Pore-scale modeling of nanoparticle transport and retention in real porous materials

Sanematsu, Paula C.; Thompson, Karsten E.; Willson, Clinton S.

doi:10.1016/j.cageo.2018.10.010

Cited by 10 publications

(3 citation statements)

References 43 publications

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“…The transport and retention of colloids in a porous medium is commonly simulated using continuum‐scale models that neglect the full complexity of the pore structure by employing average values of pore‐scale transport and surface deposition parameters (including η , α , and S f ) that are assumed to be constants for the whole porous medium. Previous work has emphasized the influence of pore structure on colloid transport and retention processes (Long & Hilpert, 2009; Molnar et al., 2016; Sanematsu et al., 2019). Direct determination of average pore‐scale colloid transport and retention parameters in a porous medium can be very computationally expensive due to the complicated boundary conditions for flow and transport and variability in factors that influence retention.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Colloid Transport and Retention Using a Pore‐Network Model With Roughness and Chemical Heterogeneity on Pore Surfaces

Lin

Bradford

et al. 2021

Water Resources Research

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Colloid transport and retention in porous media is a common phenomenon in both nature and industry. However, many questions remain on how to obtain colloid transport and retention parameters. Previous work usually assumed constant transport parameters in a medium under a given physicochemical condition. In this study, pore‐network modeling is employed to upscale colloid transport and retention from the pore‐scale to the macro‐scale. The pore‐scale transport parameters including the collection efficiency (η), the sticking efficiency (α), and the fraction of the solid‐water interface that contributes to the colloid attachment (Sf) are obtained using numerical simulation and probability analysis for each pore throat. The influence of roughness and charge heterogeneity on the distribution of pore‐scale parameters is discussed. Breakthrough curves and the retention profiles under different roughness and charge heterogeneity conditions are also analyzed. Results show that pore‐scale parameters η, α, and Sf have various distributions in porous media that may not be accurately described using single‐valued effective parameters. The value of η decreases with velocity and exhibits a wide distribution under low‐velocity conditions. The parameter α tends to decrease with the colloid size and the pore water velocity and increased with the charge heterogeneity fraction. Nanoscale roughness alters α in a non‐monotonic fashion but tends to increase for lower roughness fractions and zeta potential. Microscopic roughness increases values of α for colloids that would otherwise be susceptible to hydrodynamic removal. Breakthrough curves and retention profiles show that more retention occurs for smaller particles, which reflects the influence of blocking.

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Section: Introductionmentioning

confidence: 99%

Simulation of Colloid Transport and Retention Using a Pore‐Network Model With Roughness and Chemical Heterogeneity on Pore Surfaces

Lin

Bradford

et al. 2021

Water Resources Research

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“…The paper showed that clogging is responsible for permeability reduction when particle size and adhesion forces are increased, but not when surface roughness is increased. Sanematsu, Thompson & Willson (2019) also studied the effects of varying attractive surface force strengths on particle deposition in a realistic material, using a three-dimensional model generated from X-ray microtomography images of Berea sandstone samples. Realistic pore geometries allowed researchers to model particle transport processes directly on structures taken from real materials, though this approach requires intricate image analysis techniques and discretization algorithms for simulation.…”

Section: Introductionmentioning

confidence: 99%

Pore-scale modelling of particle transport in a porous bed

Storm,

Marshall

2024

J. Fluid Mech.

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A computational study was performed of the transport of both non-adhesive and adhesive particles in a porous bed with a body-centred cubic (BCC) structure. Pore-scale simulation of the flow within the porous bed was achieved through combining the immersed boundary method and the lattice Boltzmann method. Particle transport is computed using an adhesive discrete-element method based on a multi-time-scale soft-sphere model. The fluid flow results are validated by comparison with experimental data for dimensionless permeability of flow in a porous bed of spheres. For computations with non-adhesive particles, the particles are observed to drift to the centre of ‘channels’ in the BCC array, within which most of the fluid flow occurs. The mechanism of this inward drift was found to be related to the phenomenon of oscillatory clustering, which is an inertial drift mechanism observed for particles in a corrugated channel. A measure for particle drift into these channels was developed, and the time rate of change of this measure was found to compare closely with an approximate theoretical prediction based on oscillatory clustering theory. The drift measure was observed to be limited at long time by hold-up of outlier particles caught in long-duration collisions with the fixed bed particles in regions of low fluid velocity magnitude. Simulations with adhesive particles exhibited marked increase in collision duration, as well as inhibition of the tendency to drift toward the flow channels due to adhesive hold-up.

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“…Accurate modeling of core 3D pore structure is of fundamental significance to the study of physical and transport properties [7,8,9,10,11]. The digital core [12,13,14,15,16,17,18,19] is a popular technique for modeling 3D core pore structure, which can be obtained by imaging equipment, such as X-ray Computed Tomography (micro-CT). Generally, imaging equipment captures only a single length-scale core feature in a single imaging session.…”

Section: Introductionmentioning

confidence: 99%

Multiscale reconstruction of porous media based on multiple dictionaries learning

Yan¹,

Teng²,

He³

et al. 2022

Preprint

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Digital modeling of the microstructure is important for studying the physical and transport properties of porous media.Multiscale modeling for porous media can accurately characterize macro-pores and micro-pores in a large-FoV (field of view) high-resolution three-dimensional pore structure model. This paper proposes a multiscale reconstruction algorithm based on multiple dictionaries learning, in which edge patterns and micro-pore patterns from homology high-resolution pore structure are introduced into low-resolution pore structure to build a fine multiscale pore structure model. The qualitative and quantitative comparisons of the experimental results show that the results of multiscale reconstruction are similar to the real high-resolution pore structure in terms of complex pore geometry and pore surface morphology. The geometric, topological and permeability properties of multiscale reconstruction results are almost identical to those of the real high-resolution pore structures. The experiments also demonstrate the proposal algorithm is capable of multiscale reconstruction without regard to the size of the input. This work provides an effective method for fine multiscale modeling of porous media.

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Pore-scale modeling of nanoparticle transport and retention in real porous materials

Cited by 10 publications

References 43 publications

Simulation of Colloid Transport and Retention Using a Pore‐Network Model With Roughness and Chemical Heterogeneity on Pore Surfaces

Simulation of Colloid Transport and Retention Using a Pore‐Network Model With Roughness and Chemical Heterogeneity on Pore Surfaces

Pore-scale modelling of particle transport in a porous bed

Multiscale reconstruction of porous media based on multiple dictionaries learning

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