Monte Carlo neutral particle transport through a binary stochastic mixture using chord length sampling

A Monte Carlo algorithm is developed to estimate the ensemble-averaged behavior of neutral particles within a binary stochastic mixture. A special case stochastic mixture is examined, in which non-overlapping spheres of constant radius are uniformly mixed in a matrix material. Spheres are chosen to represent the stochastic volumes due to their geometric simplicity and because spheres are a common approximation to a large number of applications. The boundaries of the mixture are impenetrable, meaning that spheres in the stochastic mixture cannot be assumed to overlap the mixture boundaries. The algorithm employs a method called Limited Chord Length Sampling (LCLS). While in the matrix material, LCLS uses chord-length sampling to sample the distance to the next stochastic interface. After a surface crossing into a stochastic sphere, transport is treated explicitly until the particle exits or is killed. This capability eliminates the need to explicitly model a representation of the random geometry of the mixture.; The algorithm is first proposed and tested against benchmark results for a two dimensional, fixed source model using stand-alone Monte Carlo codes. The algorithm is then implemented and tested in a test version of the Los Alamos M&barbelow;onte C&barbelow;arlo n&barbelow;-p&barbelow;article Code MCNP. This prototype MCNP version has the capability to calculate LCLS results for both fixed source and multiplied source (i.e., eigenvalue) problems. Problems analyzed with MCNP range from simple binary mixtures, designed to test LCLS over a range of optical thicknesses, to a detailed High Temperature Gas Reactor fuel element, which tests the value of LCLS in a current problem of practical significance.; Comparisons of LCLS and benchmark results include both accuracy and efficiency comparisons. To ensure conservative efficiency comparisons, the statistical basis for the benchmark technique is derived and a formal method for optimizing the benchmark calculations is developed. LCLS results are compared to results obtained through other methods to gauge accuracy and efficiency. The LCLS model is efficient and provides a high degree of accuracy through a wide range of conditions.