Population-based variance reduction for dynamic Monte Carlo

Abstract Dynamic Monte Carlo (DMC) simulation of realistic nuclear reactors requires powerful variance reduction methods for even a few seconds of real time calculations. State-of-the-art numerical methods deal with the dynamic nature of the problem via successive Monte Carlo transport and TH (thermal-hydraulic) runs in a time step by time step manner. Such halting of the sample population at the beginning of time steps also allows for a joint handling of samples in a variance reduction effort. A theoretical framework is given for the connection of weight distribution and tally variance by factorizing it into a population variance accumulated by previous time steps and the variance caused by the transport process in the last interval. A long term importance function is proposed for decreasing the main contribution of a power release tally variance. Novel techniques are shown and compared when using Russian Roulette and Splitting. A simple fast critical assembly and a detailed thermal reactor geometry are used for testing showing that a factor of at least two orders of magnitude is to be gained by a simple population comb targeting the average weight. Further improvement using importance and variance functions is less than a factor two.