Brownian bridge and statistical error estimation in Monte Carlo reactor calculation

Abstract

Brownian bridge (BB) is an effective vehicle in processing an output series in Monte Carlo (MC) simulation. However, most estimators based on BB cost the capability of on-the-fly monitoring. Here, on-the-fly implies that statistical error can be computed at every generation except some initial generations. In this work, onthe-fly estimation of standard deviation by the way of BB, which maintains a fixed storage size of tallies, has been investigated within a framework of the iterated integration of simulation output (IISO). Numerical tests on the MC power distribution calculation of a pressurized water reactor core reveal that the IISO approach with a relatively few number of integrations performs fairly well on average. The bias of statistical error can be managed to be about 10% or less.

Fractal dimension analysis for run length diagnosis of Monte Carlo criticality calculation

“…The BCD estimation of S n (t) is, therefore, insensitive to the choice of the estimator of σ . In a series of recent works, OW-STS was shown to be unbiased for a sufficiently large number of generations run [4], be much more stable than other BB-based estimator of σ [15], and perform comparatively with a typical batch means estimator of σ accompanied by asymptotic bias correction [16]. For these reasons, OWSTS will be employed to compute σ .…”