Longitudinal Dispersion Coefficient: Effects of Particle-Size Distribution

Jourak, Amir; Frishfelds, Vilnis; Hellström, J. Gunnar I.; Lundström, T. Staffan; Herrmann, Inga; Hedström, Annelie

doi:10.1007/s11242-013-0159-5

Cited by 18 publications

(21 citation statements)

References 30 publications

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“…Thus, some colloids are getting slowed down having to pass through small pore spaces, where other colloids are managing to stay in fast flow paths. This result is in agreement with the work published by Jourak et al [2013] who studied theoretically the effects of particle-size distribution on the longitudinal dispersion coefficient in models of packed columns, and the work published by Bennacer et al [2013] who investigated experimentally the transport and deposition of suspended particles with different size distributions in water-saturated porous media. However, Keller et al [2004], Vasiliadou and Chrysikopoulos [2011], and Syngouna and Chrysikopoulos [2011] reported that a L decreases with increasing d p .…”

Section: 1002/2014wr016094supporting

confidence: 92%

Colloid particle size‐dependent dispersivity

Chrysikopoulos,

Katzourakis

2015

Water Resources Research

122

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Laboratory and field studies have demonstrated that dispersion coefficients evaluated by fitting advection-dispersion transport models to nonreactive tracer breakthrough curves do not adequately describe colloid transport under the same flow field conditions. Here an extensive laboratory study was undertaken to assess whether the dispersivity, which traditionally has been considered to be a property of the porous medium, is dependent on colloid particle size and interstitial velocity. A total of 48 colloid transport experiments were performed in columns packed with glass beads under chemically unfavorable colloid attachment conditions. Nine different colloid diameters and various flow velocities were examined. The breakthrough curves were successfully simulated with a mathematical model describing colloid transport in homogeneous, water-saturated porous media. The experimental data set collected in this study demonstrated that the dispersivity is positively correlated with colloid particle size, and increases with increasing velocity. The dispersivity values determined in this laboratory study were compared with 380 dispersivity values from earlier laboratory and field-scale solute, colloid and biocolloid transport studies published in the literature.

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Section: 1002/2014wr016094supporting

confidence: 92%

Colloid particle size‐dependent dispersivity

Chrysikopoulos,

Katzourakis

2015

Water Resources Research

122

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“…A comprehensive investigation of the flow at the pore level may result in more sophisticated models with heterogeneous distributions of variables instead of the common assumption of homogeneous profiles (Andrigo et al 1999;Eigenberger 1992). To reveal the detailed flow within porous media, the pore level flow has been modelled theoretically and numerically (Geller and Hunt 1993;Hill et al 2001;Powers et al 1994;Payatakes 1982;Hellström et al 2010;Jourak et al 2013Jourak et al , 2014Fabricius et al 2016;Burström et al 2016). There is also some experimental work on the detailed scale.…”

Section: Introductionmentioning

confidence: 99%

Measurements of Transitional and Turbulent Flow in a Randomly Packed Bed of Spheres with Particle Image Velocimetry

et al. 2016

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Particle image velocimetry (PIV) has been used to investigate transitional and turbulent flow in a randomly packed bed of mono-sized transparent spheres at particle Reynolds number, 20 < Re p < 3220. The refractive index of the liquid is matched with the spheres to provide optical access to the flow within the bed without distortions. Integrated pressure drop data yield that Darcy law is valid at Re p ≈ 80. The PIV measurements show that the velocity fluctuations increase and that the time-averaged velocity distribution start to change at lower Re p . The probability for relatively low and high velocities decreases with Re p and recirculation zones that appear in inertia dominated flows are suppressed by the turbulent flow at higher Re p . Hence there is a maximum of recirculation at about Re p ≈ 400. Finally, statistical analysis of the spatial distribution of time-averaged velocities shows that the velocity distribution is clearly and weakly self-similar with respect to Re p for turbulent and laminar flow, respectively.

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“…In this approach discrete Voronoi diagrams are used to divide the system of particles into cells, each containing one pellet. The pellets Details about the discrete model can be found in Burström et al [6], Jourak et al [18] and Ljung et al [31]. …”

Section: Discrete Modelmentioning

confidence: 99%

“…However, when varying the turbulence intensity at the inlet it turns out that the exact value does not influence the overall result, see Figs. 16,17,18,and 19 where the turbulence intensity values used are based on simulation done with a relevant up-and downstream geometry. The difference between laminar and turbulent gas temperature for five points in the bed is plotted as a function of time to highlight the influence of turbulence and inlet intensity.…”

Section: Turbulencementioning

confidence: 99%

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Modelling heat transfer during flow through a random packed bed of spheres

et al. 2017

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Heat transfer in a random packed bed of monosized iron ore pellets is modelled with both a discrete three-dimensional system of spheres and a continuous Computational Fluid Dynamics (CFD) model. Results show a good agreement between the two models for average values over a cross section of the bed for an even temperature profiles at the inlet. The advantage with the discrete model is that it captures local effects such as decreased heat transfer in sections with low speed. The disadvantage is that it is computationally heavy for larger systems of pellets. If averaged values are sufficient, the CFD model is an attractive alternative that is easy to couple to the physics up-and downstream the packed bed. The good agreement between the discrete and continuous model furthermore indicates that the discrete model may be used also on non-Stokian flow in the transitional region between laminar and turbulent flow, as turbulent effects show little influence of the overall heat transfer rates in the continuous model.

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Longitudinal Dispersion Coefficient: Effects of Particle-Size Distribution

Cited by 18 publications

References 30 publications

Colloid particle size‐dependent dispersivity

Colloid particle size‐dependent dispersivity

Measurements of Transitional and Turbulent Flow in a Randomly Packed Bed of Spheres with Particle Image Velocimetry

Modelling heat transfer during flow through a random packed bed of spheres

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