Finite-size effects and percolation properties of Poisson geometries

Probability density functions for photon propagation in a binary (isotropic-Poisson) statistical mixture with unmatched positive and negative refractive indexes

The exact homogenized probability density function, for a photon making a step of length s has been analytically derived for a binary (isotropic-Poisson) statistical mixture with unmatched refractive indexes. The companions, exact, homogenized probability density functions for a photon to change direction ("scatter"), with polar ϑ and azimuthal φ angles, and the homogenized albedo, have also been obtained analytically. These functions also apply to negative refractive indexes and can reduce the number of Monte Carlo simulations needed for photon propagation in complex binary (isotropic-Poisson) statistical mixtures from hundreds to just one, for an equivalent homogeneous medium. Note, that this is not an approximate approach, but a mathematically equivalent and exact result. Additionally, some tutorial examples of homogenized Monte Carlo simulations are also given.

Chord length sampling with memory effects for spatially heterogeneous Markov media: Application to the rod model

In this work we propose a modified Chord Length Sampling (CLS) algorithm, endowed with two layers of "memory effects," aimed at solving particle transport problems in one-dimensional spatially nonhomogeneous Markov media. CLS algorithms are a family of Monte Carlo methods which account for the stochastic nature of the media by sampling on-the-fly the random interfaces between material phases during the particle propagation. The possibility for the particles to remember the last crossed interfaces increases the accuracy of these models with respect to reference solutions obtained by solving the Boltzmann equation on a large number of realizations of the Markov media. In previous investigations, CLS models with memory have been tested exclusively for spatially uniform stochastic media: in this paper we extend this class of Monte Carlo methods to the case of spatially nonhomogeneous configurations. The effectiveness and the robustness of the modified CLS are probed considering several benchmark problems with varying material cross sections and Markov media densities. The obtained results are a stepping stone towards a generalization to three-dimensional models.

Analysis of heterogeneous Markov media for particle transport problems

Markov media provide a prototype class of stochastic geometries that are widely used in order to model several complex and disordered systems encompassing, e.g., turbulent fluids and plasma, atmospheric layers, or biological tissues, especially in relation to particle transport problems. In several key applications, the statistical properties of random media may display spatial gradients due to material stratification, which means that the typical spatial scale and the probability of finding a given material phase at a spatial location become nonhomogeneous. In this paper we investigate the main features of spatially heterogeneous Markov media, using Poisson hyperplane tessellations and Arak polygonal fields. We show that both models can generate geometry realizations sharing Markov-like properties, and discuss their distinct advantages and drawbacks in terms of flexibility and ease of use. The impact of these models on the observables related to particle transport will be assessed using Monte Carlo simulations.

Stochastic radiative transfer in random media. II. Coupling of radiation to material

We study the mechanism of the impact of random media on the stochastic radiation transport based on a one-dimensional (1D) planar model. To this end, we use a random sampling of mixtures combined with a deterministic solution of the time-dependent radiation transport equation coupled to a material temperature equation. Compared to purely absorbing cases [C.-Z. Gao et al., Phys. Rev. E 102, 022111 (2020)10.1103/PhysRevE.102.022111], we find that material temperatures can significantly suppress the impact of mixing distribution and size, which is understood from the analysis of energy transport channels. By developing a steady-state stochastic transport model, it is found that the mechanism of transmission of radiation is distance dependent, which is closely related to the mean free path of photons l_{p}. Furthermore, we suggest that it is the relationship between l_{p} and L (the width of random medium) that determines the impact of random media on the stochastic radiation transport, which is further corroborated by additional simulations. Most importantly, combining the proposed simple relationship and 1D simulations, we resolve the existing disputable issue of the impact of random media in previous multidimensional works, showing that multidimensional results are essentially consistent and the observed weak or remarkable impact of random media is mainly due to the distinctly different relationship between l_{p} and L. Our results may have practical implications in relevant experiments of stochastic radiative transfer.

PRELIMINARY INVESTIGATIONS OF TRANSPORT IN HETEROGENEOUS RANDOM MEDIA

Random media emerge in several applications in reactor physics and safety analysis. Most often, models of stochastic media assume spatial homogeneity, whereas real-world complex materials, such as fuel chunks resulting from core degradation, typically display apparent heterogeneities. In a series of previous works, we have shown that stochastic tessellations can be successfully used in order to describe the material properties of several classes of random media. In this paper we extend these results to the case of heterogeneous random media by using Voronoi tessellations with space-dependent seed distributions, allowing for spatial gradients.

Stochastic radiative transfer in random media: Pure absorbing cases

We study stochastic radiation transport through random media in one dimension, in particular for pure absorbing cases. The statistical model to calculate the ensemble-averaged transmission for a binary random mixture is derived based on the cumulative probability density function (PDF) of optical depth, which is numerically simulated for both Markovian and non-Markovian mixtures by Monte Carlo calculations. We present systematic results about the influence of mixtures' stochasticity on the radiation transport. It is found that mixing statistics affects the ensemble-averaged intensities mainly due to the distribution of cumulative PDF at small optical depths, which explains well why the ensemble-averaged transmission is observed to be sensitive to chord length distribution and its variances. The effect of the particle size is substantial when the mixtures' correlation length is comparable to the mean free path of photons, which imprints a moderately broad transition region into the cumulative PDF. With the mixing probability increasing, the intensity decreases nearly exponentially, from which the mixing zone length can be approximately estimated. The impact of mixed configuration is also discussed, which is in line with previous results.