Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR024833 |
Velocity Field Estimation on Density-Driven Solute Transport With a Convolutional Neural Network | |
Kreyenberg, Philipp J.1; Bauser, Hannes H.1,2; Roth, Kurt1,3 | |
2019-08-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:8页码:7275-7293 |
文章类型 | Article |
语种 | 英语 |
国家 | Germany; USA |
英文摘要 | Recent advances in machine learning open new opportunities to gain deeper insight into hydrological systems, where some relevant system quantities remain difficult to measure. We use deep learning methods trained on numerical simulations of the physical processes to explore the possibilities of closing the information gap of missing system quantities. As an illustrative example we study the estimation of velocity fields in numerical and laboratory experiments of density-driven solute transport. Using high-resolution observations of the solute concentration distribution, we demonstrate the capability of the method to structurally incorporate the representation of the physical processes. Velocity field estimation for synthetic data for both variable and uniform concentration boundary conditions showed equal results. This capability is remarkable because only the latter was employed for training the network. Applying the method to measured concentration distributions of density-driven solute transport in a Hele-Shaw cell makes the velocity field assessable in the experiment. This assessability of the velocity field even holds for regions with negligible solute concentration between the density fingers, where the velocity field is otherwise inaccessible. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000490973700052 |
WOS关键词 | ENCODER-DECODER NETWORKS ; GENERIC GRID INTERFACE ; EVAPORATING SALT LAKE ; POROUS-MEDIA ; NATURAL-CONVECTION ; CARBON-DIOXIDE ; NUMERICAL-SIMULATION ; MASS-TRANSFER ; CO2 ; FLOW |
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/185892 |
专题 | 资源环境科学 |
作者单位 | 1.Heidelberg Univ, Inst Environm Phys IUP, Heidelberg, Germany; 2.Univ Arizona, Biosphere 2, Tucson, AZ USA; 3.Heidelberg Univ, Interdisciplinary Ctr Sci Comp IWR, Heidelberg, Germany |
推荐引用方式 GB/T 7714 | Kreyenberg, Philipp J.,Bauser, Hannes H.,Roth, Kurt. Velocity Field Estimation on Density-Driven Solute Transport With a Convolutional Neural Network[J]. WATER RESOURCES RESEARCH,2019,55(8):7275-7293. |
APA | Kreyenberg, Philipp J.,Bauser, Hannes H.,&Roth, Kurt.(2019).Velocity Field Estimation on Density-Driven Solute Transport With a Convolutional Neural Network.WATER RESOURCES RESEARCH,55(8),7275-7293. |
MLA | Kreyenberg, Philipp J.,et al."Velocity Field Estimation on Density-Driven Solute Transport With a Convolutional Neural Network".WATER RESOURCES RESEARCH 55.8(2019):7275-7293. |
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