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Kernel Density Estimator Methods for Monte Carlo Radiation Transport.

Banerjee, Kaushik

Banerjee, Kaushik

2010

Abstract: In this dissertation, the Kernel Density Estimator (KDE), a nonparametric probability density estimator, is studied and used to represent global Monte Carlo (MC) tallies. KDE is also employed to remove the singularities from two important Monte Carlo tallies, namely point detector and surface crossing flux tallies. Finally, KDE is also applied to accelerate the Monte Carlo fission source iteration for criticality problems.
In the conventional MC calculation histograms are used to represent global tallies which divide the phase space into multiple bins. Partitioning the phase space into bins can add significant overhead to the MC simulation and the histogram provides only a first order approximation to the underlying distribution. The KDE method is attractive because it can estimate MC tallies in any location within the required domain without any particular bin structure. The quantitative and numerical convergence properties of KDE tallies are also investigated.
Monte Carlo point detector and surface crossing flux tallies are two widely used tallies but they suffer from an unbounded variance. As a result, the central limit theorem can not be used for these tallies to estimate confidence intervals. By construction, KDE tallies can be directly used to estimate flux at a point but the variance of this point estimate does not converge as 1/N, which is not unexpected for a point quantity. However, an improved approach is to modify both point detector and surface crossing flux tallies directly by using KDE within a variance reduction approach by taking advantage of the fact that KDE estimates the underlying probability density function. This methodology is demonstrated by several numerical examples.
KDE is also applied to Monte Carlo eigenvalue calculations for nuclear reactor analyses. KDE is used to estimate the fission source distribution at the end of each generation and realizations from the estimated source distribution are used as the starting locations for the next generation. The source convergence is measured by the relative source entropy. Significant source convergence improvement is observed for the proposed KDE method compared to the conventional Monte Carlo fission source iteration.