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Monte Carlo Source Convergence Diagnosis Method and Thermal-Physics Coupling Method Based on Functional Expansion Tallies

Nan An, Xiaoyu Guo, Hao Luo, Zhaoyuan Liu, Kan Wang

Nuclear Science and Engineering / Volume 199 / Number 1S / April 2025 / Pages S325-S341

Research Article / dx.doi.org/10.1080/00295639.2024.2363575

Received:November 9, 2023
Accepted:May 30, 2024
Published:April 30, 2025

In iterative Monte Carlo calculations for nuclear reactors, the inactive cycles should be calculated first to ensure that the source distribution is converged, and then the tallies of various parameters in the active cycles can begin. In order to acquire the mesh-free distribution of the fission source, this research proposes the functional expansion tallies (FET) source convergence diagnosis method in the Reactor Monte Carlo code, which is a self-developed stochastic simulation code maintained by the Reactor Engineering Analysis Laboratory of Tsinghua University.

Due to the randomness in Monte Carlo calculations and the difficulty in determining the precise source convergence, this paper proposes a diagnostic tool based on function curve similarity and moving average, and proposes an online real-time source convergence diagnosis method. The FET online source convergence method can terminate the calculation of the inactive cycle in real time according to the convergence diagnostic tool; thus it can greatly decrease the calculation time.

The precise and effective transfer of data between different meshes is a difficult issue of thermal and physical coupling. Converting two separate meshes and transferring the data are exceptionally difficult and complex tasks within the conventional nuclear thermal-physics coupling approach. By applying the FET method to nuclear thermal-physics coupling, the mesh-free continuous-space fission source distribution can be obtained, which is suitable for more complex meshes. Additionally, computational memory can be minimized by replacing (transforming) the data from numerous mesh power distribution data points with the coefficients of the function.