Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR023552 |
Trajectories as Training Images to Simulate Advective-Diffusive, Non-Fickian Transport | |
Mose, Sebastian1; Bolster, Diogo2; Bijeljic, Branko3; Nowak, Wolfgang1 | |
2019-04-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:4页码:3465-3480 |
文章类型 | Article |
语种 | 英语 |
国家 | Germany; USA; England |
英文摘要 | We propose a spatial Markov model to simulate transport in three-dimensional complex porous media flows. Our methodology is inspired by the concept of training images from geostatistics. Instead of using a training image we use highly resolved training trajectories obtained by high-resolution particle tracking, from which we sample increments in our random walk model. To reflect higher-order processes, subsequent increments are correlated. The approach can be split into three steps. First, we subdivide (cut) the training trajectories to form an archive of trajectory segments. Next, we recursively sample segments, where subsequent samples are chosen conditioned to the previous one to ensure continuity and smoothness of velocity (conditional copy). Finally, we merge (paste) consecutive segments together to generate simulated trajectories of arbitrary length. This training trajectory approach aims to overcome three common shortcomings of spatial Markov models: (1) We simulate finite-Peclet transport in three dimensions without commonly made simplifications (e.g., dimensionality reduction, and neglecting diffusion). (2) We do not parameterize dependence via a high-dimensional transition matrix. (3) We simulate transport at the resolution of the (highly resolved) training trajectories, which can be important for processes such as mixing and reaction. To validate our methodology, we apply it to simulate transport within a three-dimensional sandstone sample and compare predictions of a broad range of benchmark metrics against measurements from direct numerical simulations. We demonstrate that the training trajectories approach accurately represents three-dimensional particle motion, suggesting that this method can capture the governing dependence structure and simulate transport processes in full complexity. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000468597900048 |
WOS关键词 | SPATIAL MARKOV MODEL ; TIME RANDOM-WALKS ; HETEROGENEOUS MEDIA ; ANOMALOUS TRANSPORT ; UPSCALING TRANSPORT ; SOLUTE TRANSPORT ; DISPERSION ; FLOW ; DILUTION ; BEHAVIOR |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/182253 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Stuttgart, Dept Stochast Simulat & Safety Res Hydrosyst, Stuttgart, Germany; 2.Univ Notre Dame, Dept Civil & Environm Engn & Earth Sci, Notre Dame, IN 46556 USA; 3.Imperial Coll London, Dept Earth Sci & Engn, London, England |
推荐引用方式 GB/T 7714 | Mose, Sebastian,Bolster, Diogo,Bijeljic, Branko,et al. Trajectories as Training Images to Simulate Advective-Diffusive, Non-Fickian Transport[J]. WATER RESOURCES RESEARCH,2019,55(4):3465-3480. |
APA | Mose, Sebastian,Bolster, Diogo,Bijeljic, Branko,&Nowak, Wolfgang.(2019).Trajectories as Training Images to Simulate Advective-Diffusive, Non-Fickian Transport.WATER RESOURCES RESEARCH,55(4),3465-3480. |
MLA | Mose, Sebastian,et al."Trajectories as Training Images to Simulate Advective-Diffusive, Non-Fickian Transport".WATER RESOURCES RESEARCH 55.4(2019):3465-3480. |
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