scholarly journals AN EVALUATION OF SUSPENDED SEDIMENT CONCENTRATION MODELS UNDER BREAKING WAVES

2018 ◽  
pp. 42
Author(s):  
Gabriel Lim ◽  
Ravindra Jayaratne ◽  
Tomoya Shibayama
Keyword(s):  
Kinetic Energy ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Critical Evaluation ◽  
Sediment Concentration ◽  
Breaking Waves ◽  
Breaking Wave ◽  
Wave Conditions ◽  
Improved Accuracy ◽  
Bed Friction

Implementing the effects of turbulent kinetic energy (TKE) is essential in producing accurate suspended sediment concentration (SSC) models, especially under breaking wave conditions. SSC is commonly attributed to two different turbulent sources under breaking wave conditions: 1) bed-friction and 2) breaking-induced turbulent vortices. Numerous studies have endeavoured to quantify the effects of TKE and incorporate them into SSC models. To name a few: Mocke & Smith (1992, henceforth MS92), Shibayama & Rattanapitikon (1993, henceforth SR93), Jayaratne & Shibayama (2007, henceforth JS07), and Yoon et al. (2015, henceforth Y15). The present study evaluates these 4 existing SSC models and validates them against recently published datasets from the ‘CROSSTEX’ (Yoon & Cox, 2010), ‘SandT-Pro’ (Ribberink et al., 2014) and ‘SINBAD’ (vdZ et al. 2015) projects. Following critical evaluation, suggestions are made to enhance existing SSC models, and these findings are then incorporated into producing two new SSC models that indicate improved accuracy.

scholarly journals Numerical Simulation of the Sea Bottom Modifications Behind a T-head Groin

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Diffusion Equation ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Surf Zone ◽  
Fluid Dynamic ◽  
Sediment Concentration ◽  
Breaking Wave ◽  
Advection Diffusion Equation ◽  
Advection Diffusion ◽  
Sea Bottom

In this paper, we simulate the sea bottom modifications produced by the presence of a T-head groin. We present a simulation model of sea bottom modifications composed of two sub-models: a two-dimensional phase-resolving model that simulate the variation of the fluid dynamic variables inside the wave; a second sub-model to simulate the sea bottom modifications, in which the suspended sediment concentration is calculated by the wave-averaged advection-diffusion equation. The fluid motion equation and the concentration equation are expressed in a new contravariant formulation. The velocity fields from deep water up to just seaward of the surf-zone are simulated by a new integral contravariant form of the Fully Nonlinear Boussinesq Equations. The new integral form of the proposed continuity equation does not contain the dispersive term. The Nonlinear Shallow Water Equations, expressed in an integral contravariant form, are solved in order to simulate the breaking wave propagation. The momentum equation, integrated over the turbulent boundary layer, is solved to calculate the near-bed instantaneous flow velocity and the intra-wave hydrodynamic quantities. Starting from the contravariant formulation of the advection–diffusion equation for the suspended sediment concentration, it is possible to calculate the sea bottom modification. The advective sediment transport terms in the advection-diffusion equation are formulated according to a quasi-three-dimensional approach

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

scholarly journals Suspended sediment concentration field quantified from a calibrated MultiBeam EchoSounder

2021 ◽  
Vol 180 ◽  
pp. 108107
Author(s):  
Guillaume Fromant ◽  
Nicolas Le Dantec ◽  
Yannick Perrot ◽  
France Floc'h ◽  
Anne Lebourges-Dhaussy ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Concentration Field ◽  
Sediment Concentration ◽  
Multibeam Echosounder
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