A Method to Alleviate the Long History Problem Encountered in Monte Carlo Simulations via Weight Window Variance Reduction

When the weight window is implemented in large and complex models, streaming path and deep penetration could lead to a long history problem that takes a disproportionate amount of time for the accomplishment of Monte Carlo simulations and exerts a detrimental effect on the efficiency of parallel computing. An approach set the limitation of weight window splitting was proposed for the alleviation of long history problem. Tests were conducted on a streaming dog-leg geometry model and a 3D model of the Chinese Fusion Engineering Testing Reactor. The results show that a suitable parameter in the alleviation method could optimize both the performance of variance reduction and the efficiency of parallel calculation, which makes long history problem tractable.