Poisson-Box Sampling algorithms for three-dimensional Markov binary mixtures
| Autor: | Eric Dumonteil, Coline Larmier, Alain Mazzolo, Fausto Malvagi, Andrea Zoia |
|---|---|
| Přispěvatelé: | CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Service des Réacteurs et de Mathématiques Appliquées (SERMA), Département de Modélisation des Systèmes et Structures (DM2S), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Service d’Études des Réacteurs et de Mathématiques Appliquées (SERMA) |
| Rok vydání: | 2017 |
| Předmět: |
010504 meteorology & atmospheric sciences
[PHYS.NUCL]Physics [physics]/Nuclear Theory [nucl-th] Computer science Monte Carlo method Binary number FOS: Physical sciences [PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex] Poisson distribution Tripoli-4 01 natural sciences 010305 fluids & plasmas Reduction (complexity) symbols.namesake benchmark 0103 physical sciences Markov geometries Applied mathematics Monte Carlo Condensed Matter - Statistical Mechanics Spectroscopy 0105 earth and related environmental sciences Radiation Markov chain Statistical Mechanics (cond-mat.stat-mech) Sampling (statistics) Computational Physics (physics.comp-ph) Poisson Boltzmann equation Atomic and Molecular Physics and Optics Chord Length Sampling symbols Physics - Computational Physics Gibbs sampling |
| Zdroj: | Journal of Quantitative Spectroscopy and Radiative Transfer Journal of Quantitative Spectroscopy and Radiative Transfer, Elsevier, 2018, 206, pp.70-82. ⟨10.1016/j.jqsrt.2017.10.020⟩ Journal of Quantitative Spectroscopy and Radiative Transfer, 2018, 206, pp.70-82. ⟨10.1016/j.jqsrt.2017.10.020⟩ |
| ISSN: | 0022-4073 |
| DOI: | 10.48550/arxiv.1708.04260 |
| Popis: | Particle transport in Markov mixtures can be addressed by the so-called Chord Length Sampling (CLS) methods, a family of Monte Carlo algorithms taking into account the effects of stochastic media on particle propagation by generating on-the-fly the material interfaces crossed by the random walkers during their trajectories. Such methods enable a significant reduction of computational resources as opposed to reference solutions obtained by solving the Boltzmann equation for a large number of realizations of random media. CLS solutions, which neglect correlations induced by the spatial disorder, are faster albeit approximate, and might thus show discrepancies with respect to reference solutions. In this work we propose a new family of algorithms (called 'Poisson Box Sampling', PBS) aimed at improving the accuracy of the CLS approach for transport in $d$-dimensional binary Markov mixtures. In order to probe the features of PBS methods, we will focus on three-dimensional Markov media and revisit the benchmark problem originally proposed by Adams, Larsen and Pomraning and extended by Brantley: for these configurations we will compare reference solutions, standard CLS solutions and the new PBS solutions for scalar particle flux, transmission and reflection coefficients. PBS will be shown to perform better than CLS at the expense of a reasonable increase in computational time. Comment: 18 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:1708.00765 |
| Databáze: | OpenAIRE |
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