Solids velocity fluctuations in concentrated slurries

Abstract

Experiments were performed in a 265 mm diameter pipe loop with sand‐in‐water slurries (d50 = 0.371 mm). In situ solids volumetric concentrations ranging from 20–40 % by volume (0.20–0.45 L/L) and mixture velocities up to 6 m/s were tested. Measurements of the instantaneous and average solids velocity and turbulent intensity profiles across the centreline diameter of the pipe were obtained using a particle velocity probe. A CFD package, ANSYS CFX 14, was used to perform numerical simulations. Solids turbulent intensities were found to be greater near the bottom of the pipe where solids concentrations are typically higher and significant particle‐pipe wall interactions occur. The agreement between the numerical results and the experimental data was found to be concentration dependent, with relatively closer agreement at lower concentrations.

Analysis and discussion of two fluid modelling of pipe flow of fully suspended slurry

“…It is also noted that the relative contributions of the two phases to the total wall shear stress significantly changes with the amount of solids in the slurry, the major role being played by w l  for Another feature of slurry flows which is rather hard to determine experimentally, but can be easily obtained by means of a CFD simulation, is the velocity field. The available measurements reported in the literature (e.g., [25,58,[64][65][66][67]) agree that the maximum velocity is located in the upper part of the pipe, this effect being hardly visible for pseudo-homogeneous flow but becoming more and more enhanced as the vertical concentration gradient increases. As it is proven in Fig.…”

“…The authors assessed the predictive capacity of the computational model for different flow conditions, mostly in terms of concentration profiles, but they also explored the hydraulic gradient and the velocity profile. Subsequent studies from the same research group [24,25] reported the analysis of the influence of some features of the TFM (e.g., turbulence model, wall boundary conditions, and interstitial forces) and its validation with respect to concentration and velocity measurements. Kaushal et al [26] made use of the granular model embedded in the Ansys Fluent code to simulate the slurry flow of fine glass beads at high concentration in the FS regime.…”

Computational Fluid Dynamics Modelling of Liquid–Solid Slurry Flows in Pipelines: State-of-the-Art and Future Perspectives

“…To provide some context regarding the application of Eulerian-Eulerian-KTGF modelling to slurry flows, a high-level overview of the past 25 years of publications in this field shows that a vast majority of the studies relate to horizontal pipeline flows (e.g., [36,67,[132][133][134]), with only a few on vertical pipeline flows [63,135] and bends/fittings [136,137]. Pipe diameters of 10-500 mm, flow velocities between 1-9 m/s, bulk solids volume concentrations up to 0.5, density ratios of 1.18 (slush nitrogen [138]) to 4.35 (iron ore slurry [139]), and particle sizes of 0.004-2.4 mm have been studied.…”

“…Most of the applications of the KTGF model for simulations of slurry pipeline flow have been interpretative rather than predictive, as shown in Figure 18. Many of these interpretative studies (e.g., see Chen et al [132], Kaushal et al [67], Gopaliya et al [145,146], Zhang et al [75], Sultan et al [147], Li et al [148]) make comparisons between predicted and experimental macroscopic variables such a pressure drop and concentration gradient while others, like the study of Hashemi et al [134], use simulations to study difficult-to-measure variables, such as velocity fluctuations or turbulence attenuation. It is speculated that the preponderance of interpretive studies is inversely related to the attention paid to rigorous verification and validation (V&V) techniques.…”

“…Additionally, experimental databases should be expanded beyond the measurement of macroscopic variables like friction loss and time-averaged vertical solids distributions. This is important especially when studies like that of Hashemi et al [134] have shown that it is possible to match friction loss and vertical solids distribution data and yet obtain poor predictions of other important variables, such as particle velocity distributions. There is also an issue of poor quality of the experimental data used for comparison with the model predictions.…”