Similarity Coefficient Generation Using Adjoint-Based Sensitivity and Uncertainty Method and Stochastic Sampling Method - Ho Jin Park; Jeong Woo Park (Energies, 2024)
In this study, a similarity coefficient generation code system was established using the Monte Carlo Code for Advanced Reactor Design (McCARD) transport code and the MIG multi-correlated input sampling code. We considered the adjoint-based sensitivity and uncertainty (S/U) and stochastic sampling (S.S.) approaches to the generation of the ck similarity coefficient. To examine the code system, the ck similarity coefficients of 23 relevant critical experiments and the System-Integrated Modular Advanced Reactor (SMART) small modular reactor (SMR) target system were generated using ENDF/B-VII.1 covariance data with the Los Alamos National Laboratory (LANL) 30-group energy group structure. Our results show that the similarity coefficients between the 16 LEU thermal-spectrum-based critical experiments and SMART are more than 0.90, which is the recommended criterion of the U.S. Nuclear Regulatory Commission (NRC). These results are very helpful for licensees and can be used to justify the determination of critical experiment benchmarks for computational bias estimations of the SMART target system. To examine the discrepancy in the similarity coefficient, ck, due to the covariance data, similarity analyses for a 24 × 24 benchmark matrix were performed using ENDF-VIII.0, JENDL-5.0, and JEFF-3.3 covariance data. The results show that the selection of the covariance data used for ck generation significantly impacts the similarity coefficient. Moreover, it was observed that the current results for the SCALE 6.1 covariance data show a consistent trend with the results reported in earlier studies.