Prediction of depth-averaged velocity in an open channel flow

Khuntia, Jnana Ranjan; Devi, Kamalini; Khatua, Kishanjit Kumar

doi:10.1007/s13201-018-0812-9

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…Before analyzing the ADV data, possible spectral analysis has been done. The measured data were despiked by an algorithm based on the acceleration thresholding strategy (Goring and Nikora 2002;Khuntia et al 2018b), which was fit for recognizing and substituting spikes in two stages. The threshold values (=1-1.5) for despiking were determined by trial and error basis, for which the velocity power spectra gave an acceptable fit to the Kolmogorov -5/3 scaling-law in the inertial subrange.…”

Section: Resultsmentioning

confidence: 99%

Turbulence Characteristics in a Rough Open Channel Under Unsteady Flow Conditions

et al. 2021

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The majority of open channel flows of interest to hydraulic engineers and hydrologists are unsteady. In unsteady flow cases, some aspects of flow (velocity, depth, viscosity, pressure, etc.) will be evolving in time. However, more numbers of issues identified with the unsteady flow have been roughly accepting as steady flow (for example, constant peak discharges in floodplains). Very few experimental investigations have been conveyed in previous literature to examine turbulence qualities in an open channel flow under unsteady flow states over rough beds. The present study investigates the vertical and horizontal fluctuating velocity profiles under unsteady flow states in a rectangular open channel. An experiment is conducted to observe the turbulence characteristics under unsteady flow conditions in a rough bed open channel for two different flow depths. One identical hydrograph is passed repeatedly through the rectangular flume with a fixed rough bed. The dense rough mat is used as a rough bed which replica of a dense grass bed. The flow patterns are investigated at both lateral and longitudinal positions over three different cross-sections by using a micro Pitot tube and Acoustic Doppler Velocimeter (ADV). For two given flow depths, the velocities on both the rising and falling limbs are observed and analyzed. Hysteresis effect loop between stage-discharge (h ~Q) rating curve between the rising and falling limbs is illustrated. Turbulence characteristics, i. e., variations of lateral and vertical Reynolds stresses, are analyzed from measured fluctuation velocities.

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Section: Resultsmentioning

confidence: 99%

Turbulence Characteristics in a Rough Open Channel Under Unsteady Flow Conditions

et al. 2021

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“…The test section is 10 m from upstream, where flow stabilizes and uniform flow is observed. A tailgate is installed downstream to achieve uniform flow conditions (Khuntia et al 2018(Khuntia et al , 2019.…”

Section: Methodsmentioning

confidence: 99%

Calibrating Coefficients of Emerged Vegetative Open Channel Flow

2021

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Vegetation is a crucial element of the river system. In open channel hydraulics, vegetation has a significant effect on flow structure; it offers resistance to the flow and responsible for flood level increase by reducing the carrying capacity of the flood. Researchers throughout the globe have analyzed the resistance provided by vegetation with a theoretical and experimental study. Many flow and channel parameters affect the flow resistance. Out of all these parameters, vegetation is an influential one in vegetative channels. It alters the velocity profiles in an open channel, which affects the roughness coefficients. The roughness coefficients in vegetative channels vary with the flow depths and sections. Therefore, due to the complex structure, it is tedious to come up with a flow model based on previous research. Though it is challenging to determine directly from a field exercise, a laboratory study has been carried out in emergent vegetation at Hydraulics Engineering Laboratory, NITR, to explore the vegetation influence. The Shiono Knight Method (SKM) has been applied to calculate the boundary shear stress and depth-averaged velocity distribution in an open channel flow. For this purpose, three calibrating coefficients, namely bed friction (f), dimensionless eddy viscosity (λ), and transverse gradient for secondary flow ( ), have been incorporated to modify the existing SKM. A mathematical model was formulated to find the calibrating coefficients in the channel and compared with the SKM.

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“…The results showed that the model was more suitable for the prediction of bend flow velocity of 60 o [ 2 ]. Khuntia et al [ 3 ] proposed to use multivariate regression to analyze and predict the river velocity and found that the machine learning algorithm performed better than the traditional Shiono and Knight methods [ 3 ]. Based on particle tracking velocimetry, Eltner et al [ 4 ] collected and estimated River images.…”

Section: Related Workmentioning

confidence: 99%

Prediction and Estimation of River Velocity Based on GAN and Multifeature Fusion

Wang

Chen

Wang

2022

Computational Intelligence and Neuroscience

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The necessity of predicting and estimating river velocity motivates the development of a prediction method based on GAN image enhancement and multifeature fusion. In this method, in order to improve the image quality of river velocity, GAN network is used to enhance the image, so as to improve the integrity of image data set. In order to improve the accuracy of prediction, the image is extracted and fused with multiple features, and the extracted multiple features are taken as the input of CNN, so as to improve the prediction accuracy of convolution neural network. The results show that when the velocity is 0.25 m/s, 0.50 m/s, and 0.75 m/s, the accuracy of improved method can reach 85%, 90%, and 92%, which are higher than SVM, VGG-16, and BPNET algorithms. The above results indicate that the improvement has certain positive value and practical application value.

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Prediction of depth-averaged velocity in an open channel flow

Cited by 12 publications

References 16 publications

Turbulence Characteristics in a Rough Open Channel Under Unsteady Flow Conditions

Turbulence Characteristics in a Rough Open Channel Under Unsteady Flow Conditions

Calibrating Coefficients of Emerged Vegetative Open Channel Flow

Prediction and Estimation of River Velocity Based on GAN and Multifeature Fusion

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