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Coupled CLASS and DONJON5 3D full-core calculations and comparison with the neural network approach for fuel cycles involving MOX fueled PWRs
Annals of Nuclear Energy ( IF 1.9 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.anucene.2020.107971
Martin Guillet , Xavier Doligez , Guy Marleau , Maxime Paradis , Marc Ernoult , Nicolas Thiollière

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.

中文翻译:

耦合 CLASS 和 DONJON5 3D 全核计算,并与涉及 MOX 燃料压水堆的燃料循环的神经网络方法进行比较

摘要 场景代码 CLASS 依赖于无限装配模拟来预测出口燃耗时的燃料锕系库存。在当前的工作中,我们用全核模拟代替了这些组装计算,并评估了对 CLASS 预测的锕系元素库存的影响。为了实现这一目标,我们使用网格代码 DRAGON5 为 CLASS 生成神经网络训练数据库。对于 UOX 燃料,数据库是随机采样的,用于出口燃耗、慢化剂硼浓度和铀 235 浓缩,而对于 MOX 燃料,则对八维网格进行采样,其中还考虑了钚和镅 241 的初始含量。DRAGON5 用于生成 CLASS 中 DONJON5 3D 全核扩散计算的数据库。然后将使用神经网络 CLASS 和 DONJON5/CLASS 计算获得的结果进行比较,以评估经典场景模拟中使用的不同假设并确定错误的主要来源。然后研究了一个涉及长期燃料储存的简单 UOX 情景和一个涉及 UOX 乏燃料后处理和 MOX 制造的更复杂的情景。他们表明铀 235 和次锕系元素的库存对全核模拟很敏感。此外,可以使用取决于初始燃料成分的自适应 k 阈值来改进神经网络 CLASS 模拟。然后研究了一个涉及长期燃料储存的简单 UOX 情景和一个涉及 UOX 乏燃料后处理和 MOX 制造的更复杂的情景。他们表明铀 235 和次锕系元素的库存对全核模拟很敏感。此外,神经网络 CLASS 模拟可以使用取决于初始燃料成分的自适应 k 阈值来改进。然后研究了一个涉及长期燃料储存的简单 UOX 方案和一个涉及 UOX 乏燃料后处理和 MOX 制造的更复杂方案。他们表明铀 235 和次锕系元素的库存对全核模拟很敏感。此外,神经网络 CLASS 模拟可以使用取决于初始燃料成分的自适应 k 阈值来改进。
更新日期:2021-03-01
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