Simulation of flow through bead packs using the lattice Boltzmann method

Maier, Robert S.; Kroll, D. M.; Kutsovsky, Y. E.; Davis, H. T.; Bernard, Robert S.

doi:10.1063/1.869550

Cited by 216 publications

(175 citation statements)

References 33 publications

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“…The local counter diffusive flux, as well as the Darcy counter flow (Kutsovsky et al 1996;Maier et al 1998;Ogawa et al 2001;Li et al 2010), was observed in the present study (Figs. 9c, 11c).…”

Section: Negative Tail and Long Positive Tailsupporting

confidence: 54%

“…(i) The local counter flow was already found for the Darcy flow (not diffusion) in porous media (Kutsovsky et al 1996;Maier et al 1998;Ogawa et al 2001;Li et al 2010). The local flux obeys the log-normal distribution for the random resistor network (de Arcangelis et al 1985) and for the Darcy flow (Cenedese and Viotti 1996;Lebon et al 1996).…”

Section: Introductionmentioning

confidence: 99%

“…For a system with a sufficiently large Péclet number, the local tracer flux vector mostly agrees with the local Darcy flux vector. Then the histogram of the local tracer flux depends on the Reynolds number (Maier et al 1998), the negligible local counter flux occurs (Kutsovsky et al 1996;Ogawa et al 2001;Li et al 2010), and the Jz component obeys the log-normal distribution (Cenedese and Viotti 1996;Lebon et al 1996). It should be also noted that the 3D migration trajectory of tracers dissolved in the flowing fluid would switch from the less tortuous path to more tortuous one having larger local pore diameters (Zhang and Knackstedt 1995).…”

Section: Concept Ofmentioning

confidence: 99%

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Steady-State Local Diffusive Fluxes in Porous Geo-Materials Obtained by Pore-Scale Simulations

Nakashima

Nakano

2012

Transp Porous Med

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Computer simulations of non-sorbing tracers diffusing in fluid-saturated porous sediment/rock were performed using pore-scale X-ray microtomographic images to reveal the following. (i) The histogram of the magnitude of the local diffusive flux vector obeys a unimodal log-normal distribution having a long positive tail. Simulations using model images were also performed to show that the flux broadening in large pores and the flux mixing at the pore network junctions are responsible for the log-normal shape. (ii) The simulation enabled us to directly visualize pore voxels with large and small fluxes, confirming the existence of transport pores and stagnant pores. Because of the unimodal nature, however, it was difficult to distinguish transport pores from stagnant pores using an objective threshold in the histogram. (iii) Another histogram of the flux vector component along the direction of the macroscopic concentration gradient was analyzed. A negative tail was found in the histogram, indicating that local counter diffusion exists in the porous geo-materials. However, the population and intensity of the counter diffusion fluxes are too small and weak to contribute to the overall diffusive transport across the porous media system. A long positive tail representing a large-flux diffusive pathway was also observed in the histogram. However, again, the population of the large-flux transport pores is small. As a result, the main conveyer of the tracer is the stagnant pores (not the transport pores), which have small positive flux values but a large population.

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Section: Negative Tail and Long Positive Tailsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Section: Concept Ofmentioning

confidence: 99%

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Steady-State Local Diffusive Fluxes in Porous Geo-Materials Obtained by Pore-Scale Simulations

Nakashima

Nakano

2012

Transp Porous Med

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“…Figure 7 also indicates self-similarity for the lowest range of Re p with a relatively flat plateau of the most probable u * x for u * x < 0.4 and then the probability of the velocity spectrum drops rather abruptly which was also observed by Maier et al (1998) and Moroni et al (2001). Johns et al (2000) who studied porous media with MRI obtained a peak for the most probable u * x at Re of about 1.…”

Section: Detailed Studies Of the Flowmentioning

confidence: 54%

“…7 also shows that there are negative u * x for all Re p . A number of studies have reported the presence of recirculation zones as well, i.e., Moroni et al (2001), Maier et al (1998), Saeger et al (1991Saeger et al ( , 1995, Lebon et al (1996), Hill et al (2001). The percentage negative u * x , which is an indication of recirculation zones, vary with Re p according to There is less recirculation within the plane 6 mm from the midplane than within the other planes probably because the overall porosity is about 20 % higher than for the other planes.…”

Section: Detailed Studies Of the Flowmentioning

confidence: 99%

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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Quantitative determination of molecular propagator distributions for solute transport in homogeneous and heterogeneous porous media using lattice Boltzmann simulations

2013

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[1] We simulate flow and solute transport directly on pore space images of rock cores with increasing degree of heterogeneity : a bead pack, Bentheimer sandstone, and Portland carbonate. A novel scheme is proposed to predict probability distributions for molecular displacements using the lattice Boltzmann (LB) method to calculate both the flow field and solute dispersion. We observe that for homogeneous rock samples, such as bead packs, the displacement distribution remains Gaussian with time increasing. In the more heterogeneous rocks, the displacement distribution separates into a moving and a stagnant part. In this case, we observe that the magnitude of the moving distribution decreases whereas a stagnant peak at the initial position (f50) increases with increasing heterogeneity of the porous medium. We compare our results directly with NMR experimental data for molecular displacements and find excellent agreement. To quantify the degree of heterogeneity, we calculate the fraction of solute particles trapped by integrating over the stagnant peak. We observe that the fraction of trapped solute increases from the bead pack (0 %) to Bentheimer sandstone (1.488%) to Portland carbonate (8.131%). Our analysis shows that the LB-based propagator simulation is able to predict the transport in porous media efficiently and accurately.Citation: Yang, J., J. Crawshaw, and E. S. Boek (2013), Quantitative determination of molecular propagator distributions for solute transport in homogeneous and heterogeneous porous media using lattice Boltzmann simulations, Water Resour. Res., 49,[8531][8532][8533][8534][8535][8536][8537][8538]

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Simulation of flow through bead packs using the lattice Boltzmann method

Cited by 216 publications

References 33 publications

Steady-State Local Diffusive Fluxes in Porous Geo-Materials Obtained by Pore-Scale Simulations

Steady-State Local Diffusive Fluxes in Porous Geo-Materials Obtained by Pore-Scale Simulations

Measurements of Transitional and Turbulent Flow in a Randomly Packed Bed of Spheres with Particle Image Velocimetry

Quantitative determination of molecular propagator distributions for solute transport in homogeneous and heterogeneous porous media using lattice Boltzmann simulations

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