Water-discharge determinations for the tidal reach of the Willamette River from Ross Island Bridge to Mile 10.3, Portland, Oregon

The reliability of water flow measurement in irrigational canals depends on the measurement method and design features of the flow-measuring structure and the upstream flow velocity profile. The flow velocity profile is a function of the channel geometry and wall roughness. The article presents the study results of the influence of the upstream flow velocity profile on the discharge measurement accuracy. For this, the physical and numerical modeling of two structures was carried out: a critical depth flume and a hydrometric overfall in a rectangular channel. According to the data of numerical simulation of the critical depth flume with a uniform and parabolic (1/7) velocity profile in the upstream channel, the values of water discharge differ very little from the experimental values in the laboratory model with a similar geometry (δ < 2 %). In contrast to the critical depth flume, a change in the velocity profile only due to an increase in the height of the bottom roughness by 3 mm causes a decrease of the overfall discharge coefficient by 4…5 %. According to the results of the numerical and physical modeling, it was found that an increase of backwater by hydrometric structure reduces the influence of the upstream flow velocity profile and increases the reliability of water flow measurements.

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COOLING WATER DISCHARGE INTO A STRATIFIED ESTUARY

Proceeding of International Heat Transfer Conference 7 ◽

10.1615/ihtc7.820 ◽

1982 ◽

Author(s):

Siegfried Bloss

Keyword(s):

Water Discharge ◽

Cooling Water ◽

Stratified Estuary

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Continuing the Zero Water Discharge Program in Heavy Oil Operating Unit PT CPI with Waste Water Disposal Project

10.29118/ipa.0.16.269.e ◽

2018 ◽

Author(s):

Ekasurya Poerwanto

Keyword(s):

Waste Water ◽

Heavy Oil ◽

Water Discharge ◽

Water Disposal ◽

Operating Unit

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

Global NEST Journal ◽

10.30955/gnj.000628 ◽

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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