Abstract Uncertainty analysis is a critical requirement in reactor simulation as it is used to quantify the reliability of best-estimate calculation. A comprehensive uncertainty analysis should characterize all sources of uncertainties in a computationally-feasible and scientifically-defendable manner. This manuscript employs a well-established reduced order modeling (ROM) based uncertainty quantification methodology to propagate uncertainties throughout neutronic calculations. ROM relies on recent advances in randomized data mining techniques applied to large data streams. In our proposed implementation, the nuclear data uncertainties are first propagated from multi-group level through lattice physics calculation to generate few-group parameter uncertainties, described using a vector of mean values and a covariance matrix. Employing an ROM-based compression of the covariance matrix, the few-group uncertainties are then propagated through downstream core simulation in a computationally efficient manner. This straightforward approach, albeit efficient as compared to brute force forward and/or adjoint-based methods, often employs a number of assumptions that have been unquestioned in the literature of neutronic uncertainty analysis. This manuscript argues that these assumptions could introduce another source of uncertainty referred to as modeling uncertainties, whose magnitude needs to be quantified in tandem with nuclear data uncertainties. Thus, our primary goal is to explore the interactions between these two uncertainty sources in order to assess whether modeling uncertainties have an impact on parameter uncertainties. To explore this endeavor, the impact of a number of modeling assumptions on core attributes uncertainties is quantified. The study employs a CANDU reactor model, with Serpent and NEWT as lattice physics solvers and NESTLE-C as core simulator. The modeling assumptions investigated include those related with the uncertainty propagation method employed, e.g., deterministic vs. stochastic, the few-group energy structure employed to represent the cross-sections, the resonance treatment in lattice physics calculation, the reference values for the cross-section, and the number of samples employed to render ROM compression. Results indicate that some of the modeling assumptions could have a non-negligible impact on the core responses propagated uncertainties, highlighting the need for a more comprehensive approach to combine parameter and modeling uncertainties.

中文翻译：

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中子学核心模拟中的核数据不确定性传播和建模不确定性影响评估

摘要 不确定性分析是反应堆模拟中的一项关键要求，因为它用于量化最佳估计计算的可靠性。全面的不确定性分析应以计算上可行和科学上可辩护的方式表征所有不确定性来源。这份手稿采用了一种完善的基于降阶建模 (ROM) 的不确定性量化方法，以在整个中子计算中传播不确定性。ROM 依赖于应用于大数据流的随机数据挖掘技术的最新进展。在我们提出的实现中，核数据不确定性首先通过晶格物理计算从多组级别传播，以生成少数组参数不确定性，使用平均值向量和协方差矩阵进行描述。采用基于 ROM 的协方差矩阵压缩，然后以计算效率高的方式通过下游核心模拟传播少数组不确定性。这种直接的方法虽然与蛮力前向和/或基于伴随的方法相比是有效的，但通常采用许多假设，这些假设在中子不确定性分析的文献中是毋庸置疑的。这份手稿认为，这些假设可能会引入另一个不确定性来源，称为建模不确定性，其大小需要与核数据不确定性一起量化。因此，我们的主要目标是探索这两个不确定性来源之间的相互作用，以评估建模不确定性是否对参数不确定性产生影响。为了探索这一努力，一些建模假设对核心属性不确定性的影响被量化。该研究采用 CANDU 反应堆模型，Serpent 和 NEWT 作为晶格物理求解器，NESTLE-C 作为核心模拟器。所研究的建模假设包括与所采用的不确定性传播方法相关的假设，例如确定性与随机性、用于表示横截面的少数群能量结构、晶格物理计算中的共振处理、横截面的参考值部分，以及用于渲染 ROM 压缩的样本数。结果表明，一些建模假设可能对核心响应传播的不确定性产生不可忽视的影响，突出表明需要一种更全面的方法来结合参数和建模不确定性。