EPJ Web Conf.
Volume 247, 2021PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|Number of page(s)||8|
|Section||Fuel Cycle and Scenarios|
|Published online||22 February 2021|
A NOVEL COMPUTATIONAL PLATFORM FOR THE PROPAGATION OF NUCLEAR DATA UNCERTAINTIES THROUGH THE FUEL CYCLE CODE ANICCA
1 SCK-CEN (Belgian Nuclear Research Centre) Boeretang 200, 2400 Mol, Belgium
2 Universidad Catolica del Maule Av. San Miguel 3605, Talca, Region del Maule, Chile
Published online: 22 February 2021
This paper presents the first results of a computational platform dedicated to the propagation of nuclear data covariances, all the way to fuel cycle scenario observables. Such platform, based on in-house codes developed at SCK•CEN in Belgium, both for the creation of the many-randomized nuclear data libraries based on ENDF format and for fuel cycle scenario-studies (known as SANDY and ANICCA, respectively), was employed for the uncertainty assessment of the time-dependent inventory computed from a mono-recycling of Plutonium scenario based on a PWR fleet. An essential part of the procedure that deals with the creation of input data libraries to ANICCA, has been carried out this time by the SERPENT2 code. Due to the fact that its neutron transport and depletion parallelized calculation in 72 cores for up to 1640 days and 60 MWd/kg-HM takes almost one hour, it is feasible to finish a total of 100 ANICCA runs based on randomized input libraries created from ENDF/B-VII.1 neutron-reaction covariances in about one week. Therefore, it is consider that the computation of the output population statistics can be inferred from 100 observables representing time-dependent mass inventories. To mention a few results from the aforementioned NEA/OECD benchmark scenario, it was found out that the relative standard deviation of the accumulated plutonium in the final disposal after 120 years was of 7%, while for curium it corresponded to 8%. Thus, sources of uncertainty arising from neutron-reaction covariances do have an impact in the final quantitative analysis of the fuel cycle output uncertainties.
Key words: ANICCA / SANDY / fuel cycle / nuclear data covariance / uncertainty analysis
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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