Reducing spectrum-driven uncertainties with variance reduction techniques

    Research output

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    Abstract

    This master’s thesis investigates the use of variance reduction techniques to mitigate spectrumdriven uncertainties in nuclear simulations, focusing on three primary contexts: the TIARA experiment, the KENS shielding experiment, and MYRRHA simulations. In the TIARA experiment, the study explores the potential of an equivalent neutron source for variance reduction in proton-based systems. The accuracy of results was found to be significantly influenced by nuclear data libraries, especially those associated with protons as incident particles. The study also underscores the importance of mesh optimization for efficient and precise simulation. The KENS shielding experiment offered insights into how results could vary based on the selected optimization area. The study conducted several simulations in ADVANTG, adjusting meshes and tallies to assess their impact. The unconventional approach of using non-equivalent neutron sources, as suggested by ADVANTG developers, was also examined and found to be a potentially viable option. In the MYRRHA simulations, the research examined the feasibility of a fixed source in ADVANTG simulations for criticality issues. Despite the source’s complexity and size, significant progress was made by establishing a fixed neutron source for both subcritical and critical modes. The thesis also delves into the effects of reducing uncertainties in the neutron spectrum on irradiation calculations and the determination of isotopic composition post-irradiation. This aspect is crucial as it directly influences the decay time and dismantling of nuclear facilities, as well as the estimated radiation dose received by workers. In summary, this thesis offers valuable insights into the application of variance reduction techniques in nuclear simulations, particularly the use of ADVANTG generated weight windows in proton-driven systems, paving the way for future research in this field.
    Original languageEnglish
    QualificationMaster of Science
    Awarding Institution
    • UPM, Universidad Politécnica de Madrid
    Supervisors/Advisors
    • Çelik, Yurdunaz, SCK CEN Mentor
    • Cabellos, Oscar, Supervisor, External person
    • García-Herranz, Nuria , Supervisor, External person
    • Fiorito, Luca, SCK CEN Mentor
    Date of Award20 Jul 2023
    Publisher
    StatePublished - Jul 2023

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