Effect of Mean Network Coordination Number on Dispersivity Characteristics

Vasilyev, Leonid; Raoof, Amir; Nordbotten, Jan Martin

doi:10.1007/s11242-012-0054-5

Cited by 37 publications

(32 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Øren and coworkers [Øren et al, 1997;Øren et al, 1998;Bakke, 2002, 2003] developed pore-network models of real sandstones and reported mean coordination numbers of 3:5 À 4:5. In the present study, we employ an MDPN model in which pore throats can be oriented in 13 different directions, allowing a maximum coordination number of 26 [Nogues et al, 2012;Vasilyev et al, 2012;Raoof and Hassanizadeh, 2009]. To get a desired coordination number distribution, we follow an elimination procedure to rule out some of the connections (for details, see Raoof and Hassanizadeh [2009]).…”

Section: Coordination Number Distribution In Mdpnmentioning

confidence: 99%

Saturation‐dependent solute dispersivity in porous media: Pore‐scale processes

Raoof

Hassanizadeh

2013

Water Resources Research

View full text Add to dashboard Cite

[1] It is known that in variably saturated porous media, dispersion coefficient depends on Darcy velocity and water saturation. In one-dimensional flow, it is commonly assumed that the dispersion coefficient is a linear function of velocity. The coefficient of proportionality, called the dispersivity, is considered to depend on saturation. However, there is not much known about its dependence on saturation. In this study, we investigate, using a pore network model, how the longitudinal dispersivity varies nonlinearly with saturation. We schematize the porous medium as a network of pore bodies and pore throats with finite volumes. The pore space is modeled using the multidirectional pore-network concept, which allows for a distribution of pore coordination numbers. This topological property together with the distribution of pore sizes are used to mimic the microstructure of real porous media. The dispersivity is calculated by solving the mass balance equations for solute concentration in all network elements and averaging the concentrations over a large number of pores. We have introduced a new formulation of solute transport within pore space, where we account for different compartments of residual water within drained pores. This formulation makes it possible to capture the effect of limited mixing due to partial filling of the pores under variably saturated conditions. We found that dispersivity increases with the decrease in saturation, it reaches a maximum value, and then decreases with further decrease in saturation. To show the capability of our formulation to properly capture the effect of saturation on solute dispersion, we applied it to model the results of a reported experimental study.Citation: Raoof, A., and S. M. Hassanizadeh (2013), Saturation-dependent solute dispersivity in porous media: Pore-scale processes,

show abstract

Section: Coordination Number Distribution In Mdpnmentioning

confidence: 99%

Saturation‐dependent solute dispersivity in porous media: Pore‐scale processes

Raoof

Hassanizadeh

2013

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…In addition, for incompressible, steady-state flow, the sum of discharges of pore throats connected to a pore body must be zero (Vasilyev et al, 2012):…”

Section: Flow Simulationmentioning

confidence: 99%

Pore-scale modeling of reactive transport in wellbore cement under CO2 storage conditions

Raoof

Nick

Wolterbeek

et al. 2012

International Journal of Greenhouse Gas Control

103

View full text Add to dashboard Cite

“…Therefore, each point that is not on a border of the lattice will be connected to 26 other points [28]. The number of connections of each point in the porous space to other neighbouring points belonging to the porous space is called the void coordination number, C n [24].…”

Section: Topology Of the Void Spacementioning

confidence: 99%

“…The number of connections of each point in the porous space to other neighbouring points belonging to the porous space is called the void coordination number, C n [24]. As reported in [28], even if the maximum void coordination number is 26, real porous media have a much lower value of C n . For example, in [24], average values of C n between 2.90 and 10.64 are reported and successfully compared to real values from the literature.…”

Section: Topology Of the Void Spacementioning

confidence: 99%

Stochastic generation of virtual air pores in granular materials

2015

View full text Add to dashboard Cite

A computational method is described for the generation of virtual air pores with randomized features in granular materials. The method is based on the creation of a stack of two dimensional stochastically generated domains of packed virtual aggregate particles that are converted to three dimensions and made to intersected with one another. The three dimensional structure that is created is then sampled with an algorithm that detects the void space left between the intersected particles, which corresponds to the air void volume in real materials. This allows the generation of a map of the previously generated three dimensional model that can be used to analyse the topology of the void channels. The isotropy of the samples is here discussed and analysed. The air void size distribution in all the virtual samples generated in this study is described with the Weibull distribution and the goodness of fit is successfully evaluated with the Kolmogorov-Smirnov test. The specific surface of the virtual samples is also successfully compared to that of real samples. The results show that a stochastic approach to the generation of virtual granular materials based only on geometric principles is feasible and provides realistic results.

show abstract

Effect of Mean Network Coordination Number on Dispersivity Characteristics

Cited by 37 publications

References 33 publications

Saturation‐dependent solute dispersivity in porous media: Pore‐scale processes

Saturation‐dependent solute dispersivity in porous media: Pore‐scale processes

Pore-scale modeling of reactive transport in wellbore cement under CO2 storage conditions

Stochastic generation of virtual air pores in granular materials

Contact Info

Product

Resources

About