A Two-Dimensional Depth-Averaged Sediment Transport Mobile-Bed Model with Polygonal Meshes

Lai, Yong G.

doi:10.3390/w12041032

Cited by 17 publications

(16 citation statements)

References 40 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the SDAKE model does not give the true depth-averaged values for the turbulent kinetic energy, it is of interest to know how the virtual values, k, differ from the true ones, k. A simple way to do that is to use the SDAKE model (Equations (10) and (11)) to predict the turbulent kinetic energy for the case of uniform flow over a flat bed. In the case of uniform flow over a flat bed, all the spatial derivatives in Equations ( 10) and ( 11) can be dropped and, consequently, the two equations can be reduced to:…”

Section: Uniform Flow Over Flatbed Benchmark Casementioning

confidence: 99%

“…Both depth-averaged models and VAM flow models require the use of relevant turbulence models, mainly to work as closure equations for the mathematical differential terms resulting from the time averaging of the Navier Stocks equations and to capture the dominating length and velocity scales in the turbulence/eddy structure of the flow field [6][7][8][9][10][11][12]. The two-transport equations model (Rastogi and Rodi's k-ε model) is the standard turbulence closure model that was introduced early in 1978 for depth-averaged models [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Moment-Based Depth-Averaged K-ε Model for Predicting the True Turbulence Intensity over Bedforms

Elgamal

2022

Water

View full text Add to dashboard Cite

Turbulence models are critical for depth-averaged flow models in at least two ways: (i) as closures for momentum equations and (ii) as indicators of the spatial variability in the turbulence intensity field, which is crucial for sediment transport and bedform evolutions. This paper introduces a novel moment-based depth-averaged k-ε turbulence (MDAKE) model that could be considered as a revised version for the standard k-ε Rastogi–Rodi (SDAKE) model and can be used to estimate the true values for the depth-averaged turbulence kinetic energy in more complex and varied flow conditions with accelerating–decelerating flow fields. The study in hand shows that the SDAKE model tends to overestimate the true depth-averaged turbulent kinetic energy () by 50 to 130% in the benchmark case of uniform flow over a flatbed. Further, the SDAKE model assumes that the bed shear velocity is an appropriate scale for the generation terms of both turbulent kinetic energy and dissipation. When bed topographic features vary, a shear flow zone is formed and the assumption is invalid. Since most of the turbulence is generated by shear flow zones away from the bed, the SDAKE model’s estimates for the depth-averaged turbulent kinetic energy field are out of phase with measurements for the flow over a train of bedforms. Therefore, a newly developed depth-averaged KE model based on the moment concept (MDAKE) is presented here. The model replaces bed shear velocity with the integral moment velocity scale (). The calibrated MDAKE model is used to predict turbulent kinetic energy over a train of bedforms. The results of the MDAKE model are in phase and generally in reasonable agreement with the measurements.

show abstract

Section: Uniform Flow Over Flatbed Benchmark Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Moment-Based Depth-Averaged K-ε Model for Predicting the True Turbulence Intensity over Bedforms

Elgamal

2022

Water

View full text Add to dashboard Cite

show abstract

“…Considering the bed scour of non-equilibrium sediment migration in floods, a depth-averaged two-dimensional (2D) hydrodynamic model can be used to estimate the water depth and flow velocity to assess the time variation of the total scour depth of the pier. The sedimentation and river hydraulics two-dimensional (SRH-2D) model developed by the U.S. Bureau of Reclamation is a depth-averaged 2D hydraulic and sediment model that solves 2D shallow water flow equations through unstructured hybrid meshes [ 51 ]. The model can simulate the hydrodynamic flows in fluvial rivers with hydraulic structures such as bridge piers.…”

Section: Scour Depth Simulation For Early Warningmentioning

confidence: 99%

“…Regarding the phenomena of a mobile riverbed in floods, sediment transport can simulate the flow field and pattern by a numerical hydrodynamic model to assess the time variation of the total scour depth of the pier. The sedimentation and river hydraulics two-dimensional (SRH-2D) model is a depth-averaged 2D hydraulic and sediment model, simulating flows in fluvial rivers with hydraulic structures such as bridge piers [ 51 ]. The model is suitable for modeling riverbed migration, such as the general scour of the riverbed.…”

