Velocity lag between particle and liquid in sediment-laden open channel turbulent flow

Pal, Debasish; Jha, Sanjeev; Ghoshal, Koeli

doi:10.1016/j.euromechflu.2015.11.003

Cited by 8 publications

(21 citation statements)

References 40 publications

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“…The reason is that shear stress increases with the increase of fluid velocity in terms of u * , which leads the particle to follow fluid velocity, and hence, velocity lag u l decreases. This characteristic was also observed in the work of Pal et al [13]. On the other hand, Figure 16 suggests that the velocity lag decreases with the increase of specific gravity s. This characteristic is also matched with the analysis of Cheng [12].…”

Section: Discussionsupporting

confidence: 77%

“…It is observed from Table 2 that the proposed model provided the least error for twenty test cases out of twenty-two experimental runs considered here. This result shows the superiority of the entropy-based model to the existing deterministic models of Cheng [12], Greimann et al [10] and Pal et al [13]. …”

Section: Comparison With Experimental Data and Other Modelssupporting

confidence: 50%

“…The proposed model was also compared with other existing models reported in the literature. The models of velocity lag selected were the models of Cheng [12], Greimann et al [10] and Pal et al [13]. These models are deterministic.…”

Section: Comparison With Experimental Data and Other Modelsmentioning

confidence: 99%

“…For the purposes of discussion of the model comparison, the models proposed in the present study and by Cheng [12], Greimann et al [10] and Pal et al [13] were denoted, respectively, as PM, CM, GM and DM. Hence, onwards, throughout the present study, we use these acronyms to refer to the corresponding models on velocity lag.…”

Section: Comparison With Experimental Data and Other Modelsmentioning

confidence: 99%

“…Cheng [12] presented a model of velocity lag, based on a hindered drag force in sediment-laden flow and stated that the model may not be well applicable when the velocity and concentration gradients were significant in the flow. Based on the hindered drag force on particle impact and viscous shear stress, particle-liquid and particle-particle interactions and the dispersion of sediment due to these interactions, Pal et al [13] developed a model of velocity lag and verified it with a wide range of experimental data. Apart from this, in recent years, researchers have investigated particle-fluid and particle-particle interactions in particulate turbulent flow using direct numerical simulations (DNS) (Vowinckel et al [14]).…”

Section: Introductionmentioning

confidence: 99%

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Entropy-Based Modeling of Velocity Lag in Sediment-Laden Open Channel Turbulent Flow

Kumbhakar

Kundu

Ghoshal

et al. 2016

Entropy

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Abstract:In the last few decades, a wide variety of instruments with laser-based techniques have been developed that enable experimentally measuring particle velocity and fluid velocity separately in particle-laden flow. Experiments have revealed that stream-wise particle velocity is different from fluid velocity, and this velocity difference is commonly known as "velocity lag" in the literature. A number of experimental, as well as theoretical investigations have been carried out to formulate deterministic mathematical models of velocity lag, based on several turbulent features. However, a probabilistic study of velocity lag does not seem to have been reported, to the best of our knowledge. The present study therefore focuses on the modeling of velocity lag in open channel turbulent flow laden with sediment using the entropy theory along with a hypothesis on the cumulative distribution function. This function contains a parameter η, which is shown to be a function of specific gravity, particle diameter and shear velocity. The velocity lag model is tested using a wide range of twenty-two experimental runs collected from the literature and is also compared with other models of velocity lag. Then, an error analysis is performed to further evaluate the prediction accuracy of the proposed model, especially in comparison to other models. The model is also able to explain the physical characteristics of velocity lag caused by the interaction between the particles and the fluid.

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Section: Discussionsupporting

confidence: 77%

Section: Comparison With Experimental Data and Other Modelssupporting

confidence: 50%

Section: Comparison With Experimental Data and Other Modelsmentioning

confidence: 99%

Section: Comparison With Experimental Data and Other Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Entropy-Based Modeling of Velocity Lag in Sediment-Laden Open Channel Turbulent Flow

Kumbhakar

Kundu

Ghoshal

et al. 2016

Entropy

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Theoretical modeling of suspended grain-size distribution in fluvial environment by stratification and secondary current approaches

Pal

Ghoshal

2017

Environ Fluid Mech

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Eddy interaction model for turbulent suspension in Reynolds-averaged Euler–Lagrange simulations of steady sheet flow

Cheng

Chauchat

Hsu

et al. 2018

Advances in Water Resources

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A Reynolds-averaged Euler-Lagrange sediment transport model (CFDEM-EIM) was developed for steady sheet flow, where the inter-granular interactions were resolved and the flow turbulence was modeled with a low Reynolds number corrected k − ω turbulence closure modified for two-phase flows. To model the effect of turbulence on the sediment suspension, the interaction between the turbulent eddies and particles was simulated with an eddy interaction model (EIM). The EIM was first calibrated with measurements from dilute suspension experiments. We demonstrated that the eddy-interaction model was able to reproduce the well-known Rouse profile for suspended sediment concentration.The model results were found to be sensitive to the choice of the coefficient, C 0 , associated with the turbulence-sediment interaction time. A value C 0 = 3 was suggested to match the measured concentration in the dilute suspension. The calibrated CFDEM-EIM was used to model a steady sheet flow experiment of lightweight coarse particles and yielded reasonable agreements with measured velocity, concentration and turbulence kinetic energy profiles. Further numerical experiments for sheet flow suggested that when C 0 was decreased to C 0 < 3, the simulation under-predicted the amount of suspended sediment in the dilute region and the Schmidt number is over-predicted (Sc > 1.0). Ad- * Corresponding author Email address: zcheng@whoi.edu (Zhen Cheng) Preprint submitted to Advances in Water ResourcesOctober 31, 2017ditional simulations for a range of Shields parameters between 0.3 and 1.2 confirmed that CFDEM-EIM was capable of predicting sediment transport rates similar to empirical formulations. Based on the analysis of sediment transport rate and transport layer thickness, the EIM and the resulting suspended load were shown to be important when the fall parameter is less than 1.25.

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Velocity lag between particle and liquid in sediment-laden open channel turbulent flow

Cited by 8 publications

References 40 publications

Entropy-Based Modeling of Velocity Lag in Sediment-Laden Open Channel Turbulent Flow

Entropy-Based Modeling of Velocity Lag in Sediment-Laden Open Channel Turbulent Flow

Theoretical modeling of suspended grain-size distribution in fluvial environment by stratification and secondary current approaches

Eddy interaction model for turbulent suspension in Reynolds-averaged Euler–Lagrange simulations of steady sheet flow

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