Boundary shear distribution in open channel compound

Ghosh, Sukhendu; Jena, Somesh

doi:10.1680/iicep.1971.6191

Cited by 29 publications

(19 citation statements)

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“…Due to the secondary currents, the location of the maximum streamwise velocity occurs below the water surface [2], and the velocity contour lines are bulged towards the corners [3]. Secondary currents also increase bed shear stress near the corner [4], which may a ect the sediment transport capacity of the open-channel signiÿcantly.…”

Section: Introductionmentioning

confidence: 99%

Reynolds stress modelling of rectangular open-channel flow

Kang

Choi

2006

Int. J. Numer. Meth. Fluids

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SUMMARYA Reynolds stress model for the numerical simulation of uniform 3D turbulent open-channel ows is described. The ÿnite volume method is used for the numerical solution of the ow equations and transport equations of the Reynolds stress components. The overall solution strategy is the SIMPLER algorithm, and the power-law scheme is used to discretize the convection and di usion terms in the governing equations. The developed model is applied to a ow at a Reynolds number of 77 000 in a rectangular channel with a width to depth ratio of 2. The simulated mean ow and turbulence structures are compared with measured and computed data from the literature. The computed ow vectors in the plane normal to the streamwise direction show a small vortex, called inner secondary currents, located at the juncture of the sidewall and the free surface as well as the free surface and bottom vortices. This small vortex causes a signiÿcant increase in the wall shear stress in the vicinity of the free surface. A budget analysis of the streamwise vorticity is carried out. It is found that both production terms by anisotropy of Reynolds normal stress and by Reynolds shear stress contribute to the generation of secondary currents.

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Section: Introductionmentioning

confidence: 99%

Reynolds stress modelling of rectangular open-channel flow

Kang

Choi

2006

Int. J. Numer. Meth. Fluids

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“…It is well known that this value is decreased when there is appreciable suspended sediment; it also varies when secondary flows are present (e.g. Ghosh and Roy (1970), Rozovskii (1961), Yen (1972)). Thus if K is evaluated over bed waves (of any scale) from semilogarithmic velocity profiles defined throughout the full depth (instead of close to the bed), its value varies systematically about 0.4 according to the location of the velocity profile on the bed wave (e.g.…”

Section: Introductionmentioning

confidence: 99%

Velocity profiles and bed shear stress over various bed configurations in a river bend

Bridge

Jarvis

1977

Earth Surf. Process.

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SUMMARYVertical velocity profiles measured over various bed configurations (plane beds, ripples, and dunes) in the meandering River South Esk, Glen Clova, Scotland are presented on semilogarithmic paper. Local bed shear stress and roughness height are calculated from the lowermost parts of the profiles using the Karman-Prandtl law of the wall; these parameters, and the geometrical properties of the profiles, are related to the various bed configurations. A graphical model is used to identify profiles developed on specific regions of dune geometry, in order to discriminate those profiles that define bed shear effective in transporting sediment over dunes. An assessment is made of the errors involved in estimating local mean velocity from extrapolating the law of the wall to the water surface. A Darcy-Weisbach friction coefficient is related to bed configuration and local stream power.

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“…In order to predict the mean bed and wall shear stress in smooth rectangular channels, the experimental results of Cruff (1965), Ghosh and Roy (1970), Kartha and Leutheusser (1970), Myers (1978), Knight and Macdonald (1979), Knight (1981), Noutsopoulos and Hadjipanos (1982), Knight et al (1984) and Seckin et al (2006) were used. Different flumes with rectangular cross sections and several aspect ratios (B/h) were employed in their experiments to estimate the mean bed shear stress and wall shear stress.…”

Section: Data Description Usedmentioning

confidence: 99%

Using two soft computing methods to predict wall and bed shear stress in smooth rectangular channels

2017

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Two soft computing methods were extended in order to predict the mean wall and bed shear stress in open channels. The genetic programming (GP) and Genetic Algorithm Artificial Neural Network (GAA) were investigated to determine the accuracy of these models in estimating wall and bed shear stress. The GP and GAA model results were compared in terms of testing dataset in order to find the best model. In modeling both bed and wall shear stress, the GP model performed better with RMSE of 0.0264 and 0.0185, respectively. Then both proposed models were compared with equations for rectangular open channels, trapezoidal channels and ducts. According to the results, the proposed models performed the best in predicting wall and bed shear stress in smooth rectangular channels. The obtained equation for rectangular channels could estimate values closer to experimental data, but the equations for ducts had poor, inaccurate results in predicting wall and bed shear stress. The equation presented for trapezoidal channels did not have acceptable accuracy in predicting wall and bed shear stress either.

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Boundary shear distribution in open channel compound

Cited by 29 publications

References 0 publications

Reynolds stress modelling of rectangular open-channel flow

Reynolds stress modelling of rectangular open-channel flow

Velocity profiles and bed shear stress over various bed configurations in a river bend

Using two soft computing methods to predict wall and bed shear stress in smooth rectangular channels

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