Coupled CLASS and DONJON5 3D full-core calculations and comparison with the neural network approach for fuel cycles involving MOX fueled PWRs. (March 2021)

• Record Type: Journal Article
• Authors: Guillet, Martin; Doligez, Xavier; Marleau, Guy; Paradis, Maxime; Ernoult, Marc; Thiollière, Nicolas
• Is Part Of: Annals of nuclear energy. Volume 152(2021)
• Publication Date: 2021-03
• Subjects: CLASS -- DONJON5 -- Neural Networks -- MOX -- PWR -- Fuel cycle scenario; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2020.107971
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: Coupling scenario code CLASS with DONJON5 3D full-core diffusion calculations. Evaluate errors in CLASS resulting from use of neural networks models based on infinite-assembly simulations. Show how CLASS/DONJON5 simulations for simple and complex scenario affects actinides in storage. Abstract: The scenario code CLASS relies on infinite assembly simulation to predict fuel actinide inventories at exit burnup. In the current work, we replace these assembly calculations by full-core simulations and evaluate the impact on actinide inventories predicted by CLASS. To achieve this goal, we generate neural network training databanks for CLASS using the lattice code DRAGON5. For UOX fuels, the databanks are sampled stochastically for exit burnup, moderator boron concentration and uranium 235 enrichment while for MOX fuels an eight-dimensional grid is sampled that also accounts for plutonium and americium-241 initial contents. DRAGON5 is used to generate the databases for DONJON5 3D full-core diffusion calculations in CLASS. Results obtained using neural networks CLASS and DONJON5/CLASS calculations are then compared to assess the different assumptions used in classical scenario simulations and determine the major source of errors. A simple UOX scenario involving long-term fuel storage and a more complex scenario involving reprocessed UOX spent fuel and MOX fabrication are then studied. They show that inventories of uranium 235 and minor actinides are sensitive to full-core simulations. Moreover, the neural networks CLASS simulations can be improved using an adapted k threshold that depends on the initial fuel composition.