期刊
JOURNAL OF CONTAMINANT HYDROLOGY
卷 234, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jconhyd.2020.103642
关键词
Lagrangian modeling; Particle methods; Imperfect mixing; Diffusion-reaction equation; Heavy metal cycling
资金
- US Army Research Office [W911NF-18-1-0338]
- National Science Foundation [EAR-1351625, EAR-1417145, DMS-1614586]
- DOE Office of Science [DE-SC0019123]
- U.S. Department of Energy (DOE) [DE-SC0019123] Funding Source: U.S. Department of Energy (DOE)
Geochemical systems are known to exhibit highly variable spatiotemporal behavior. This may be observed both in non-smooth concentration curves in space for a single sampling time and also in variability between samples taken from the same location at different times. However, most models that are designed to simulate these systems provide only single-solution smooth curves and fail to capture the noise and variability seen in the data. We apply a recently developed reactive particle-tracking method to a system that displays highly complex geochemical behavior. When the method is made to most closely resemble a corresponding Eulerian method, in its unperturbed form, we see near-exact match between solutions of the two models. More importantly, we consider two approaches for perturbing the model and find that the spatially-perturbed condition is able to capture a greater degree of the variability present in the data. This method of perturbation is a task to which particle methods are uniquely suited and Eulerian models are not well-suited. Additionally, because of the nature of the algorithm, noisy spatial gradients can be highly resolved by a large number of mobile particles, and this incurs negligible computational cost, as compared to expensive chemistry calculations.
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