Abstract
Method of Characteristics (MOC) is a transport-solving method with high numerical accuracy and geometric flexibility, but it is computationally intensive. Since parameters have a significant impact on both computational accuracy and speed, optimization and selection of MOC parameters are necessary. This optimization is challenging due to the large number of parameters involved, the nonlinearities among parameters and objectives, and the contradictory relationship between different objectives. In this study, a genetic algorithm was applied to enable the intelligent and automated optimization process. The results demonstrated its effectiveness in providing the accuracy and speed Pareto front (PF) of MOC when solving C5G7 benchmark problems. And the optimized parameters showed certain applicability in NuScale calculations. Additionally, the PFs can reflect a certain inherent characteristic of MOC. Based on the PFs, the influence of the flat source (FS) approximation, linear source (LS) approximation, and coarse mesh finite difference (CMFD) acceleration on MOC was analyzed.
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