Book Description

Particle transport through binary stochastic mixtures has received considerable research attention in the last two decades. Zimmerman and Adams proposed a Monte Carlo algorithm (Algorithm A) that solves the Levermore-Pomraning equations and another Monte Carlo algorithm (Algorithm B) that should be more accurate as a result of improved local material realization modeling. Zimmerman and Adams numerically confirmed these aspects of the Monte Carlo algorithms by comparing the reflection and transmission values computed using these algorithms to a standard suite of planar geometry binary stochastic mixture benchmark transport solutions. The benchmark transport problems are driven by an isotropic angular flux incident on one boundary of a binary Markovian statistical planar geometry medium. In a recent paper, we extended the benchmark comparisons of these Monte Carlo algorithms to include the scalar flux distributions produced. This comparison is important, because as demonstrated, an approximate model that gives accurate reflection and transmission probabilities can produce unphysical scalar flux distributions. Brantley and Palmer recently investigated the accuracy of the Levermore-Pomraning model using a new interior source binary stochastic medium benchmark problem suite. In this paper, we further investigate the accuracy of the Monte Carlo algorithms proposed by Zimmerman and Adams by comparing to the benchmark results from the interior source binary stochastic medium benchmark suite, including scalar flux distributions. Because the interior source scalar flux distributions are of an inherently different character than the distributions obtained for the incident angular flux benchmark problems, the present benchmark comparison extends the domain of problems for which the accuracy of these Monte Carlo algorithms has been investigated.