Propagation of Nuclear Data Uncertainties for PWR Core Analysis

Cabellos, Ó.; Castro, E.; Ahnert, C.; Holgado, C.

doi:10.5516/net.01.2014.709

Cited by 27 publications

(14 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[5]), but up to now, no study demonstrates the impact of nuclear data. In the case of the full core uncertainty propagation, the number of studies for uncertainty propagation is more limited due to the required computer power, see for instance references [6][7][8].…”

Section: Monte Carlo Propagationmentioning

confidence: 99%

Uncertainties for Swiss LWR spent nuclear fuels due to nuclear data

Rochman

Vasiliev

Dokhane

et al. 2018

EPJ Nuclear Sci. Technol.

View full text Add to dashboard Cite

Abstract. This paper presents a study of the impact of the nuclear data (cross sections, neutron emission and spectra) on different quantities for spent nuclear fuels (SNF) from Swiss power plants: activities, decay heat, neutron and gamma sources and isotopic vectors. Realistic irradiation histories are considered using validated core follow-up models based on CASMO and SIMULATE. Two Pressurized and one Boiling Water Reactors (PWR and BWR) are considered over a large number of operated cycles. All the assemblies at the end of the cycles are studied, being reloaded or finally discharged, allowing spanning over a large range of exposure (from 4 to 60 MWd/kgU for ≃9200 assembly-cycles). Both UO 2 and MOX fuels were used during the reactor cycles, with enrichments from 1.9 to 4.7% for the UO 2 and 2.2 to 5.8% Pu for the MOX. The SNF characteristics presented in this paper are calculated with the SNF code. The calculated uncertainties, based on the ENDF/B-VII.1 library are obtained using a simple Monte Carlo sampling method. It is demonstrated that the impact of nuclear data is relatively important (e.g. up to 17% for the decay heat), showing the necessity to consider them for safety analysis of the SNF handling and disposal.

show abstract

Section: Monte Carlo Propagationmentioning

confidence: 99%

Uncertainties for Swiss LWR spent nuclear fuels due to nuclear data

Rochman

Vasiliev

Dokhane

et al. 2018

EPJ Nuclear Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Comparing the results derived by Cabellos et al (2014) to the results presented above, shows that uncertainties in the boron concentration are much larger for TENDL-2012/TMC than for ENDF/B-VII.1/NUDUNA, and also the relative importance of the isotopes differs. This confirms that the choice of a nuclear data evaluation may have a large impact on predictions and their corresponding uncertainties, as has already been demonstrated by Díez et al (2015).…”

Section: Boron Letdown Curve Of a Burnup Cyclementioning

confidence: 56%

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

Castro

Ahnert

Buss³

et al. 2016

Annals of Nuclear Energy

Self Cite

View full text Add to dashboard Cite

A Monte Carlo-based Bayesian inference model 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 burnupdependent 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.

show abstract

“…The soluble boron-free operation can decrease the footprint of a nuclear power plant (NPP) because of the absence of the chemical volume control system (CVCS) and a lower amount of liquid radioactive waste, and further remove the corrosion problem caused by boric acid [5]. The fuel assembly (FA) is based on the Westinghouse 17 × 17 FA with 24 guide thimbles and 1 instrumentation thimble at the center [9,10]. However, the number of control rods is limited and the rods are classified according to their roles: regulating rod, shutdown rod, and others.…”

Section: Introductionmentioning

confidence: 99%

Boron-free small modular pressurized water reactor design with new burnable absorber

Choe

Zheng

Lee

et al. 2016

Int. J. Energy Res.

View full text Add to dashboard Cite

Summary This paper presents a preliminary design of a Small Modular Pressurized Water Reactor (SMPWR) aimed at boron‐free operation which can reduce the size of a nuclear power plant (NPP) and the amount of liquid radioactive waste, and also reduce the corrosion issues caused by boric acid in the coolant. The design parameter limits, such as reactivity swing, axial offset (AO), 3D pin peaking factor (Fq), and the required shutdown margin, have been established for the boron‐free SMPWR. Furthermore, a new ring type of burnable absorber (R‐BA) with Zr–167Er is adopted as a burnable absorber (BA) and HfB2 is adopted as a control rod material to satisfy those design limits without liquid boron. Optimal fuel assemblies (FAs) and loading patterns have been searched through a sensitivity study, and the excess reactivity control capacity and design requirements have been demonstrated to be satisfactory. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Propagation of Nuclear Data Uncertainties for PWR Core Analysis

Cited by 27 publications

References 10 publications

Uncertainties for Swiss LWR spent nuclear fuels due to nuclear data

Uncertainties for Swiss LWR spent nuclear fuels due to nuclear data

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

Boron-free small modular pressurized water reactor design with new burnable absorber

Contact Info

Product

Resources

About