Discharge coefficient of sharp-crested weirs using potential flow

Afzalimehr, Hossein; Bagheri, Sara

doi:10.3826/jhr.2009.3609

Cited by 15 publications

(3 citation statements)

References 11 publications

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“…Experiments are performed with unit discharges ranging from 7.36 m 3 h −1 to 88.57 m 3 h −1 via a controllable valve connected to a pump. The discharge was measured repeatedly with a weir at each step before the experiments, and the repeatability of the tests was guaranteed (Afzalimehr & Bagheri, 2010; Johnson, 2000). The experiments began by establishing a steady discharge of 7.36 m 3 h −1 for at least 30 min to create a stable base flow for the river channel.…”

Section: Experimental Model Testsmentioning

confidence: 99%

A deep learning technique based flood propagation experiment

Hou

Bai

et al. 2021

J Flood Risk Management

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This work presents an experiment involving detailed fluvial flood propagation process. Comparing to the existing flood experiments which collect hydrodynamical data just at gauges, flood evolution process in river channel and flood plain is measured and temporal–spatial data are provided. In the experiment, three inflow patterns are considered to reflect the different severity of the floods. The flood propagation and inundation are captured by using an array of surveillance cameras. The images are pre‐processed by applying camera calibration method to correct the barrel distortion. A deep learning technique is introduced to automatically identify inundated area. The inundation process is therefore obtained by identifying image series. In addition to the spatial data, the water level evolutions at three gauges are also monitored to supply detailed hydrodynamic information at gauges. The repeatability of the experiments and reliability of the deep learning technique are verified. The experimental data including spatial and point hydrodynamic features for flood events can be used to systematically validate numerical model and calibrate parameters.

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Section: Experimental Model Testsmentioning

confidence: 99%

A deep learning technique based flood propagation experiment

Hou

Bai

et al. 2021

J Flood Risk Management

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“…The significance of the various restrictions can be shown only by co mparing the calculated results with those obtained fro m observations or real data. Afzalimehr and Bagheri [10] developed an equation for estimating the discharge coefficient of sharp-crested weirs based on potential flow theory. They compared the results with the experimental data and suggested an equation for the discharge coefficient for a large range of vertical aspect ratios.…”

Section: Introductionmentioning

confidence: 99%

Analytical Solution for the Free Over-Fall Weir Flow Using Conformal Mapping and Potential Flow Theory

Kabiri-Samani

Amirabdollahian²,

Farshi³

2013

IJHE

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In this study an analytical approach is presented based on the potential flow theory and conformal mapping technique to solve the problem of an ideal and steady flow over a free over-fall weir. The results are arranged for rectangular sharp-crested over-fall weirs with different vertical aspect ratios (h/P, h is the water head above the weir and P is the weir height). To validate the results of this approach, the free over-fall weir d ischarge equation and the water free-surface profiles obtained with the potential flow theory have been calibrated with the experimental data. In conclusion, the information made available and the close correlation among the results of present analytical ap proach and the experimental data are adequate to warrant the reco mmendation of this method as a valuable supplement to existing methods employed in engineering design.

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“…In [Afzalimehr and Bagheri, 2009] a calibrated coefficient was used, there is a correction factor that should be determined as a function (power) of H e /W :…”

Section: Afzalimehr and Bagherimentioning

confidence: 99%

Model predictive control of resonance sensitive irrigation canals

Horváth

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Saving water is an economic and ecological need. One way to save water is to reduce losses in irrigation networks by canal automation. The goal of canal automation is to make the right amount of water to at arrive in the right time. In order to achieve this goal, one of the ways is controlling the gates in the irrigation network by some control algorithm. In this work the control of a specific type of canal pools is studied: short and flat pools that are prone to resonance. The downstream water level control of this type of canals is investigated using the example of the 3-reach laboratory canal of the Technical University of Catalonia. Numerical and experimental studies are carried out to investigate the following: the choice of models for predictive control, the possibility to achieve offset-free control while using gravity offtakes and the best choice of control action variables. The objective of this work is to develop a well performing centralized model predictive controller (MPC) for the laboratory canal that is able to handle known and unknown setpoint changes and disturbances, and also to draw further conclusions about controller design for this type of canals. A recently developed model for resonant canals, the Integrator Resonance, is implemented and successfully tested experimentally for the first time. A new method to achieve offset free control for model predictive control is developed and tested numerically and experimentally. A choice of control variables are tested: As opposed to the discharge which is generally used as the control action variable, a state space model is formulated by using the gate opening as control variable without the need of water level measurement downstream of the gates. The results are summarized and conclusions are presented for control of short and flat canals that are prone to resonance.

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Discharge coefficient of sharp-crested weirs using potential flow

Cited by 15 publications

References 11 publications

A deep learning technique based flood propagation experiment

A deep learning technique based flood propagation experiment

Analytical Solution for the Free Over-Fall Weir Flow Using Conformal Mapping and Potential Flow Theory

Model predictive control of resonance sensitive irrigation canals

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