Collaborative Research: NSF/EAR-BSF: Coupled Sand and Clay Motion, Bed Morphodynamics, and Porewater Exchange

合作研究：NSF/EAR-BSF：沙土耦合运动、床层形态动力学和孔隙水交换

基本信息

批准号：
1734300
负责人：
Aaron Packman
金额：
$ 34.97万
依托单位：
Northwestern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734300&HistoricalAwards=false
关键词：
Collaborative Research NSF EAR BSF

项目摘要

Interactions between water and sediments shape water bodies, set habitat for fish, shellfish, and other aquatic organisms, and control land stability in river floodplains and coastal regions. While the behavior of sandy systems is fairly well understood, little information is available on interactions between sand and finer sediments such as clay and mud. Mixed clay-sand deposits are very common in rivers and coastal estuaries, yet it is not currently possible to predict important behavior of clay-sand mixtures, such as changes in sand dunes, beaches, and channels following shoreline erosion. This project will advance capability to measure and predict interactions between water flow, sand, and clay in freshwater and marine systems. This capability will enable better management of critical U.S. water resources, reduce damage from coastal erosion, and facilitate shipping and military operations in shallow coastal waters. This project aims to show that clay-sand-water coupling follows a regular and fundamental set of processes: migration of clay into sand beds, accumulation of clay deposits, and feedbacks from clay filling pore spaces and cementing sand grains. Work will be conducted jointly by U.S. and Israeli research teams. Experiments will use a suite of new optical, acoustic, and X-ray imaging methods to measure water flow, clay-sand dynamics, and sediment bed formation. These results will be used to develop new mathematical theory and computer models enabling prediction of water flow and sediment dynamics in clay-sand systems.This award is cofunded by the Geomorphology and Land-use Dynamics Program, the Hydrologic Sciences Program, and the Office of International Science and Engineering.

水和沉积物之间的相互作用塑造了水体，为鱼类、贝类和其他水生生物提供栖息地，并控制河流漫滩和沿海地区的土地稳定性。虽然沙质系统的行为已得到相当多的了解，但有关沙子与粘土和泥土等细沉积物之间相互作用的信息却很少。混合粘土-砂沉积物在河流和沿海河口中非常常见，但目前还无法预测粘土-砂混合物的重要行为，例如海岸线侵蚀后沙丘、海滩和河道的变化。该项目将提高测量和预测淡水和海洋系统中水流、沙子和粘土之间相互作用的能力。这种能力将有助于更好地管理美国关键水资源，减少海岸侵蚀造成的损害，并促进沿海浅水区的航运和军事行动。该项目旨在表明粘土-砂-水耦合遵循一组规则且基本的过程：粘土迁移到砂床中、粘土沉积物的积累以及粘土填充孔隙空间和胶结砂粒的反馈。工作将由美国和以色列研究团队联合进行。实验将使用一套新的光学、声学和 X 射线成像方法来测量水流、粘土-砂动力学和沉积物床的形成。这些结果将用于开发新的数学理论和计算机模型，从而预测粘土-砂系统中的水流和沉积物动力学。该奖项由地貌和土地利用动力学计划、水文科学计划和办公室共同资助国际科学与工程。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Application of Hausdorff fractal derivative to the determination of the vertical sediment concentration distribution

Hausdorff分形导数在确定泥沙浓度垂直分布中的应用

DOI：
10.1016/j.ijsrc.2022.07.007
发表时间：
2022-08-01
期刊：
International Journal of Sediment Research
影响因子：
3.6
作者：
Hongguang Sun;Shiqian Nie;A. Packman;Yong Zhang;Dong Chen;Chengpeng Lu;C. Zheng
通讯作者：
C. Zheng

Interactions Between Suspended Kaolinite Deposition and Hyporheic Exchange Flux Under Losing and Gaining Flow Conditions

失流和增益流条件下悬浮高岭石沉积与潜流交换通量之间的相互作用

DOI：
10.1029/2018gl077951
发表时间：
2018-05-16
期刊：
Geophysical Research Letters
影响因子：
5.2
作者：
A. Fox;A. Packman;F. Boano;C. Phillips;S. Arnon
通讯作者：
S. Arnon

Impacts of suspended clay particle deposition on sand‐bed morphodynamics

悬浮粘土颗粒沉积对砂床形态动力学的影响

DOI：
10.1029/2019wr027010
发表时间：
2020-05
期刊：
Water Resources Research
影响因子：
5.4
作者：
Dallmann, J.;Phillips, C. B.;Teitelbaum, Y.;Sund, N.;Schumer, R.;Arnon, S.;Packman, A. I.
通讯作者：
Packman, A. I.

Dynamics of Hyporheic Exchange Flux and Fine Particle Deposition Under Moving Bedforms

移动床型下的潜流交换通量和细颗粒沉积动力学

DOI：
10.1029/2020wr028541
发表时间：
2021-04-01
期刊：
Water Resources Research
影响因子：
5.4
作者：
Y. Teitelbaum;J. Dallmann;C. Phillips;A. Packman;R. Schumer;N. Sund;S. Hansen;S. Arnon
通讯作者：
S. Arnon

Fine Sediment Deposition and Filtration Under Losing and Gaining Flow Conditions: A Particle Tracking Model Approach

失流和增益流条件下的细粒沉积物沉积和过滤：颗粒跟踪模型方法