Numerical derivation of dispersion coefficients for flow through three‐dimensional randomly packed beds of monodisperse spheres

Jourak, Amir; Hellström, J. Gunnar I.; Lundström, T. Staffan; Frishfelds, Vilnis

doi:10.1002/aic.14284

Cited by 18 publications

(16 citation statements)

References 54 publications

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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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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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“…Many researchers have modelled pore level flow, as such, theoretically or numerically, e.g. Geller and Hunt (1993), Hellström et al (2010b), Ljung et al (2012), Jourak et al (2014) and Fabricius et al (2016) and Pérez-Ràfols et al 2016). Also, experiments have been performed on simplified geometries like parallel tubes (Khayamyan et al 2014;Khayamyan and Lundström 2015).…”

Section: Introductionmentioning

confidence: 99%

Transitional and Turbulent Flow in a Bed of Spheres as Measured with Stereoscopic Particle Image Velocimetry

et al. 2017

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Stereoscopic particle image velocimetry has been used to investigate inertia dominated, transitional and turbulent flow in a randomly packed bed of monosized PMMA spheres. By using an index-matched fluid, the bed is optically transparent and measurements can be performed in an arbitrary position within the porous bed. The velocity field observations are carried out for particle Reynolds numbers, Re p , between 20 and 3220, and the sampling is done at a frequency of 75 Hz. Results show that, in porous media, the dynamics of the flow can vary significantly from pore to pore. At Re p around 400 the spatially averaged time fluctuations of total velocity reach a maximum and the spatial variation of the time-averaged total velocity, u tot increases up to about the same Re p and then it decreases. Also in the studied planes, a considerable amount of the fluid moves in the perpendicular directions to the main flow direction and the time-averaged magnitude of the velocity in the main direction, u x , has an averaged minimum of 40% of the magnitude of u tot at Re p about 400. For Re p > 1600, this ratio is nearly constant and u x is on average a little bit less than 50% of u tot . The importance of the results for longitudinal and transverse dispersion is discussed.

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“…It is defined as the average ratio between a straight line between two points in the porous media and the actual flow path. In the work by Jourak et al [19] it was concluded that the tortuosity factor for the gas phase within 3D randomly mono-sized spherical packing is in the range of 1.38-1.49. In the current study it is thus set to ς = 1.4 which also agrees well with what is suggested in other literature.…”

Section: Dpmentioning

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%

“…In the second strategy a discrete micro-level model is applied where the pore space between the pellets is divided into cells with modified Voronoi diagrams. The convective heat transfer of hot fluid flow through the system including dispersion due to random configuration of the pellets is then modelled [16,19,31]. A random packing of spheres is considered and temperature distributions in time are compared resulting in conclusions about the advantages and drawbacks of respective model.…”

Section: Introductionmentioning

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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Numerical derivation of dispersion coefficients for flow through three‐dimensional randomly packed beds of monodisperse spheres

Cited by 18 publications

References 54 publications

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

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

Transitional and Turbulent Flow in a Bed of Spheres as Measured with Stereoscopic Particle Image Velocimetry

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

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