Uncertainty quantification analysis with ANICCA fuel cycle code

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    Abstract

    While talking about uncertainties is important to bear in mind the context and the final objective of the topic. That is so due to the great variety of uncertainties nuclear research has. These uncertainties can be categorised in four main groups, technological uncertainties, operational uncertainties, numerical calculations uncertainties and nuclear data uncertainties. Here data from all known isotopes and their interactions are contained: interactions with other isotopes, physical properties, reactions, time evolution and correlations can cause variations on output information. This last uncertainty group is the most important one and the one used for this work since although every other uncertainty may be present, only nuclear data uncertainties will be propagated, which means that even if there are other uncertainties, they will not be treated and only data will be perturbed. Throughout this paper we show the work done with the ANICCA fuel cycle code. From the first steps to understand and know how to use the code itself, its characteristics, its structure, the required information, the mode of operation, ... It also includes the study of the Belgian reference scenario. With this study it was observed that it was necessary to update also the ANICCA input that represents this scenario in order to obtain an updated and representative input that could be used for uncertainty analysis or other studies. The workflow for the creation of new reference libraries and how that libraries have been modified to create 300 perturbed libraries will also be explained. ANICCA will be used to carry on an uncertainty study and for that, another section will describe the process followed to run ANICCA as well as the results, uncertainties and mass flows obtained for the most significant variables that have the greatest influence on the back end of the fuel cycle. Finally, the work done with the economic module of ANICCA will also be described.
    Original languageEnglish
    QualificationMaster of Science
    Awarding Institution
    • UPM, Universidad Politécnica de Madrid
    Supervisors/Advisors
    • Romojaro, Pablo, SCK CEN Mentor
    • García-Herranz, Nuria , Supervisor, External person
    Date of Award20 Jul 2023
    Publisher
    StatePublished - 2 Jul 2023

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