This paper presents an approach for the optimisation of geological disposal canister loadings, combining high resolution simulations of used nuclear fuel characteristics with an articial neural network and a genetic algorithm. The used nuclear fuels (produced in an open fuel cycle without reprocessing) considered in this work come from a Swiss Pressurised Water Reactor, taking into account their realistic lifetime in the reactor core and cooling periods, up to their disposal in the final geological repository. The case of 212 representative used nuclear fuel assemblies is analysed, assuming a loading of 4 fuel assemblies per canister, and optimizing two safety parameters: the fuel decay heat (DH) and the canister effective neutron multiplication factor k\(_{\mathrm{eff}}\). In the present approach, a neural network is trained as a surrogate model to evaluate the k\(_{\mathrm{eff}}\) value to substitute the time-consuming-code Monte Carlo transport & depletion SERPENT for specific canister loading calculations. A genetic algorithm is then developed to optimise simultaneously the canister k\(_{\mathrm{eff}}\) and DH values. The k\(_{\mathrm{eff}}\) computed during the optimisation algorithm is using the previously developed artificial neural network. The optimisation algorithm allows (1) to minimize the number of canisters, given assumed limits for both DH and k\(_{\mathrm{eff}}\) quantities and (2) to minimize DH and k\(_{\mathrm{eff}}\) differences among canisters. This study represents a proof-of-principle of the neural network and genetic algorithm capabilities, and will be applied in the future to a larger number of cases.

本文提出了一种优化地质处置罐装载的方法，将使用过的核燃料特性的高分辨率模拟与人工神经网络和遗传算法相结合。这项工作中考虑的用过的核燃料（在开放式燃料循环中生产，未经后处理）来自瑞士压水反应堆，考虑到它们在反应堆堆芯中的实际寿命和冷却期，直到它们在最终地质处置库中的处置。分析了212个具有代表性的使用过的核燃料组件的案例，假设每个罐装载4个燃料组件，并优化两个安全参数：燃料衰变热（DH）和罐有效中子倍增因子k \(_{\mathrm{效果}}\). 在本方法中，神经网络被训练为替代模型来评估k \(_{\mathrm{eff}}\)值，以替代耗时代码 Monte Carlo 传输和消耗 SERPENT 用于特定的罐加载计算. 然后开发遗传算法以同时优化罐k \(_{\mathrm{eff}}\)和 DH 值。在优化算法期间计算的k \(_{\mathrm{eff}}\)使用先前开发的人工神经网络。优化算法允许 (1) 最小化罐的数量，给定 DH 和k \(_{\mathrm{eff}}\)数量的假设限制和 (2) 最小化 DH 和k\(_{\mathrm{eff}}\)罐之间的差异。这项研究代表了神经网络和遗传算法能力的原理证明，并将在未来应用于更多案例。