Sensitivity studies of PWR MOX fuel management to the plutonium initial vector using Artificial Neural Networks
Year: 2024
Type: article
Abstract: 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.
Authors: Sarah Eveillard, Fanny Courtin, Xavier Doligez, Marc Ernoult, Nicolas Thiollière, Kévin Tirel, Jean-François Vidal
Institutions: Centre National de la Recherche Scientifique, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, CEA Cadarache, Institut National de Physique Nucléaire et de Physique des Particules, Laboratoire de Physique des 2 Infinis Irène Joliot-Curie, Institut de Recherche sur les Systèmes Nucléaires pour la production d'Energie bas carbone, Laboratoire de Physique Subatomique et des Technologies Associées, Université de Nantes, Nantes Université
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6959917150
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