: 99%

Application of nuclear data covariance matrices to representativity calculations

Grimaldi,

Blaise,

Fiorito

et al. 2024

EPJ Web of Conf.

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The neutronics design of new reactors has been historically relying on zero power research reactors for the simulation of the core irradiation conditions as well as for nuclear data and code validation. In this framework, a study on the possible MYRRHA representativity improvement coming from the loading of MOX fuel in VENUS-F zero power reactor is under investigation. This work analyses the effect of the nuclear data used in the sensitivity and representativity calculations, highlighting the need for a comprehensive set of nuclides and reactions to be further studied.

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Section: Representativitymentioning

confidence: 99%

Application of nuclear data covariance matrices to representativity calculations

Grimaldi,

Blaise,

Fiorito

et al. 2024

EPJ Web of Conf.

Self Cite

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“…Several measures of model similarity were found in the literature. To consider the effect of the nuclear data uncertainties, the representativity coefficient r is defined as in Blaise et al (2021):…”

Section: Representativity Studymentioning

confidence: 99%

“…where S is the sensitivity vector of the observable to the nuclear data, Σ nd is the nuclear data covariance matrix and subscripts, and T and C indicate the samples obtained from Takahama and Calvert Cliffs, respectively. As reported in Blaise et al (2021), this equals the correlation between the concentrations predicted by the two models, being the numerator the covariance of the two model responses and the denominator a re-normalization to the Concentration uncertainty evolution with burnup for nuclides 244 Cm (top) and 242 Pu (bottom).…”

Section: Representativity Studymentioning

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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Editorial. Special Section on “A tribute to Massimo Salvatores, a great scientist and a great man”; Edited by: Giuseppe Palmiotti, Mario Carta, Stefano Monti, Temitope Taiwo, Toshikazu Takeda

Ravetto

2022

Annals of Nuclear Energy

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An application of sensitivity and representativity approach for the design of a 100%MOX BWR experimental program in ZPR

Cited by 3 publications

References 12 publications

Application of nuclear data covariance matrices to representativity calculations

Application of nuclear data covariance matrices to representativity calculations

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

Editorial. Special Section on “A tribute to Massimo Salvatores, a great scientist and a great man”; Edited by: Giuseppe Palmiotti, Mario Carta, Stefano Monti, Temitope Taiwo, Toshikazu Takeda

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