SUMMARY

Because of the accuracy and ease of implementation, the Monte Carlo methodology is widely used in the analysis of nuclear systems. The estimated multiplication factor (keff) and flux distribution are statistical by their nature. Therefore, it is necessary to ensure that only the converged data are obtained for further analysis. Discarding a larger amount of initial histories could reduce the risk of contaminating the results by non-converged data, but increase the computational expense. This issue is amplified for large nuclear systems with slow convergence. One possible solution is to determining the convergence of keff or the flux distribution. Although several approaches have been developed, these methods are not always reliable, especially for slow convergence problems. As a result, one of my research objectives will aim to find a more reliable and robust way to assess convergence by analyzing the local flux change. In addition, an alternative solution by increasing the convergence rate to reduce the computational expense is also a reasonable research topic. Therefore, the other objective of my research will focus on development and implementation of convergence acceleration methodology.