Parallelized domain decomposition for multi-dimensional Lagrangian random walk mass-transfer particle tracking schemes

作者：Lucas Schauer, Michael J. Schmidt, Nicholas B. Engdahl, Stephen Pankavich, David A. Benson, Diogo Bolster · 发表于：Geoscientific model development · 年份：2023 · DOI：10.5194/gmd-16-833-2023 · 被引用次数：5 · 研究领域：Spectroscopy Techniques in Biomedical and Chemical Research、Groundwater flow and contamination studies、Lattice Boltzmann Simulation Studies

Abstract. Lagrangian particle tracking schemes allow a wide range of flow and transport processes to be simulated accurately, but a major challenge is numerically implementing the inter-particle interactions in an efficient manner. This article develops a multi-dimensional, parallelized domain decomposition (DDC) strategy for mass-transfer particle tracking (MTPT) methods in which particles exchange mass dynamically. We show that this can be efficiently parallelized by employing large numbers of CPU cores to accelerate run times. In order to validate the approach and our theoretical predictions we focus our efforts on a well-known benchmark problem with pure diffusion, where analytical solutions in any number of dimensions are well established. In this work, we investigate different procedures for “tiling” the domain in two and three dimensions (2-D and 3-D), as this type of formal DDC construction is currently limited to 1-D. An optimal tiling is prescribed based on physical problem parameters and the number of available CPU cores, as each tiling provides distinct results in both accuracy and run time. We further extend the most efficient technique to 3-D for comparison, leading to an analytical discussion of the effect of dimensionality on strategies for implementing DDC schemes. Increasing computational resources (cores) within the DDC method produces a trade-off between inter-node communication and on-node work. For an optimally subdivided diffusion problem, the 2-D paral...