Abstract: Modeling sediment transport processes by means of numerical approach often poses severe challenges. In this way, a number of techniques have been suggested to solve flow and sediment equations in decoupled, semi-coupled or fully coupled forms. Furthermore, in order to capture flow discontinuities, a number of techniques, like artificial viscosity and shock fitting, have been proposed for solving these equations which are mostly required careful calibration processes. In this research, a numerical scheme for solving shallow water and Exner equations in fully coupled form is presented. First-Order Centered scheme is applied for producing required numerical fluxes and the reconstruction process is carried out toward using Monotonic Upstream Scheme for Conservation Laws to achieve a high order scheme. In order to satisfy C-property of the scheme in presence of bed topography, Surface Gradient Method is proposed. Combining the presented scheme with fourth order Runge-Kutta algorithm for time integration yields a competent numerical scheme. In addition, to handle non-prismatic channels problems, Cartesian Cut Cell Method is employed. A trained Multi-Layer Perceptron Artificial Neural Network which is of Feed Forward Back Propagation (FFBP) type estimates sediment flow discharge in the model rather than usual empirical formulas. Hydrodynamic part of the model is tested for showing its capability in simulation of flow discontinuities, transcritical flows, wetting/drying conditions and non-prismatic channel flows. In this end, dam-break flow onto a locally non-prismatic converging-diverging channel with initially dry bed conditions is modeled. The morphodynamic part of the model is verified simulating dam break on a dry movable bed and bed level variations in an alluvial junction. The results show that the model is capable in capturing the flow discontinuities, solving wetting/drying problems even in non-prismatic channels and presenting proper results for movable bed situations. It can also be deducted that applying Artificial Neural Network, instead of common empirical formulas for estimating sediment flow discharge, leads to more accurate results.
Abstract: This study investigated the effect of cross sectional
geometry on sediment transport rate. The processes of sediment
transport are generally associated to environmental management,
such as pollution caused by the forming of suspended sediment in the
channel network of a watershed and preserving physical habitats and
native vegetations, and engineering applications, such as the
influence of sediment transport on hydraulic structures and flood
control design. Many equations have been proposed for computing
the sediment transport, the influence of many variables on sediment
transport has been understood; however, the effect of other variables
still requires further research. For open channel flow, sediment
transport capacity is recognized to be a function of friction slope,
flow velocity, grain size, grain roughness and form roughness, the
hydraulic radius of the bed section and the type and quantity of
vegetation cover. The effect of cross sectional geometry of the
channel on sediment transport is one of the variables that need
additional investigation. The width-depth ratio (W/d) is a
comparative indicator of the channel shape. The width is the total
distance across the channel and the depth is the mean depth of the
channel. The mean depth is best calculated as total cross-sectional
area divided by the top width. Channels with high W/d ratios tend to
be shallow and wide, while channels with low (W/d) ratios tend to be
narrow and deep. In this study, the effects of the width-depth ratio on
sediment transport was demonstrated theoretically by inserting the
shape factor in sediment continuity equation and analytically by
utilizing the field data sets for Yalobusha River. It was found by
utilizing the two approaches as a width-depth ratio increases the
sediment transport decreases.