Abstract This manuscript addresses a key technical gap in cross-section adjustment and uncertainty analysis research employed in support of neutronic model validation and data assimilation. The key assumption in this body of research is that cross-sections, in condensed group format, are responsible for the key sources of uncertainties in responses of interest, e.g., multiplication factor, flux and power distribution, etc. In reality, the condensed group cross-sections are contaminated not only by epistemic uncertainties, propagated from ENDF, but also by the various modeling assumptions and approximations, typically customizing the condensed cross-sections to the reactor type and flux spectra. Thus, any reliable procedure employed to identify or adjust for key sources of uncertainties must be able to distinguish between epistemic uncertainties and modeling errors. This manuscript proposes a new algorithm that adds new types of constraints to conventional sensitivity analysis procedure to ensure that uncertainty analysis and cross-section adjustment studies are not contaminated by the modeling errors. The proposed approach will directly benefit research and development efforts in many areas of analysis relying on uncertainty and sensitivity methods, such as criticality safety, core monitoring, and analysis of first-of-a-kind reactor and fuel concept technologies. Demonstrative examples using a CANDU-6 core model during steady state and transient conditions are employed to exemplify application of the proposed methodology.

中文翻译：

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建模误差保留约束敏感性分析

摘要 本手稿解决了用于支持中子模型验证和数据同化的横截面调整和不确定性分析研究中的关键技术差距。该研究主体的关键假设是，压缩群格式的横截面是感兴趣响应不确定性的关键来源，例如倍增因子、通量和功率分布等。实际上，压缩群横截面不仅受到来自 ENDF 的认知不确定性的污染，而且还受到各种建模假设和近似的污染，通常将冷凝横截面定制为反应器类型和通量谱。因此，用于识别或调整关键不确定性来源的任何可靠程序都必须能够区分认知不确定性和建模错误。这份手稿提出了一种新算法，该算法为传统的敏感性分析程序添加了新的约束类型，以确保不确定性分析和横截面调整研究不受建模错误的影响。提议的方法将直接有利于依赖不确定性和敏感性方法的许多分析领域的研发工作，例如临界安全、堆芯监测以及对一流反应堆和燃料概念技术的分析。在稳态和瞬态条件下使用 CANDU-6 核心模型的示范示例被用来举例说明所提出的方法的应用。这份手稿提出了一种新算法，该算法为传统的灵敏度分析程序添加了新类型的约束，以确保不确定性分析和横截面调整研究不会受到建模错误的影响。提议的方法将直接有利于依赖不确定性和敏感性方法的许多分析领域的研发工作，例如临界安全、堆芯监测以及对一流反应堆和燃料概念技术的分析。在稳态和瞬态条件下使用 CANDU-6 核心模型的示范示例被用来举例说明所提出的方法的应用。这份手稿提出了一种新算法，该算法为传统的敏感性分析程序添加了新的约束类型，以确保不确定性分析和横截面调整研究不受建模错误的影响。提议的方法将直接有利于依赖不确定性和敏感性方法的许多分析领域的研发工作，例如临界安全、堆芯监测以及对一流反应堆和燃料概念技术的分析。在稳态和瞬态条件下使用 CANDU-6 核心模型的示范示例被用来举例说明所提出的方法的应用。