A Covariance Generation Methodology for Fission Product Yields

Terranova, Nicholas; Sérot, O.; Archier, P.; Vallet, Vanessa; Jean, C. De Saint; Sumini, Marco

doi:10.1051/epjconf/201611109003

Cited by 3 publications

(4 citation statements)

References 11 publications

(11 reference statements)

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“…The matrices displayed are the ones associated with the best estimate method analysis. This experimental matrix is different from evaluated fission yields [1,56,57]. For instance in the…”

Section: Appendix C4: Complementary Resultsmentioning

confidence: 95%

“…Measurements of fission product mass yields are required for nuclear fuel cycle studies and reactor calculations. For instance, decay heat calculations which are critical for storage and safety purposes, rely on fission yield mean values and associated covariance matrices [1]. It is then necessary to determine both mean values and the experimental covariance matrix of fission product yields to perform more precise and reliable calculations.…”

Section: Introductionmentioning

confidence: 99%

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Measurements of $$^{233}$$U$$(\text {n}_{\text {th}},\text {f})$$ fission product mass yields with the LOHENGRIN recoil mass spectrometer

et al. 2021

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Numerous measurements of fission product yields were performed since the discovery of the nuclear fission process. However, more precise and reliable fission product yields are requested. Lack of covariance matrices make difficult to use it for specific application purposes such as the propagation uncertainty of decay heat. In this work, we propose to measure independently the fission product mass yields for the whole heavy peak (including the symmetric mass region) for the 233 U(n th , f) reaction. Both average values and experimental covariance is provided. The fission product mass yields are measured with the LOHEN-GRIN recoil mass spectrometer of the ILL using an ionization chamber located at the focal plane. A new procedure of data taking has been developed in order to minimize the biases. Concretely several ionic charges and kinetic energy distributions have been measured for each mass. Particular attention has been considered in the monitoring of the target time evolution. Additional corrections were necessary in the symmetry mass region due to contaminants coming from the LOHENGRIN recoil mass spectrometer. A complex Monte Carlo analysis has been developed in order to better propagate all the uncertainties. The fission product mass yields of the 233 U(n th , f) and its associated covariance matrix has been produced. An overall good agreement has been observed with ENDF/B-VIII.0 in contrast with the JEFF-3.3 evaluation. A precision around 2% for the heavy peak has been measured. The experimental covariance matrix was also computed. In the symmetry mass region, two components were observed in the kinetic energy distribution. One of this component was considered as an artifact and was ruled-out.

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“…The matrices displayed are the ones associated with the best estimate method analysis. This experimental matrix is different from evaluated fission yields [1,56,57]. For instance in the…”

Section: Appendix C4: Complementary Resultsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Measurements of $$^{233}$$U$$(\text {n}_{\text {th}},\text {f})$$ fission product mass yields with the LOHENGRIN recoil mass spectrometer

et al. 2021

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“…The total propagated uncertainty coming from both nuclear data (fission yield and cross sections) and manufacturing data is obtained by applying the process previously described in [8,9]. The covariance matrices used are the same as those used in [9] (read [14,15] for fission yields and [11,[16][17][18] for cross sections). The results obtained by the propagation are described for the three parameters: reactivity, power factors, and isotopic concentrations.…”

Section: Computation Of Simultaneous Nuclear Data and Manufacturing Dmentioning

confidence: 99%

Manufacturing Data Uncertainties Propagation Method in Burn-Up Problems

Frosio

Bonaccorsi

Blaise

2017

Science and Technology of Nuclear Installations

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A nuclear data-based uncertainty propagation methodology is extended to enable propagation of manufacturing/technological data (TD) uncertainties in a burn-up calculation problem, taking into account correlation terms between Boltzmann and Bateman terms. The methodology is applied to reactivity and power distributions in a Material Testing Reactor benchmark. Due to the inherent statistical behavior of manufacturing tolerances, Monte Carlo sampling method is used for determining output perturbations on integral quantities. A global sensitivity analysis (GSA) is performed for each manufacturing parameter and allows identifying and ranking the influential parameters whose tolerances need to be better controlled. We show that the overall impact of some TD uncertainties, such as uranium enrichment, or fuel plate thickness, on the reactivity is negligible because the different core areas induce compensating effects on the global quantity. However, local quantities, such as power distributions, are strongly impacted by TD uncertainty propagations. For isotopic concentrations, no clear trends appear on the results.

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“…The covariance generation methodology effectiveness was already demonstrated in [6,7] and it is summarized in Fig. 1.…”

Section: Covariance Generationmentioning

confidence: 99%

Covariance generation and uncertainty propagation for thermal and fast neutron induced fission yields

et al. 2017

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Abstract. Fission product yields (FY) are fundamental nuclear data for several applications, including decay heat, shielding, dosimetry, burn-up calculations. To be safe and sustainable, modern and future nuclear systems require accurate knowledge on reactor parameters, with reduced margins of uncertainty. Present nuclear data libraries for FY do not provide consistent and complete uncertainty information which are limited, in many cases, to only variances. In the present work we propose a methodology to evaluate covariance matrices for thermal and fast neutron induced fission yields. The semi-empirical models adopted to evaluate the JEFF-3.1.1 FY library have been used in the Generalized Least Square Method available in CONRAD (COde for Nuclear Reaction Analysis and Data assimilation) to generate covariance matrices for several fissioning systems such as the thermal fission of U235, Pu239 and Pu241 and the fast fission of U238, Pu239 and Pu240. The impact of such covariances on nuclear applications has been estimated using deterministic and Monte Carlo uncertainty propagation techniques. We studied the effects on decay heat and reactivity loss uncertainty estimation for simplified test case geometries, such as PWR and SFR pin-cells. The impact on existing nuclear reactors, such as the Jules Horowitz Reactor under construction at CEA-Cadarache, has also been considered.

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A Covariance Generation Methodology for Fission Product Yields

Cited by 3 publications

References 11 publications

Measurements of $$^{233}$$U$$(\text {n}_{\text {th}},\text {f})$$ fission product mass yields with the LOHENGRIN recoil mass spectrometer

Measurements of $$^{233}$$U$$(\text {n}_{\text {th}},\text {f})$$ fission product mass yields with the LOHENGRIN recoil mass spectrometer

Manufacturing Data Uncertainties Propagation Method in Burn-Up Problems

Covariance generation and uncertainty propagation for thermal and fast neutron induced fission yields

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