**Êtes-vous un étudiant de l'EPFL à la recherche d'un projet de semestre?**

Travaillez avec nous sur des projets en science des données et en visualisation, et déployez votre projet sous forme d'application sur GraphSearch.

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

Résumé

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/).

Source officielle

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (1)

Concepts associés (7)

Spent nuclear fuel

Spent nuclear fuel, occasionally called used nuclear fuel, is nuclear fuel that has been irradiated in a nuclear reactor (usually at a nuclear power plant). It is no longer useful in sustaining a nuclear reaction in an ordinary thermal reactor and, depending on its point along the nuclear fuel cycle, it will have different isotopic constituents than when it started. Nuclear fuel rods become progressively more radioactive (and less thermally useful) due to neutron activation as they are fissioned, or "burnt" in the reactor.

Nuclear data

Nuclear data represents measured (or evaluated) probabilities of various physical interactions involving the nuclei of atoms. It is used to understand the nature of such interactions by providing the fundamental input to many models and simulations, such as fission and fusion reactor calculations, shielding and radiation protection calculations, criticality safety, nuclear weapons, nuclear physics research, medical radiotherapy, radioisotope therapy and diagnostics, particle accelerator design and operations, geological and environmental work, radioactive waste disposal calculations, and space travel calculations.

Principe d'incertitude

En mécanique quantique, le principe d'incertitude ou, plus correctement, principe d'indétermination, aussi connu sous le nom de principe d'incertitude de Heisenberg, désigne toute inégalité mathématique affirmant qu'il existe une limite fondamentale à la précision avec laquelle il est possible de connaître simultanément deux propriétés physiques d'une même particule ; ces deux variables dites complémentaires peuvent être sa position (x) et sa quantité de mouvement (p).

Characteristics of the spent nuclear fuel (SNF) are typically calculated, requiring validation a priori. The validation process relies on the difference between calculations and measurements, namely the bias. Usually, predicting the bias based on benchmarks is essential, which motivated the present research, focusing on SNF decay heat and Cs-137, U-235, and Pu-239 concentrations.The validation benchmarks are from open-literature, i.e., SNF design and irradiation specifications, as well as the measurements of their characteristics. For the decay heat, they correspond to 262 measurements, conducted at the Clab and the GE-Morris facilities. For the radionuclide concentrations, they are 285 post-irradiation-examination samples, obtained from the SFCOMPO database. The calculations rely on the SCALE code system, namely the Polaris code and the SCALE-based nuclear data.Uncertainties of nuclear data and SNF design and operational history are propagated to the calculated quantities, for two purposes: (1) to assess if the biases are statistically significant, given the calculated uncertainties, and (2) to obtain correlation matrices between the benchmarks. Statistical analyses, resampling and z-tests, are applied on the validation and uncertainty analyses data. They indicate that the biases in several of the analyzed characteristics are significant with respect to uncertainties in the calculated values. For the decay heat case, the biases are considered not significant considering both the calculated and experimental uncertainties. It is also shown that it is crucial to include the correlations between the benchmarks into the hypothesis testing.Then, a novel approach is followed, by applying machine learning (ML) methods to predict the bias of calculated SNF characteristics. The predictive performance is analyzed by comparing the ML-based bias predictions and the validation-based biases. The analyzed ML models predict the bias using highly similar benchmarks or neighbors of the benchmarks, namely Random Forests (RF) and Weighted k-Nearest Neighbors (KKNN). Also, the linear model is analyzed.This research shows that the bias of the decay heat and Pu-239 concentration can be predicted with a reasonable accuracy, relying on specific features of validation benchmarks, or their correlations. The predicted biases bear statistically significant similarities to the observed ones from the validation procedure, using both the RF and the KKNN models. The variances in the original validation data are significantly reduced. The models predict the bias using the spectral index for the decay heat and the hydrogen-to-fissile atom ratio for the Pu-239 concentration. Also, the correlation matrices show that they are informative in predicting the bias of both characteristics. In the case of the U-235 and Cs-137 concentrations, biases could not be satisfactorily predicted. Additionally, the linear models have shown unsatisfactory performance.