Research on angle-dependent Monte Carlo cascade variance reduction method

BackgroundThe Monte Carlo method is widely used in the field of shielding calculations. To address the computational efficiency issues of the Monte Carlo method, the Monte Carlo variance reduction method emerged. Most existing representative Monte Carlo variance reduction methods neglect the influence of particle flight direction when assessing importance.PurposeThis study aims to address the issue of strong anisotropy problems by proposing a variance reduction method based on Monte Carlo pre-computation that fully utilizes particle angular information.MethodsThe core idea of angle-dependent Monte Carlo cascade variance reduction method involved directly calculating particle flux within phase-space meshes divided by spatial, energy, and angular dimensions, evaluating mesh importance, and generating angle-dependent weight windows. Subsequently, a cascade algorithm leveraging response factors was developed to rapidly obtain flux and importance distributions. By performing a single Monte Carlo pre-computation to acquire response factors, regional importance was efficiently evaluated through the integration of response factors and target responses. Finally, comparative calculations were conducted using representative local and global models.ResultsComparison results show that both local and global issues are handled by the angle-dependent cascade variance reduction method can handle, hence the FOM (Figure of Merit) is improved by about 30% compared with the MAGIC (Method of Automatic Generation of Importances by Calculation) method.ConclusionsThe angle-dependent method can effectively accelerate the convergence speed of Monte Carlo particle transport simulations and improve computational efficiency, showing obvious advantages in solving anisotropic problems.