Discrete and continuous modelling of convective heat transport in a thin porous layer of mono sized spheres

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

doi:10.1007/s00231-016-1792-7

Cited by 3 publications

(7 citation statements)

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“…If the pellets just had point contact in the discrete model it would respond to an χ s = 0 in the continuous model. In this work the conduction between the pellets modelled by an analytical expression in the discrete model and χ s is set to 0.2 as in Burström et al [6]. The additional source terms S f and S s also appears in the transport equations for turbulence, therefore they will be described more explicitly in the next chapter.…”

Section: Governing Equationsmentioning

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%

“…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%

“…Heat transfer to a bed of iron ore pellets is therefore examined numerically on several scales. In an earlier work by Burström et al [6] a continuous model was compared against a discrete Voronoi discretization model showing that mean values can be approximated with a 1D model. Here a full Navier-Stokes model is added to the comparison with a turbulent flow and an uneven temperature profile on the inlet.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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: Governing Equationsmentioning

confidence: 99%

Section: Turbulence Modellingmentioning

confidence: 99%

Section: Discrete Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…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%

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

et al. 2017

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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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