Particle clusters in gravel-bed rivers: an experimental morphological approach to bed material transport and stability concepts

2005 ◽  
Vol 30 (11) ◽  
pp. 1351-1368 ◽  
Author(s):  
Lea Wittenberg ◽  
Malcolm D. Newson
Keyword(s):  
Material Transport ◽  
Gravel Bed ◽  
Morphological Approach ◽  
Gravel Bed Rivers ◽  
Bed Material ◽  
Stability Concepts

scholarly journals Bed material transport estimate in large gravel-bed rivers using the virtual velocity approach

2016 ◽  
Vol 42 (4) ◽  
pp. 595-611 ◽  
Author(s):  
Luca Mao ◽  
Lorenzo Picco ◽  
Mario A. Lenzi ◽  
Nicola Surian
Keyword(s):  
Material Transport ◽  
Gravel Bed ◽  
Gravel Bed Rivers ◽  
Bed Material ◽  
Virtual Velocity

scholarly journals The Prediction of Fine Sediment Distribution in Gravel-Bed Rivers Using a Combination of DEM and FNN

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1515
Author(s):  
Van Hieu Bui ◽  
Minh Duc Bui ◽  
Peter Rutschmann
Keyword(s):  
Feedforward Neural Networks ◽  
Fine Sediment ◽  
Sediment Distribution ◽  
Gravel Bed ◽  
Fine Sediments ◽  
Porosity Variation ◽  
Sediment Sorting ◽  
Gravel Bed Rivers ◽  
Bed Material ◽  
The Relationship

Large amounts of fine sediment infiltration into void spaces of coarse bed material have the ability to alter the morphodynamics of rivers and their aquatic ecosystems. Modelling the mechanisms of fine sediment infiltration in gravel-bed is therefore of high significance. We proposed a framework for calculating the sediment exchange in two layers. On the basis of the conventional approaches, we derived a two-layer fine sediment sorting, which considers the transportation of fine sediment in the form of infiltration into the void spaces of the gravel-bed. The relationship between the fine sediment exchange and the affected factors was obtained by using the discrete element method (DEM) in combination with feedforward neural networks (FNN). The DEM model was validated and applied for gravel-bed flumes with different sizes of fine sediments. Further, we developed algorithms for extracting information in terms of gravel-bed packing, grain size distribution, and porosity variation. On the basis of the DEM results with this extracted information, we developed an FNN model for fine sediment sorting. Analyzing the calculated results and comparing them with the available measurements showed that our framework can successfully simulate the exchange of fine sediment in gravel-bed rivers.

scholarly journals The dominance of dispersion in the evolution of bed material waves in gravel-bed rivers

2001 ◽  
Vol 26 (13) ◽  
pp. 1409-1420 ◽  
Author(s):  
Thomas E. Lisle ◽  
Yantao Cui ◽  
Gary Parker ◽  
James E. Pizzuto ◽  
Annjanette M. Dodd
Keyword(s):  
Gravel Bed ◽  
Gravel Bed Rivers ◽  
Bed Material

Evaluation of effective boundary roughness for gravel-bed rivers

1980 ◽  
Vol 7 (2) ◽  
pp. 392-397 ◽  
Author(s):  
D. I. Bray
Keyword(s):  
Average Velocity ◽  
Gravel Bed ◽  
Effective Boundary ◽  
Gravel Bed River ◽  
Gravel Bed Rivers ◽  
Definition Of ◽  
Bed Material ◽  
Boundary Roughness

In some cases the Keulegan equation is utilized to estimate the average velocity for in-bank flows for gravel-bed rivers. To apply the equation, the effective boundary roughness, k5, is usually estimated in terms of some characteristic bed material size.Data from 67 gravel-bed river reaches in Alberta are used to determine an optimum method of defining the effective boundary roughness in the Keulegan equation. The analysis indicates that k5 is best estimated as 6.8d50, 5.2d65, 3.5d84, or 3. Id90. These findings are compared with the work of other investigators who used gravel-bed river data from Britain and the United States.Errors in the estimation of average velocity by the Keulegan equation range from about 20% for D/d90 = 200 to about 100% for D/d50 = 5 if the characteristic bed material size, d0, is used to estimate the effective boundary roughness, k5, rather than the optimum definition of the effective boundary roughness, Cd0, as determined in this note.

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