Development of a generalized perturbation theory model for uncertainty propagation in lattice cell codes

本研究计划的主要目标是开发一种程序，该程序可用于通过DRAGON代码传播基本核数据中的不确定性。包含这种不确定性分析特征的晶格单元码将清楚地代表比当前分析码的一个量子步骤，因为人们将能够评估实验核数据中的不确定性对总体反应堆运行参数的影响。我们建议开发的敏感性系数评估程序将基于直接方法，但也基于蒙特卡罗和更有效的伴随技术。后一种方法是基于已经在DRAGON代码中实现的广义摄动理论（GPT）方法。我们还将讨论推导GPT方法来评估与DRAGON中编程的共振自屏蔽模型相关的灵敏度系数的问题。最后，将在DRAGON中定义并实现一个包含协方差矩阵的新截面库。这项工作将导致用于反应堆物理计算的分析工具的质量和多功能性的改进，这样它们也可以满足下一代反应堆（先进CANDU和第四代）的分析需求。由于截面误差导致的堆芯功率分布精度的误差限制可以使用这些工具进行评估，这一事实应该会大大降低反应堆安全研究中的随意性水平。

The main objective of this research proposal consists of developing a procedure that can be used to propagate through the code DRAGON the uncertainties in basic nuclear data. A lattice cell code that includes such uncertainty analysis features will clearly represent a quantum step over the current analysis codes since one will then be able to evaluate the effect of uncertainties in the experimental nuclear data on global reactor operation parameter. The procedures for sensitivity coefficient evaluation we propose to develop will be based on a direct method but also on a Monte Carlo and a more efficient adjoint technique. This later method is based on the generalized perturbation theory (GPT) method already implemented in the code DRAGON. We will also address the problem of deriving a GPT approach to evaluate the sensitivity coefficients associated with the resonance self-shielding model programmed in DRAGON. Finally, a new cross section library that includes covariance matrices will be defined and implemented in DRAGON. This work will lead to improvements in the quality and versatility of the analysis tools that are used for reactor physics calculations in such a way that they can also satisfy the analysis needs of the next generations of reactors (Advanced CANDU and Generation IV). The fact that the errors limits in the precision of the core power distributions due to errors in the cross sections can be evaluated using these tools should reduce substantially the level of arbitrariness in reactor safety studies.