Improvement of the computational scheme for performing uncertainty analysis through the ANICCA fuel cycle code

SCK•CEN Mentor

Hernandez Solis Augusto, ahsolis@sckcen.be, +32 (0)14 33 21 87

Expert group

Nuclear Systems Physics

Introduction

In recent years, SCK CEN has developed the fuel cycle code known as ANICCA, and is continously performing improvements in order to keep the code up to state-of-the-art standards. One of the most novel capabilities that were added to the code, is related to the possibility of propagating nuclear data uncertainties through its irradation module and, therefore, in the final assessmen of the impact that neutron-induced reaction covariances would have in the final scenario inventory (i.e. uranium, plutnonium, fission products and minor actinides invenotry in final repository, reprocessing facilities, etc.).

The irradiation module of ANICCA constitutes the core computational part of the code; currently, it employs pre-computed one-group macroscopic cross-section libraries related to the type of reactor of interest for a particular scenario analysis. Nowadays, such libraries are computed by means of the best estimate neutron transport code knwon as SERPENT2. This Monte Carlo-based code offers (among many capabilities) the possibility of perfomring depletion calculations in a very fast and accurate way. Moreover, its latest version of the code is able to manage different models to treat different levels of accuracy while estimating the energy deposition along the different materials of the domain of study. Thus, if these different modeling strategies are applied to depletion calculations, there would be a strong impact in the output observables depending on the model of choice. This is due to the fact that the spatial distribution of the energy released per fission event is drectly link to the normalization factor while depleting at constant power.

So far, in the beginnig of 2019 a computational scheme was derived to treat neutron-induced reactions as random variables; practically speaking, this was achieved by the creation of the many randomized nuclear data libraries (based on the ENDF/B-VII.1 data) via the SANDY code. In the end, many pre-computed SERPENT2-based libraries were created to feed ANICCA and to perform a statistical analysis on the inventory related to a PWR UOX+MOX reprocessing scenario. Nevertheless, at that time the SERPENT2 version only allowed to perform depletion calculations by normalizing to a total fission Q-value that assumed local deposition in the fuel (this in fact, has been the prefered way of normalization for inventory predictions by the nuclear industry in the past). This assumption, however, has one drawback when the power is assumed constant while depleting and many random libraries take place: that the computation of the physical reaction rates, based on a normalization factor to a certain power, remains the same for all the perturbed nuclear data libraries (in other words, even if the fission cross-section differs at each SANDY library, the number of fissions per time remains the same when depleting). Therefore, this work would aim at correcting this assumption for the proper propagation of nuclear data uncertainties through closed cycle scenarios.

Objective

The objevtive of this MSc thesis is two-fold:

1) Under nominal conditions, to test the impact that the different energy deposition models (included now in the newest version of SERPENT2) would have in nominal depletion calculations for PWR UOX and MOX. In the end, such nominal libraries would be tested by ANICCA for a closed reprocessing scenario.

2) To create a computational scheme to correct previous assumptions on the way of creating pre-computed libraries with SERPENT2. In the end, a bias in the power can be introduced based on the ratio between each SANDY fission cross-section and the nominal cross-section. This will allow to properly assess the statistical change in the computation of the physical reaction rate with the default energy deposition model.

 

The minimum diploma level of the candidate needs to be

Master of industrial sciences , Master of sciences , Master of sciences in engineering

The candidate needs to have a background in

Physics , Energy engineering, Nuclear engineering