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PARTICLE POPULATION DIAGNOSTICS IN ITERATED-SOURCE MONTE CARLO METHODS

On-the-fly diagnostics of the number of particles per iteration cycle in iterated-source Monte Carlo computation, which output diagnostic measures for a given spatial resolution of binning cells as iteration cycles progress, have been studied using relative entropy and chi-square distance. A source ratio vector is defined whose components are the ratio of the sources of adjacent iteration cycles at the individual binning cells. This enables one to define a problem-independent reference vector based on the integral equation representation of the static eigenvalue problem of particle multiplication. All vectors involved are normalized so that they represent discrete probability distribution. The weighted difference between the relative entropy and chi-square distance of the source ratio vector, both with respect to the reference vector, has been shown to be an effective measure of particle population. Numerical examples are presented for the initial core of a pressurized water reactor and the k-effective-of-the-world problem.