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The investigation of correlations in Monte Carlo power iteration has beenlong dominated by the question of generational correlations and their effectson the estimation of statistical uncertainties. More recently, there has been agrowing interest in spatial correlations, prompted by the discovery of neutronclustering. Despite several attempts, a comprehensive framework concerning howMonte Carlo sampling strategies, population control and variance reductionmethods affect the strength of such correlations is still lacking. In thiswork, we propose a set of global and local (i.e., space-dependent) tallies thatcan be used to characterize the impact of correlations. These tallies encompassthe Shannon entropy, the pair distance, the normalized variance and the Feynmanmoment. In order to have a clean, yet fully meaningful setting, we carry outour analysis in a few homogeneous and heterogeneous benchmark problems ofvarying dominance ratio. Several classes of collision sampling strategies,population control and variance reduction techniques are tested, and theirrelative advantages and drawbacks are assessed with respect to the proposedtallies. The major finding of our study is that branchless collisions, whichsuppress the emergence of branches in neutron histories, also considerablyreduce the effects of correlations in most of the explored configurations.

The effect of branchless collisions and population control on correlations in Monte Carlo power iteration