In multi-step Monte Carlo simulation of time-dependent particle transport problems, particle-flag based physical quantity can be calculated by appropriate classification of diverse particle's attributes. Some particle-flag based physical quantities' fluctuation are strong since only very small fraction of total histories can make non-zero contribution and it is inefficient to deal with this problem by increasing purely total history number. A source bias algorithm is proposed to decrease stochastic error of target quantity by increasing number of source particle with a specific type only. Meanwhile, precision of non-target quantities are hardly decreased. A one-dimensional multi-layer model is utilized to display effect of the method.

A global variance reduction method is described.The method is based on a modified transport problem that can be solved by analog Monte Carlo with zero variance.It is developed by breaking up the whole into parts and coupling Monte Carlo method with discrete ordinates method and using approximate importance function to guide Monte Carlo simulation.Performance of the method for a one-dimensional sphere transport problem is demonstrated.The method is shown to produce lower variances globally compared to analog Monte Carlo.