Pointing probability Driven semi-analytic Monte Carlo Method (PDMC) – Part I: Global variance reduction for large-scale radiation transport analysis

We propose a Pointing Probability Driven Semi-Analytic Monte Carlo Method (PDMC) to replace the traditional Monte Carlo method for reactor physics analysis and radiation transport analysis. This paper describes the calculation principle and acceleration effect of the PDMC method in radiation transport analysis, highlighting its advantages in dealing with the deep-penetration problem. The PDMC method no longer requires particles to be transported to the detector. During the particle transport process, the global response of each particle state is calculated with a pointing probability to construct global information quickly. The statistical unbiasedness of the global response of a single particle state is demonstrated through rigorous mathematical derivation. The PDMC method does not rely on a deterministic program or iterative calculation, ensuring its universality and efficiency of global variance reduction (GVR). The PDMC method is tested in the China Fusion Engineering Test Reactor (CFETR) and the HBR2 benchmark. The results show that the PDMC method can significantly improve the efficiency of Monte Carlo deep-penetration simulation and is helpful for the GVR for large-scale radiation analysis. •Innovation: constructing global information from pseudo tracks is a new idea.•Universality: the method can be easily implemented and applied in a variety of Monte Carlo codes and models/benchmarks.•Feasibility: tested by the CFETR model and the HBR2 benchmark.•Rigorousness: the statistical unbiasedness is demonstrated through rigorous mathematical derivation.•Significance: the method is helpful for the global variance reduction for large-scale radiation analysis.