Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2583
Title: Calibrating Coefficients for Prediction of Depth Averaged Velocity Distribution
Authors: Khuntia, J.R
Devi, K.
Khatua, K.K
Keywords: Shiono and Knight Method
Calibrating coefficients
Multi-linear regression
Depth averaged velocity
Issue Date: Dec-2016
Citation: 21st International Conference on Hydraulics, Water Resources and Coastal Engineering (HYDRO 2016 International), Central Water & Power Research Station (CWPRS), Pune, India, 8-10 Dec, 2016
Abstract: The Shiono Knight method offers an improved numerical solution to predict depth averaged velocity distribution and boundary shear distribution in an open channel flow. While applying this method one has to calibrate the coefficients like 𝑓, 𝜆 and 𝑘 which account for bed shear, lateral shear and secondary flow respectively. Experimental investigations have been carried out to find out the calibrating coefficients. These calibrating coefficients are found to vary laterally, flow depth to flow depth and due to change of the bed roughness. Experiments have been conducted on both trapezoidal and rectangular open channel for different flow conditions and roughness conditions. Keeping the geometry and flow conditions same, boundary roughness has been changed. The effect of roughness on evaluating the calibrating coefficients has been studied and modelled. Multi-linear regression models to predict 𝑓, 𝜆 and 𝑘 have been attempted. Each model incorporates four non dimensional geometric, hydraulic and roughness parameters. The parameters are roughness ratio, relative lateral distance, aspect ratio Reynolds no. The paper demonstrates the ability of the model to provide depth averaged velocity distribution successfully for both smooth and rough channels under different flow condition recorded for this purpose as well as for FCF data for a better comparison.
Description: Copyright belongs to the proceeding publisher
URI: http://hdl.handle.net/2080/2583
Appears in Collections:Conference Papers

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