Benchmarking of the nuclear data uncertainty quantification capabilities of the SANDY code

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    Abstract

    Nuclear safety is one of the most popular concern about nuclear energy and, for this reason, knowing the safety-related parameters, at design stage, is fundamental to reduce substantially the risk. However, especially in the nuclear field, the uncertainty sources are very widespread and, as consequence, computing with accuracy important parameters results very difficult. Here, the the effective multiplication factor (keff) uncertainty due to only nuclear data uncertainties is investigated. In order to perform it, different methods has been developed in the past, but the stochastic Monte Carlo method is chosen for this Thesis. The Monte Carlo method, for uncertainty propagation, has become lately attractive because of faster computation skill (and so better performance) of the new computers, since it requires usually huge computational costs. Moreover, it can compute uncertainties that with traditional methods cannot be computed with accuracy due to their assumptions and simplifications. To be applied, samples of nuclear data must be produced and SANDY has been developed to perform this task. SANDY can work easily with cross-sections data while, for the nubar, there is not a clear method implemented. With the following Thesis, the nubar (and the method for the energy distributions is started) is added to the already-existing methods of SANDY and, after it, the sampling is verified. Furthermore the uncertainty quantification/propagation on integral benchmarks keff, due to the nubar uncertainty and using the Monte Carlo method, is analysed as well as the simultaneous effect of cross-sections and nubar nuclear data, comparing the results with already existing methods such as the sandwich rule and NDaST. In this way, the uncertainty quantification/propagation can be easily performed using SANDY, also for the nubar, and the results on integral responses, such as the keff or power density, can be useful for improving the nuclear facilities safety as well as helping the nuclear data evaluators to create increasingly reliable data.
    Original languageEnglish
    QualificationMaster of Science
    Awarding Institution
    • Politecnico di Torino
    Supervisors/Advisors
    • Fiorito, Luca, SCK CEN Mentor
    • Romojaro, Pablo, SCK CEN Mentor
    • Dulla, Sandra, Supervisor, External person
    Date of Award7 Dec 2023
    Publisher
    StatePublished - 7 Dec 2023

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