Sensitivity studies of PWR MOX fuel management to the plutonium initial vector using Artificial Neural Networks

This paper presents new metamodels based on artificial neural networks trained on full core 3D depletion simulations performed with APOLLO2 and CRONOS2. They are used to estimate the irradiation cycle length, discharge burn-up of each fuel assembly type and radial power factor of a PWR loaded with 3...

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Bibliographic Details
Published in:EPJ Web of conferences 2024, Vol.302, p.17008
Main Authors: Eveillard, Sarah, Courtin, Fanny, Doligez, Xavier, Ernoult, Marc, Thiollière, Nicolas, Tirel, Kévin, Vidal, Jean-François
Format: Article
Language:English
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Summary:This paper presents new metamodels based on artificial neural networks trained on full core 3D depletion simulations performed with APOLLO2 and CRONOS2. They are used to estimate the irradiation cycle length, discharge burn-up of each fuel assembly type and radial power factor of a PWR loaded with 30% of MOX fuels, as a function of the initial plutonium composition. They allow to explore the impact of the plutonium isotopic vector on the reactor characteristics and can be used for scenarios studied for future fuel cycle. Some exclusion domains in the plutonium isotopic vector phase space are identified as a function of the cycle length. As an example, the potentialities of such fuel management for plutonium recycling from MOX spent fuel are studied.
ISSN:2100-014X
2100-014X
DOI:10.1051/epjconf/202430217008