scholarly journals Suspended Sediment Concentration Distribution of Flow over Vegetated Sand Bed

1991 ◽  
Vol 35 ◽  
pp. 477-482
Author(s):  
Yoshihiko SHIMIZU ◽  
Tetsuro TSUJIMOTO ◽  
Tadanori KITAMURA
Keyword(s):  
Suspended Sediment ◽  
Concentration Distribution ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Distribution Of Flow

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. 

Distribusi Kecepatan dan Konsentrasi Sedimen Suspensi pada Aliran Menikung (Studi Kasus pada Saluran Irigasi Mataram Yogyakarta)

2016 ◽  
Vol 12 (1) ◽  
pp. 11
Author(s):  
Chairul Muharis
Keyword(s):  
Velocity Distribution ◽  
Flow Velocity ◽  
Suspended Sediment ◽  
Centrifugal Force ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Low Velocity ◽  
Distribution Profile ◽  
Bend Flow ◽  
Distribution Of Flow

The flow velocity and suspended sediment concentration are important parameters of sediment transport mechanisms, especially for agradation and degradation problems. The centrifugal force at the bend channels will increased flow velocity at the outer bank of the bend. It is of course also affects the distribution of flow velocity toward the outside and the inside of the bend channels. The change of the velocity distribution  it is very possible also changes the distribution of suspended sediment concentration. In this paper will discuss the velocity distribution profile and distribution of sediment concentration in the bend flow. This research was conducted at Mataram Irrigation Channel Yogyakarta. The channel rectangular in shape and made of masonry with angle bend 580. Measuring the flow velocity used Propeller currentmeter and  sediment concentration used Opcon Probe. Measuring flow velocity and sediment concentration conducted simultaneously for each measurement point.The results showed that due to the centrifugal force in bend flow, flow velocity distribution and sediment concentration distribution undergoing significant change the outside and the inside of the bend. In general, the distribution of flow velocity toward the outerbank of the bend has increased and the distribution suspended  sediment concentration has decrease  and the opposite occurs innerbank of the bend. A low velocity on the inner bank of the channel bend causes larger grains of sediment that settles and potentially silting.

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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