Abstract
In the Monte Carlo simulation of particle transport, and especially for shielding applications, variance reduction techniques are widely used to help simulate realisations of rare events and reduce the relative errors on the estimated scores for a given computation time. Adaptive Multilevel Splitting is one of these variance reduction techniques that has recently appeared in the literature. In the present paper, we propose an alternative version of the AMS algortihm, adapted for the first time to the field of particle tranport. Within this context, it can be used to build an unbiased estimator of any quantity associated with particle tracks, such as flux, reaction rates or even non-Boltzmann tallies. Furthermore, the effciency of the AMS algorithm is shown not to be very sensitive to variations of its input parameters, which makes it capable of significant variance reduction without requiring extended user effort.
Highlights
The challenge in using Monte Carlo particle transport simulations for shielding applications is to minimize the computation time required to attain a reasonable variance on the quantity of interest, called score
We present a specific mathematical setting of the AMS algorithm, which will be afterwards fairly easy to adapt to the context of particle transport
These results are from photon-only simulations, as the coupled photons/electrons/positrons AMS is not yet fully operational. These preliminary results show that the use of the Adaptive Multilevel Splitting algorithm in TRIPOLI-4 R reduces significatively the variance
Summary
The challenge in using Monte Carlo particle transport simulations for shielding applications is to minimize the computation time required to attain a reasonable variance on the quantity of interest, called score. The basic approach of variance reduction techniques is to modify the simulation behaviour so as to increase rare events occurrence while keeping an unbiased estimator of the score. In this view, multilevel splitting techniques were introduced to the field of particle transport by Kahn and Harris [1]. The core of this work is presented, in which we introduce for the first time a pratical implementation of AMS within a Monte Carlo particle transport simulation. In the last Section of this paper, we present some of the results obtained using AMS within TRIPOLI-4 R These examples illustrate the validity of the algorithm as described, as well as the efficiency of AMS as a variance reduction technique These examples illustrate the validity of the algorithm as described in Section 4, as well as the efficiency of AMS as a variance reduction technique
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