Discrete and Continuous Modeling of Heat and Mass Transport in Drying of a Bed of Iron Ore Pellets

Ljung, Anna‐Lena; Frishfelds, Vilnis; Lundström, T. Staffan; Marjavaara, B. Daniel

doi:10.1080/07373937.2012.662567

Cited by 37 publications

(22 citation statements)

References 26 publications

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“…11. Naturally, the temperature front in the discrete model is not straight as in the CFD model as also shown in Ljung et al [31]. This is because of the natural dispersion in the system due to the arrangement of the pellets and the uneven velocity distribution as disclosed numerically in Frishfelds et al [14] and experimentally in Khayamyan et al [21,22].…”

Section: Resultsmentioning

confidence: 82%

“…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%

“…As in the models by Ljung et al [31] and Burström et al [6] additional source terms S f and S s have been incorporated into the energy equations to account for temperature gradients inside the spheres. This yields the following extra conduction term in the energy equation:…”

Section: Turbulence Modellingmentioning

confidence: 99%

“…The discrete model in this article has earlier been used to model mass and heat transfer in a two-dimensional system of pellets [31]. The model has now been developed to handle a three-dimensional system of pellets [18] and it can now also be used to model compressible flows [6].…”

Section: Introductionmentioning

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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Section: Resultsmentioning

confidence: 82%

Section: Discrete Modelmentioning

confidence: 99%

Section: Turbulence Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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The calculations of dispersion coefficients inside two‐dimensional randomly packed beds of circular particles

et al. 2012

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in Wiley Online Library (wileyonlinelibrary.com).The longitudinal (D L ) and transverse (D T ) dispersion coefficients in two-dimensional (2-D) randomly packed beds of circular particles in a laminar flow regime are derived. A 2-D discrete system of particles is divided into cells using modified Voronoi diagrams. The relationship between the variation of the stream function and the averaged vorticity is obtained from computational fluid dynamics (CFD) simulations. The whole flow pattern is then obtained by using the principle of energy dissipation rate minimization. The obtained values of D L agree well with 3-D experimental data for all velocities investigated. At very high velocities, D T in 2-D appears to be higher than 3-D experimental data. In addition, the effects of particle-size distributions, packing structure, and porosity on the D L and D T were studied. One result was that an increase in the width of the particle-size distribution resulted in higher values of D L and D T at high velocities.

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

et al. 2013

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The longitudinal (D L ) and transverse (D T ) dispersion coefficients for flow through randomly packed beds of discrete monosized spherical particles are studied. The three-dimensional (3-D) porous-medium model consists of thousands of spherical particles that are divided into cells using Voronoi diagrams. The relationship between the variation of the dual stream function and the vorticity between neighboring particles is derived using Laurent series. The whole flow pattern at low particle Reynolds number is then obtained by minimization of the dissipation rate of energy with respect to the dual stream function. The

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Discrete and Continuous Modeling of Heat and Mass Transport in Drying of a Bed of Iron Ore Pellets

Cited by 37 publications

References 26 publications

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

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

The calculations of dispersion coefficients inside two‐dimensional randomly packed beds of circular particles

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

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