CFD modeling of pressure drop and drag coefficient in fixed beds: Wall effects

Reddy, Rupesh K.; Joshi, Jyeshtharaj B.

doi:10.1016/j.partic.2009.04.010

Cited by 60 publications

(26 citation statements)

References 14 publications

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“…The ratio d t /d p affects the properties near the wall region because the porosity is large here [3][4][5][6], and the changes of porosity and velocity may cover the core area of the bed, where the heat transfer mainly occurs [7,8]. The flow maldistribution is obvious with low tube-to-particle ratio (d t /d p < 15) and will seriously affect the heat transfer or reaction in the bed [9], which determines the design of the bed. When the bed is packed with uniform size particles, there exists a large void fraction near the wall region and it can be called wall effects [10].…”

Section: Introductionmentioning

confidence: 99%

Numerical Study on Heat Transfer Performance in Packed Bed

Wang

Liu

et al. 2019

Energies

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Packed beds are widely used in industries and it is of great significance to enhance the heat transfer between gas and solid states inside the bed. In this paper, numerical simulation method is adopted to investigate the heat transfer principle in the bed at particle scale, and to develop the direct enhanced heat transfer methods in packed beds. The gas is treated as continuous phase and solved by Computational Fluid Dynamics (CFD), while the particles are treated as discrete phase and solved by the Discrete Element Method (DEM); taking entransy dissipation to evaluate the heat transfer process. Considering the overall performance and entransy dissipation, the results show that, compared with the uniform particle size distribution, radial distribution of multiparticle size can effectively improve the heat transfer performance because it optimizes the velocity and temperature field, reduces the equivalent thermal resistance of convection heat transfer process, and the temperature of outlet gas increases significantly, which indicates the heat quality of the gas has been greatly improved. The increase in distribution thickness obviously enhances heat transfer performance without reducing the equivalent thermal resistance in the bed. The result is of great importance for guiding practical engineering applications.

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Section: Introductionmentioning

confidence: 99%

Numerical Study on Heat Transfer Performance in Packed Bed

Wang

Liu

et al. 2019

Energies

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“…Despite the advances in understanding TBRs through CFD techniques, there are still challenges to surpass due to the great complexity of these systems and their highly coupled, multiphysics, multiscale, and multiphasic nature . In this context, several efforts to improve the understanding of these systems have been developed from which two main kinds of contribution can be distinguished: 1) studies in which the hydrodynamic behaviour of the column is assessed (usually in the absence of mass transfer and reaction phenomena or with important simplifications in the multiphysics nature that allow the incorporation of more explicit descriptions of the bed textural characteristics); and 2) works where the kinetic behaviour is studied, usually with important simplifications in the description of the void fraction of the bed (average equations) and that generally appeal to form factors to take into account the textural effects of the catalytic bed and effective transport coefficients. The simplicity of these works allows the incorporation of complex chemical reaction phenomena.…”

Section: Introductionmentioning

confidence: 99%

CFD analysis of bed textural characteristics on TBR behaviour: Kinetics, scaling‐up, multiscale analysis, and wall effects

Uribe¹,

Cordero²,

Zárate³

et al. 2018

Can J Chem Eng

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A simulation of a trickle bed reactor aided by computational fluid dynamics was implemented. With a Eulerian approach, geometrical characteristics were explicitly considered and two simultaneous heterogeneous reactions were included, hydrodesulphurization (HDS) and hydrodenitrogenation (HDN). This was performed in order to achieve the following: (1) attain further insight into a proper scaling‐up procedure to be able to obtain the same hydrodynamics and kinetics behaviour in two reactors of different length and diameter scales; (2) develop a multiscale analysis regarding the communication of information between scales through the construction of a porous microstructure model from which the geometrical information of the microscale is captured by the effective transport coefficients (which affect the overall reactor behaviour); (3) investigate the effect of operation condition variations on hydrodynamics and kinetics; and (4) assess the deviations and further differences observed from average to punctual conversion values and the assumptions from kinetic literature models through a preliminary multiscale analysis. The CFD results were validated against experimental pressure drop data as well as HDS and HDN conversion theoretical data. An excellent agreement was found. The model produces a significant improvement in hydrodynamic parameter prediction, achieving 5 times better accuracy in predicting pressure drops and 50 % improvement in holdup prediction. The fully coupled model predicts HDS conversion with 96 % accuracy and HDN conversion with 94 % accuracy. Results suggest that the best way to obtain similar kinetic and hydrodynamic behaviour in TBRs with different lengths and diameter length scales is by equaling the liquid holdup true(ϵγtrue) or the mass velocities (L‐G).

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“…Generally, two different categories of CFD models can be applied for modeling multiphase gas-solid flow, namely Eulerian-Eulerian and Eulerian-Lagrangian formulations (Behjat et al, 2011;Chen et al, 2011a;Reddy and Joshi, 2010;Zimmermann and Taghipour, 2005 A c c e p t e d M a n u s c r i p t 5 provides a more reliable and detailed representation of the fluidized bed since it tracks each particle-particle collision. This model performs more efficiently when the number of particles are small (typically <1× 10 6 ), and dispersed volume fraction does not exceed 10 % of the mixture in any region (i.e., in academic researches on a laboratory scale), due to high computational cost.…”

Section: Introductionmentioning

confidence: 99%