Acropolis: Novel Approach to Single-Pin Depletion Calculations Using Stochastic Optimization

Knowledge of the radionuclide inventory in spent nuclear fuel is important for back-end operations such as fuel transport and storage, but it is also relevant for the postclosure safety case for a deep geological repository. Extensive depletion calculations using neutron transport solvers can be time consuming and resource intensive in the case of characterization of a large number of fuel assemblies. Issues of computational demand are further amplified when the inventory of only a single pin from the assembly is desired. A new approach to speeding up the computational time without significant loss of accuracy is proposed in this work, consisting of simplification of the modeled geometry by means of stochastic optimization. The development of this novel methodology, the Acropolis methodology, is described in detail in this paper. Additionally, extensive benchmark and validation exercises were carried out to present and discuss the advantages and limitations of the proposed method.

乏核燃料中的放射性核素库存（radionuclide inventory）数据，不仅对于燃料运输、储存等核燃料循环后端运营环节至关重要，同时也与深地质处置库的闭后安全论证密切相关。当需要对大量燃料组件开展表征分析时，使用中子输运求解器（neutron transport solvers）开展的大规模燃耗计算往往耗时良久且资源消耗巨大。若仅需获取单根燃料棒的放射性核素库存，计算负荷还会进一步放大。本研究提出一种可在不显著损失计算精度的前提下压缩计算时长的新型方法，其核心思路为通过随机优化（stochastic optimization）手段简化建模几何构型。本文详细阐述了这套名为Acropolis方法的新型方法论的完整开发过程。此外，本文还开展了大量基准校验与验证试验，对所提方法的优势与局限性进行了展示与讨论。