Flow Analysis and Its Prediction Methods for Compound Channels

Abstract

Compound channels are a common configuration of rivers. Dependable estimation of discharge capacity in a compound channel helps specialists to obtain reliable information regarding flood mitigation, construction of hydraulic structures, prediction of sediment loads and capacity of reservoir basin etc. which in turn assess the flow variation during catastrophic situations so as to minimize the loss. Many researchers have studied the traditional methods for estimating the discharge capacity of a compound channel based on the standard uniform flow formulas such as Chezy’s, Manning’s, and Darcy-Weisbach’s equations, by either treating the cross-section as a whole or by dividing it vertically, horizontally or diagonally into non-interacting subareas. Discharge predictions can be done by estimating the flow resistance of a compound channel. Various methods to evaluate the roughness coefficient for a compound section have been suggested by Lotter (1930), Krishnamurthy and Christensen (1970), Cox (1970) etc. which in-turn helpful to estimate the discharge capacity of the channel. Discharge calculations can also be done by the Coherence Method (COHM) suggested by Ackers (1992) for a compound open channel. It is a function of geometry where the geometrical wetted parameters of the channel is used to find discharge deficit factor (DISDEF). However, this method gives satisfactory results as compared to other methods but involves difficult formulae constituting a complex procedure. So, this discharge prediction method is not always suited in all sort of data sets. Conventional discharge prediction models such as Divided Channel Methods, Lotter, Cox, and COHM are used to analyzed the experimental data sets reported by other researcher, such as the large scale channel data of FCF (Flood Channel Facility) (1991), Straight channel data of Knight and Demetriou (1983), Myers (1984), Knight (1989), and the data observed at NIT Rourkela by Khatua (2008) and Mohanty (2013). The paper provides a distinct comparison among the different discharge predictions methods and their suitability among the different ranges of data sets.