Advancing Uncertainty Reduction: Applications for ACCRUE Relevance in Nuclear Criticality Safety

In compliance with nuclear criticality safety standards, such as those established by ANSI/ANS standards, the validation of neutron transport calculation codes is essential to ensure the accurate prediction of application bias. This model validation process relies heavily on uncertainty reduction, which enables systematic refinement of prior knowledge. A thorough understanding of the relationship between experimental and application models is critical for estimating the achievable reduction in application uncertainty before conducting new experiments. However, the conventional C_k similarity index, widely used to evaluate the relationship between experiments and applications, has notable limitations. Specifically, C_k does not adequately account for the combined impact of multiple experiments or the influence of measurement uncertainties. To address these limitations, the authors previously introduced the relevance metric j_k, which maps application information onto the experimental subspace while incorporating both past experiments and measurement uncertainties. This paper extends the investigation into the diverse applications of the j_k metric, highlighting its role in quantifying application coverage and facilitating uncertainty reduction. By leveraging the j_k metric, the study demonstrates how to identify experiments that provide the most significant contributions to reducing uncertainty in model predictions. Through the application of simplified toy models and real-world criticality benchmarks from the ICSBEP handbook, this work showcases the practical advantages of the j_k relevance metric in enhancing the reliability and accuracy of nuclear engineering models.