Publications Details

BENCHMARK COMPARISONS OF MONTE CARLO ALGORITHMS FOR ONE-DIMENSIONAL N-ARY STOCHASTIC MEDIA

Vu, Emily H.; Brantley, Patrick S.; Olson, Aaron J.; Kiedrowski, Brian C.

We extend the Monte Carlo Chord Length Sampling (CLS) and Local Realization Preserving (LRP) algorithms to the N-ary stochastic medium case using two recently developed uniform and volume fraction models that follow a Markov-chain process for N-ary problems in one-dimensional, Markovian-mixed media. We use the Lawrence Livermore National Laboratory Mercury Monte Carlo particle transport code to compute CLS and LRP reflection and transmission leakage values and material scalar flux distributions for one-dimensional, Markovian-mixed quaternary stochastic media based on the two N-ary stochastic medium models. We conduct accuracy comparisons against benchmark results produced with the Sandia National Laboratories PlaybookMC stochastic media transport research code. We show that CLS and LRP produce exact results for purely absorbing N-ary stochastic medium problems and find that LRP is generally more accurate than CLS for problems with scattering.