| Issue |
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
|
|
|---|---|---|
| Article Number | 17008 | |
| Number of page(s) | 10 | |
| Section | Artificial Intelligence & Digital in Nuclear Applications - Quantum Computing | |
| DOI | https://doi.org/10.1051/epjconf/202430217008 | |
| Published online | 15 October 2024 | |
https://doi.org/10.1051/epjconf/202430217008
Sensitivity studies of PWR MOX fuel management to the plutonium initial vector using Artificial Neural Networks
1 IJCLab, IN2P3-CNRS, Université Paris-Saclay, 91406 Orsay Cedex, France
2 CEA/DES/IRESNE/DER/SPRC/LE2C Cadarache, F-13108 Saint-Paul-Lez-Durance, France
3 Subatech, IMTA-IN2P3/CNRS-Université de Nantes, F-44307 Nantes, France
4 CEA/DES/IRESNE/DER/SPRC/LEPh Cadarache, F-13108 Saint-Paul-Lez-Durance, France
* Corresponding author: sarah.eveillard@cea.fr
Published online: 15 October 2024
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.
© The Authors, published by EDP Sciences, 2024
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