scholarly journals TIME-AVERAGED TURBULENT MIXING AND VERTICAL CONCENTRATION DISTRIBUTION OF HIGH-DENSITY SUSPENSIONS FORMED UNDER WAVES

2011 ◽  
Vol 1 (32) ◽  
pp. 20
Author(s):  
Bing Yan ◽  
Qing-He Zhang ◽  
Michael Lamb
Keyword(s):  
Turbulent Mixing ◽  
Concentration Distribution ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
High Density ◽  
Oscillating Flow ◽  
Mixing Length ◽  
Flow Data ◽  
The Stability ◽  
Mixing Length Theory

We analyzed oscillating flow data from U-tube experiments by Lamb et al. (2004) and found that the time-averaged turbulent kinetic energy (TKE) near bed decreased exponentially with height above the bed in high-density-suspension (HDS) flows under waves, and that the ratios of TKE distributions in the streamwise, cross-stream, and vertical dimensions were constant. Based on the finite-mixing-length theory, a semi-theoretical time-averaged suspended sediment concentration model for HDS was developed. To avoid the stability problems with the numerical solution, a simplified model was also formulated through combing the apparent Fickian diffusivity and the damping function. The comparison between the calculated results and measurements shows both models consider the effect of the sediment-induced stratification well.

scholarly journals Suspended sediment concentration distribution in an alluvial channel flow

1994 ◽  
Vol 16 (1) ◽  
pp. 15-19
Author(s):  
Dang Huu Chung
Keyword(s):  
Mathematical Model ◽  
Suspended Sediment ◽  
Channel Flow ◽  
Concentration Distribution ◽  
Input Data ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Concentration Gradients ◽  
Alluvial Channel ◽  
The Finite Difference Method

In this paper a numerical solution for the problem on suspended sediment concentration distribution in an alluvial channel flow has been computed on the base of PROFILE model proposed by L. C. Van Rijn for the case of uniform flow. Although, in the present case, mathematical model is quite simple, but it is exact enough to apply to Somme problems in practice. The aim of the paper is that the author would like to use the finite difference method for the same problem. The input data was used from experiment flume. The result showed that the concentration distribution fast decreased along the channel and concentration gradients became very small at the sections situated far enough from the upstream. Besides, a computing program me with the ability of graphic expression was established. 

Turbulent Sediment Transport Over a Flat Bottom Using Momentum Balance

1975 ◽  
Vol 42 (1) ◽  
pp. 38-44 ◽  
Author(s):  
D. A. Drew
Keyword(s):  
Settling Velocity ◽  
Sediment Concentration ◽  
Momentum Balance ◽  
Mixing Length ◽  
Reynolds Stresses ◽  
Asymptotic Results ◽  
Flat Bottom ◽  
Fluid Mixture ◽  
Linear Drag ◽  
Mixing Length Theory

The turbulent flow of a sediment-fluid mixture over a flat bottom is studied using momentum balance. The in-the-small forces included are gravity, buoyancy, and a linear drag. A turbulent average is applied and mixing length theory is used for the resulting Reynolds stresses. Assuming small concentrations and small still water settling velocity, the resulting velocity profile is logarithmic; the sediment concentration profile is relatively constant near the bottom, and drops off rapidly above a certain level predicted by the theory. Numerical and asymptotic results are discussed.

Examination of the Suspended Sediment Concentration Formulae Using Full-Scale Rippled Bed and Sheet-Flow Data

2011 ◽  
Vol 53 (4) ◽  
pp. 451-489 ◽  
Author(s):  
Mantripathi Prabath Ravindra Jayaratne ◽  
Sritharan Srikanthan ◽  
Tomoya Shibayama
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Full Scale ◽  
Flow Data ◽  
Sheet Flow ◽  
Rippled Bed

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

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