Capturing and utilizing the random feature in Monte Carlo fission source distributions - Shen, Pengfei; Huo, Xiaodong; Huang, Shanfang; Guo, Yuchuan; Shao, Zeng; Yang, Haifeng; Wang, Kan (Annals of nuclear energy, 2023)
•Three mesh-free SW-based methods are proposed to capture the FSD random error term’s 1m feature.•A mesh-free source convergence auto-diagnosis algorithm is introduced utilizing the 1m feature.•The algorithm can automatically save 58% to 81% of the computational time to reach a converged fission source in models with a dominance ratio of around 0.99. The Monte Carlo algorithm commonly adopts the power iteration (PI) process in criticality calculations. Previous studies have shown that random error term in the PI process significantly influences the source convergence diagnosis and variance underestimation phenomenon. This paper uses the Sliced Wasserstein (SW) distance of the fission source distributions (FSDs) to estimate the error terms in the PI process. Three mesh-free SW-based methods are proposed to capture the random error term's 1m feature for the OECD source convergence fissile slab model, sphere array model, and the BEAVRS model. Then, a mesh-free source convergence auto-diagnosis algorithm is introduced based on the methods. The algorithm sets the batch size at five stages. The 1m feature of the random error term is utilized to achieve auto-diagnosis at the target batch size. Numerical calculation results show that the auto-diagnosis algorithm is practical and efficient for source convergence diagnosis and acceleration, which can automatically save 58 % to 81 % of the computational time to reach a converged fission source in models with the dominance ratio of around 0.99.