Long-term operation of nuclear steam generators can result in the occurrence of clogging, a deposition phenomenon that may increase the risk of mechanical and vibration loadings on tube bundles and internal structures as well as potentially affecting their response to hypothetical accidental transients. To manage and prevent this issue, a robust maintenance program that requires a fine understanding of the underlying physics is essential. This study focuses on the utilization of a clogging simulation code developed by EDF R\&D. This numerical tool employs specific physical models to simulate the kinetics of clogging and generates time dependent clogging rate profiles for particular steam generators. However, certain parameters in this code are subject to uncertainties. To address these uncertainties, Monte Carlo simulations are conducted to assess the distribution of the clogging rate. Subsequently, polynomial chaos expansions are used in order to build a metamodel while time-dependent Sobol' indices are computed to understand the impact of the random input parameters throughout the whole operating time. Comparisons are made with a previous published study and additional Hilbert-Schmidt independence criterion sensitivity indices are computed. Key input-output dependencies are exhibited in the different chemical conditionings and new behavior patterns in high-pH regimes are uncovered by the sensitivity analysis. These findings contribute to a better understanding of the clogging phenomenon while opening future lines of modeling research and helping in robustifying maintenance planning.

翻译：核蒸汽发生器的长期运行可能导致堵塞现象的发生，这是一种沉积现象，可能增加管束及内部结构承受机械载荷和振动载荷的风险，并可能影响其对假想事故瞬态工况的响应。为管理和预防此问题，需要制定一项基于对内在物理过程深入理解的稳健维护计划。本研究聚焦于使用法国电力集团研发部门开发的堵塞模拟代码。该数值工具采用特定物理模型来模拟堵塞动力学，并为特定蒸汽发生器生成随时间变化的堵塞速率曲线。然而，该代码中的某些参数存在不确定性。为处理这些不确定性，我们进行了蒙特卡洛模拟以评估堵塞速率的分布。随后，采用多项式混沌展开构建元模型，并计算了时变Sobol'指数以理解随机输入参数在整个运行时间内的影响。与先前发表的研究进行了对比，并额外计算了基于Hilbert-Schmidt独立性准则的敏感性指数。在不同化学工况下揭示了关键的输入-输出依赖关系，敏感性分析还发现了高pH环境下的新行为模式。这些发现有助于更深入地理解堵塞现象，同时为未来建模研究开辟了新方向，并有助于制定更稳健的维护计划。