Section: Introductionmentioning

confidence: 99%

The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods

Lin

Lee

Chang

et al. 2021

Sensors

View full text Add to dashboard Cite

Scour around bridge piers remains the leading cause of bridge failure induced in flood. Floods and torrential rains erode riverbeds and damage cross-river structures, causing bridge collapse and a severe threat to property and life. Reductions in bridge-safety capacity need to be monitored during flood periods to protect the traveling public. In the present study, a scour monitoring system designed with vibration-based arrayed sensors consisting of a combination of Internet of Things (IoT) and artificial intelligence (AI) is developed and implemented to obtain real-time scour depth measurements. These vibration-based micro-electro-mechanical systems (MEMS) sensors are packaged in a waterproof stainless steel ball within a rebar cage to resist a harsh environment in floods. The floodwater-level changes around the bridge pier are performed using real-time CCTV images by the Mask R-CNN deep learning model. The scour-depth evolution is simulated using the hydrodynamic model with the selected local scour formulas and the sediment transport equation. The laboratory and field measurement results demonstrated the success of the early warning system for monitoring the real-time bridge scour-depth evolution.

show abstract

“…In some literature, sediment transport dynamics are modeled using a hydrodynamic sub-model based on shallow water equations coupled with the sediment concentration equation and the bedload equation, in the context of free-boundary nonhomogeneous water flow. Sediment transport in such situations has been studied numerically by [2], [6], [15], [19], [24], [23], [27], [44], [21], [33], [44] and experimentally in famous works as [12], [3], [32], [42]. Several sediment transport models have been previously developed.…”

Section: Introductionmentioning

confidence: 99%

A new class of sediment transport models: Derivation and numerical solutions

Ndengna,

Bandji,

Njifenjou

2024

Preprint

View full text Add to dashboard Cite

In coastal or estuarine environments the water fluctuations withgreater length of correlations are more intense and modify the meanmotion. The literature does not provide an averaged sedimenttransport model (STM) for coastal zones that accounts for both meanand fluctuating motions over an abrupt mobile bottom. Models basedon water are commonly used to describe sediment transport, but theyare not applicable when the flow becomes turbulent. These modelsonly consider the mean motion. To account the fluctuating motion ina STM, we assume $ |u -\overline{u}|^k \leq \tau^k h, \;\; k>0$ ($h= \mathcal{O}(\varepsilon)$ is the water depth, $\lambda = const$).However, distortion no exist always since during the dam break overerodible bed, the water near the bed can become dense thus weaklyturbulent. To always ensure the turbulence existence, we assume that$\tau = \mathcal{O}(\gamma^\alpha) \;\; \varepsilon^2 \ll \gamma \ll1$ and $\rho = \mathcal{O}(\gamma)$ with $\gamma = const$.Additionally we assume a weakly concentrated approximation ($d_{50}= \mathcal{O}(\varepsilon^\alpha)$, $\delta = \mathcal{O}(\epsilon),1< \alpha <3$) to derive the model. The model obtained in thisstudy builds upon previous work by authors in 2007, 2012, and 2018,while also improving upon more recent models developed in 2022 and2023. The hyperbolic STM is subject to various complexities arisingfrom turbulence and several nonlinear coupled terms. Furthermore,solving this problem still poses a computational challenge in termsof accuracy, convergence, robustness, and efficiency. To addressthis challenge, we propose a new path-conservative method called $\text{HLL}_{**}$, which we combine with a modified Averaging method.The Essentially Non-Oscillatory (AENO) nonlinear reconstructionmethod is used to approximate the model. This method is noteworthybecause it generalizes several HLL-based schemes developed in recentyears. Numerical test cases have been performed.

show abstract

A Two-Dimensional Depth-Averaged Sediment Transport Mobile-Bed Model with Polygonal Meshes

Cited by 17 publications

References 40 publications

A Moment-Based Depth-Averaged K-ε Model for Predicting the True Turbulence Intensity over Bedforms

A Moment-Based Depth-Averaged K-ε Model for Predicting the True Turbulence Intensity over Bedforms

The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods

A new class of sediment transport models: Derivation and numerical solutions

Contact Info

Product

Resources

About