Loss of Energy in the Converging Compound Open Channels

Naik, B.; Khatua, Kishanjit Kumar; Padhi, Ellora; Singh, Prateek Kumar

doi:10.1007/s13369-017-2963-7

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…Thus, there are totally six sets of measured α and β values in two different reaches, which have different hydraulic and bed characteristics. Figure 17 shows the plan view of Reach 1 and 2 with straight cross sections for 1, 2, 3 and 4 in Reach 1, and 1, 2, 10, 11, 12, 14 and 15 in Reach 2, respectively, which has been used in many different studies and found reliable [46][47][48][49][50]. Among all cross sections in both reaches, only such cross sections were considered where they have straight non-prismatic configuration and also have hydraulic characteristics under the range of parameters for which the models are developed.…”

Section: Application Of the Model To Natural River Systemmentioning

confidence: 99%

Models for kinetic energy and momentum correction coefficients for non-prismatic compound channels using regression and gene expression programming

et al. 2019

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This paper studies the effect of different independent variables related to the characteristics of the compound channels on the kinetic energy and momentum correction coefficients (termed as α and β, respectively) of non-prismatic compound channels. A series of experiments were carried out in a converging compound channel with three converging angles, six width ratios, six relative depths and three relative distances. Meanwhile, flow data sets from three different authors were used for diverging compound channels, which include simple rectangular and trapezoidal cross sections. In the present study, conventional multivariable regression technique and gene expression programming were used for developing different models for calculating kinetic energy and momentum correction coefficients for converging and diverging compound channels, respectively. The independent variables used in the curve fitting process were also tested for their accountability and influence on the proposed models. The developed models were finally established with the validation data set and a natural river system.

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Section: Application Of the Model To Natural River Systemmentioning

confidence: 99%