PIV measurements and CFD simulations of the particle-scale flow distribution in a packed bed

Thaker, Abhijeet H.; Karthik, G.M.; Buwa, Vivek V.

doi:10.1016/j.cej.2019.05.053

Cited by 45 publications

(14 citation statements)

References 31 publications

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“…For large-scale separation towers, the best selection is structured packing [16][17][18]. Such packings should have high separation efficiency, small amplification effect, low pressure drop, etc.…”

Section: Introductionmentioning

confidence: 99%

“…According to their findings, the k-w model is the optimum to analyze the hydrodynamics, heat and mass transfer. Using the well-known k-w model, Thaker et al [17] investigated the flow in packed beds by means of CFD modeling. The results of the numerical studies agreed well with the experimental findings obtained via particle image velocimetry measurements.…”

Section: Introductionmentioning

confidence: 99%

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Computational Fluid Dynamics Characterization of High‐Capacity Structured Packing

et al. 2020

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The novel wire gauze structured packing, PACK‐860, was characterized by means of numerical methods. The main features of PACK‐860 such as height equivalent to a theoretical plate (HETP) and dry/wet pressure drop were evaluated. The flow structure in this packing was described by numerical simulation. To estimate the amount of HETP and dry/wet pressure drop, three‐dimensional computational fluid dynamics (3D CFD) modeling with the Eulerian‐Eulerian multiphase approach was applied. The average relative errors between the results obtained from CFD simulation and experimental findings for mass transfer efficiency and wet and dry pressure drop were assessed. Numerical observations were found to agree well with the empirical results, proving the reliability of CFD simulations for modeling separation processes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational Fluid Dynamics Characterization of High‐Capacity Structured Packing

et al. 2020

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“…2020 53 ). These are listed as (i) particle diameter and superficial velocity 11,14,[54][55][56][57][58][59][60][61][62][63] , (ii) pore hydraulic diameter and interstitial velocity 33,49,[64][65][66][67][68][69][70][71][72][73] , (iii) approximate permeability and average intrinsic velocity 74,75 , (iv) pore-averaged velocity and particle size 37,38,[40][41][42][76][77][78][79] , and (v) poreaveraged length scale, and pore-averaged velocity. For the latter, the length scale has been based on the square root of pore area (Sederman et al 1998 36 ), cross-stream length of velocity profile (Johns et al 2000 35 ), or along a line perpendicular to the flow direction (Suekane et al 2003 34 ).…”

Section: Introductionmentioning

confidence: 99%

Transition to turbulence in randomly packed porous media; scale estimation of vortical structures

Ziazi,

Liburdy

2020

Preprint

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Pore-scale observation of vortical flow structures in porous media is a significant challenge in many natural and industrial systems. Vortical structure dynamics is believed to be the driving mechanism in the transition regime in porous media based on the pore Reynolds number, Re p . To examine this assertion, a refractiveindex matched randomly packed porous medium with homogeneously-sized glass spheres (D B = 15mm) is designed to measure the scale of vortical flow structures in transition from unsteady laminar to turbulent using two-dimensional time-resolved particle image velocimetry. Planar Particle Image Velocimetry (PIV) data is used to quantify the scale of these structures with regard to size, strength, and number density using two different scalings (i) Re p macroscopic (global), and (ii) Re p microscopic (local, pore-scale). Data is obtained for Re p from 100 to 948 in six different locations from the center of the bed towards the wall (one bead diameter away from wall). Direct measurement of vortex scale is quantified by employing swirl strength (λ ci ), vortex core (Γ 2 ), and enhanced swirl strength (λ cr /λ ci ) vortex identification methods. These scales are compared with turbulent integral scales. Due to the confinement of the random medium, the inertia-dependent topology of the flow creates shear-dominant vortical structures in moderate unsteady laminar Reynolds numbers (Re p < 300), while the swirl-dominant flow structures appear in weak turbulent Reynolds numbers (Re p > 500). From the macroscopic point of view, (i) the size of vortical structures decreases asymptotically to reach 20% of the global hydraulic diameter, (ii) the strength of vortices increases monotonically by enhancing the inertial effects of Re p , and (iii) the average number density of vortical structures grows from unsteady laminar to fully turbulent. From the pore-scale (local) point of view using Re p , (i) the size of vortical structures decreases monotonically with increasing local Reynolds number, (ii) the strength of vortices rises with Re p , and (iii) the number density of vortical structures for different Re p is invariant relative to the pore size. These findings suggest pore versus macro-scale coupling exists for the scale of vortical flow structures in the transition regime, albeit the scale variation of pore-scale flow structures with local inertial effects is different from the asymptotic values captured in the macroscopic level with increasing Re p .

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“…Extracting information on packing structure and flow through a rock bed relies on expensive and time-consuming techniques such as X-ray tomography and particle image velocimetry [13]. Most researchers adopted discrete element modelling (DEM) and computational fluid dynamics (CFD) simulations for this purpose [14,15].…”

Section: -2mentioning

confidence: 99%

A CFD/DEM Approach to Determine the Flow Resistance of Randomly Packed Bed of Crushed Rock Particles

Hoffmann¹

2021

Proceedings of the 8th International Conference on Fluid Flow, Heat and Mass Transfer (FFHMT'21)

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Packed beds of crushed rock particles are typically encountered in the thermal energy storage system at a solar thermal power plant. Rocks have irregular shapes, and tend to pack with their long axes in a horizontal plane. As a results its resistance to airflow is anisotropic. Designing a bed for effective charging and discharging cycles, depends on an accurate prediction of the flow distribution through it. Ellipsoids are mathematically simple shapes that characterise crushed rock particles reasonably successfully. A packed bed was generated using a discrete element model, and the flow through the bed calculated with computational fluid dynamics. The resistance tensor for the bed was extracted from the data. The tensor is almost symmetrical, with the diagonal terms dominant. Incorporating this tensor as a source term in a porous medium presentation of a packed bed, should yield more accurate results than, say the Ergun equation, at very little extra computational overhead.

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PIV measurements and CFD simulations of the particle-scale flow distribution in a packed bed

Cited by 45 publications

References 31 publications

Computational Fluid Dynamics Characterization of High‐Capacity Structured Packing

Computational Fluid Dynamics Characterization of High‐Capacity Structured Packing

Transition to turbulence in randomly packed porous media; scale estimation of vortical structures

A CFD/DEM Approach to Determine the Flow Resistance of Randomly Packed Bed of Crushed Rock Particles

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