The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards. State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics with reduced computational costs compared to physics-based models. An NN is trained using data generated from CASMO5 lattice calculations. The trained NN accurately predicts decay heat and nuclide concentrations of SNF, as a function of key input parameters, such as enrichment, burnup, cooling time between cycles, mean boron concentration and fuel temperature. The model is validated against physics-based decay heat simulations and measurements of different uranium oxide fuel assemblies from two different pressurized water reactors. In addition, the NN is used to perform sensitivity analysis and uncertainty quantification. The results are in very good alignment to CASMO5, while the computational costs (taking into account the costs of generating training samples) are reduced by a factor of 10 or more. Our findings demonstrate the feasibility of using NNs as surrogate models for fast characterization of SNF, providing a promising avenue for improving computational efficiency in assessing nuclear fuel behavior and associated risks.

翻译：乏燃料特性的精确计算与不确定性量化对于确保核能生产、废物管理及核安全保障的安全性、效率和可持续性具有关键作用。当前基于物理模型的先进方法虽可靠，但计算量大且耗时。本文提出一种基于神经网络（NN）的替代建模方法，相较于物理模型，能在降低计算成本的同时预测多项乏燃料特性。利用CASMO5晶格计算生成的数据训练神经网络。训练后的神经网络能精准预测乏燃料的衰变热与核素浓度，这些预测结果随富集度、燃耗、循环间冷却时间、平均硼浓度及燃料温度等关键输入参数变化。模型通过与两种压水堆中不同铀氧化物燃料组件的物理衰变热模拟及测量数据对比得到验证。此外，神经网络还被用于灵敏度分析与不确定性量化。结果表明，神经网络与CASMO5结果高度吻合，而计算成本（含训练样本生成成本）降低至原十分之一或更低。本研究证实了将神经网络作为替代模型用于乏燃料快速表征的可行性，为提升核燃料行为及风险相关评估的计算效率提供了新途径。