Uncertainty analyses of spent nuclear fuel decay heat calculations using SCALE modules

Decay heat residuals of spent nuclear fuel (SNF), i.e., the differences between calculations and measurements, were obtained previously for various spent fuel assemblies (SFA) using the Polaris module of the SCALE code system. In this paper, we compare decay heat residuals to their uncertainties, focusing on four PWRs and four BWRs. Uncertainties in nuclear data and model inputs are propagated stochastically through calculations using the SCALE/Sampler super-sequence. Total uncertainties could not explain the residuals of two SFAs measured at GE-Morris. The combined z-scores for all SFAs measured at the Clab facility could explain the resulting deviations. Nuclear-data-related uncertainties contribute more in the high burnup SFAs. Design and operational uncertainties tend to contribute more to the total uncertainties. Assembly burnup is a relevant variable as it correlates significantly with the SNF decay heat. Additionally, burnup uncertainty is a major contributor to decay heat uncertainty, and assumptions relating to these uncertainties are crucial. Propagation of nuclear data and design and operational uncertainties shows that the analyzed assemblies respond similarly with high correlation. The calculated decay heats are highly correlated in the PWRs and BWRs, whereas lower correlations were observed between decay heats of SFAs that differ in their burnups. (c) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Development and Application of Data Assimilation Methods in Reactor Physics

Daniel Jerôme Siefman

Simulations of nuclear reactor physics can disagree significantly from experimental evidence, even when the most accurate models are used. An important part of this bias from experiment is caused by nuclear data. The nuclear data have inherent uncertainties due to the way they are evaluated, which then propagate to nuclear reactor simulations. This creates a bias and an uncertainty in a predicted reactor parameter like \keff~or the composition of spent fuel. This thesis focuses on data assimilation techniques to ameliorate the effects of nuclear data. Data assimilation takes integral experiments and assimilates them in a Bayesian way to improve simulations. It can also be used to find trends and areas needing improvement in evaluated nuclear data. The research focuses on advancing the data assimilation theory and knowledge used in reactor physics, especially on techniques that require stochastic sampling of the nuclear data. Furthermore, the research takes advantage of rich experimental data available from the Proteus research reactor at the Paul Scherrer Institute. The thesis showed, for the first time, that two methods based on stochastic sampling (called MOCABA and BMC) gave equivalent results to each other and to the traditional method called GLLS. This was corroborated with two independent studies that used different experiments, neutron transport codes, nuclear data, and processing codes. The first study used the JEZEBEL-Pu239 benchmark, the Serpent2 neutron transport code, and NUSS. The second study used reactivity experiments from the LWR-Phase II experiments at Proteus, CASMO-5 for neutron transport, and SHARK-X. While using Serpent2, several questions pertaining to the stochastic uncertainty of its sensitivity coefficients arose. To address these, a new method called eXtended GLLS, or xGLLS, was proposed and tested in the thesis. xGLLS showed that the uncertainties associated with sensitivity coefficients have a negligible effect on the data assimilation as long as the calculated integral parameters themselves were converged. The final study focused on adjusting the fission yields and covariances made by the GEF code with post-irradiation examination experiments from Proteus. The adjustment improved the accuracy of predicted nuclide concentrations in spent fuel and improved the agreement between the GEF fission yields and those of ENDF/B-VIII.0 and JEFF3.3.

EPFL2019

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

Mathieu Hursin, Andreas Pautz, Dimitri Rochman, Daniel Jerôme Siefman

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.

EDP SCIENCES S A2020

Development and Application of Data Assimilation Methods in Reactor Physics

Daniel Jerôme Siefman

EPFL2019