A resistance model for flow through porous media

Wu, Jinsui; Yu, Bo; Yun, Meijuan

doi:10.1007/s11242-007-9129-0

Cited by 109 publications

(40 citation statements)

References 19 publications

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“…When a fluid flows through a porous bed, it moves through connected pores between particles. The ratio of the actual length of flow path to the physical depth of a porous bed is defined as the tortuosity as follows (Lu et al 2009;Wu et al 2008):…”

Section: Introductionmentioning

confidence: 99%

Predicting Tortuosity for Airflow Through Porous Beds Consisting of Randomly Packed Spherical Particles

2012

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This article presents a numerical method for determining tortuosity in porous beds consisting of randomly packed spherical particles. The calculation of tortuosity is carried out in two steps. In the first step, the spacial arrangement of particles in the porous bed is determined by using the discrete element method (DEM). Specifically, a commercially available discrete element package (PFC 3D ) was used to simulate the spacial structure of the porous bed. In the second step, a numerical algorithm was developed to construct the microscopic (pore scale) flow paths within the simulated spacial structure of the porous bed to calculate the lowest geometric tortuosity (LGT), which was defined as the ratio of the shortest flow path to the total bed depth. The numerical algorithm treats a porous bed as a series of four-particle tetrahedron units. When air enters a tetrahedron unit through one face (the base triangle), it is assumed to leave from another face triangle whose centroid is the highest of the four face triangles associated with the tetrahedron, and this face triangle will then be used as the base triangle for the next tetrahedron. This process is repeated to establish a series of tetrahedrons from the bottom to the top surface of the porous bed. The shortest flow path is then constructed geometrically by connecting the centroids of base triangles of consecutive tetrahedrons. The tortuosity values calculated by the proposed numerical method compared favourably with the values obtained from a CT image published in the literature for a bed of grain (peas). The proposed model predicted a tortuosity of 1.15, while the tortuosity estimated from the CT image was 1.14.

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Section: Introductionmentioning

confidence: 99%

Predicting Tortuosity for Airflow Through Porous Beds Consisting of Randomly Packed Spherical Particles

2012

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“…For example, Du Plessis (1994) and Hayes et al (1995) analytically derived the relationship between the pressure drop and the fluid velocity from the volume-averaged momentum equation. Wu et al (2008) proposed a new model for the pressure drop through granular porous media using the average hydraulic radius model and the contracting-expanding channel model. Their models are in good agreement with some experimental data for packed beds.…”

Section: Introductionmentioning

confidence: 99%

A Semi-empirical Correlation for Pressure Drop in Packed Beds of Spherical Particles

Choi

Kim

2008

Transp Porous Med

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The effect of a confining wall on the pressure drop of fluid flow through packed beds of spherical particles with small bed-to-particle diameter ratios was investigated to develop an improved pressure drop correlation. The dependency of pressure loss on both wall friction and increased porosity near the wall was accounted for by using a theoretical approach. A semi-empirical model was created based upon the capillary-orifice model, which included a wall correction factor for the inertial pressure loss. In this model, packed beds were treated as a bundle of capillary tubes whose orifice diameter in the core region was different from that of the wall region. Using this model, a new pressure drop correlation was obtained, based on the Ergun equation and applicable for a wide range of Reynolds numbers (10 −2 −10 3 ). The proposed correlation was compared with previous correlations, as well as with experimental data. This correlation showed close agreement with the experimental data for both low-and high-Reynolds number regimes and for a wide range of bed-to-particle diameter ratios. The ratio of the pressure drop in finite packing to that in homogeneous packing was then calculated. This ratio clearly shows how the wall effect depends on the Reynolds number and the bed-to-particle diameter ratio.Keywords Packed beds · Porous media · Pressure drop · Wall effect · Particle Notation C d Discharge coefficient of an orifice C D Drag coefficient of an orifice plate C w

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“…It can be clearly seen that effective diffusion coefficients and porosity increase as the removed number or recursion number increases. As expected, the increase of either removed number or recursion number will provide more diffusible channels for VOCs in the unsaturated building materials, this is surely benefit for the augment of effective diffusion coefficients [45].…”

Section: Effect Of Removed Number and Recursion Number In The Blockmentioning

confidence: 59%

Assessment of steady state diffusion of volatile organic compounds in unsaturated building materials based on fractal diffusion model

Tang

Chen

et al. 2015

Building and Environment

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A resistance model for flow through porous media

Cited by 109 publications

References 19 publications

Predicting Tortuosity for Airflow Through Porous Beds Consisting of Randomly Packed Spherical Particles

Predicting Tortuosity for Airflow Through Porous Beds Consisting of Randomly Packed Spherical Particles

A Semi-empirical Correlation for Pressure Drop in Packed Beds of Spherical Particles

Assessment of steady state diffusion of volatile organic compounds in unsaturated building materials based on fractal diffusion model

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