Estimation of Open Channel Roughness by using Gradual Varied Flow Profiles

Open channel flow parameter estimation is an inverse problem, which involves the prediction of a function within a domain, given an error criterion with respect to a set of observed data. Various numerical methods have been developed to estimate open channel flow parameters. For this study, Genetic Algorithm optimization technique is selected. Because of its inherent characteristics, Genetic Algorithm optimization technique avoids the subjectivity, long computation time and ill-posedness often associated with conventional optimization techniques. An accurate estimation of roughness coefficients is of vital importance in any open channel flow study. In flood routing in natural rivers, most channels have compound sections and the roughness values in main channel and flood plains are usually different. In order to have more accurate results, the roughness of main channel and flood plains should be considered separately. It is possible to identify the values of roughness using optimization methods. However, studies on the inverse problem of estimating roughness values in compound channels are still limited. The present study involves estimation of open channel flow parameters having different bed materials invoking data of Gradual Varied Flow (GVF). Use of GVF data facilitates estimation of flow parameters. The necessary data base was generated by conducting laboratory experiments in Hydraulics Lab of civil Engineering at IIT Roorkee. In the present study, the efficacy of the Genetic Algorithm (GA) optimization technique is assessed in estimation of open channel flow parameters from the collected experimental data. Computer codes are developed to obtain optimal flow parameters Optimization Technique. Applicability, Adequacy& robustness of the developed code are tested using sets of theoretical data generated by experimental work. Estimation of Mannings Roughness coefficient from the collected experimental work data by using Mannings equation& GVF equation were made. The model is designed to arrive at such values of the decision variables that permit minimized mismatch between the observed& the computed GVF profiles. A simulation model was developed to compute GVF depths at preselected discrete sections for given downstream head and discharge rate. This model is linked to an optimizer to estimate optimal value of decision variables. The proposed model is employed to a set of laboratory data for three bed materials (i. e, d50=20mm, d50=6mm and lined concrete). Application of proposed model reveals that optimal value of fitting parameter ranges from 1.42 to 1.48 as the material gets finer. This value differs from the currently documented value i. e.1.5. The optimal estimates of Mannings n of three different bed conditions of experimental channel appear to be higher than the corresponding reported /Stricklers estimates.