Literature DB >> 33712708

An intercomparison of the pore network to the Navier-Stokes modeling approach applied for saturated conductivity estimation from X-ray CT images.

Bartłomiej Gackiewicz1, Krzysztof Lamorski2, Cezary Sławiński1, Shao-Yiu Hsu3, Liang-Cheng Chang4.   

Abstract

Different modeling techniques can be used to estimate the saturated conductivity of a porous medium based on computed tomography (CT) images. In this research, two methods are intercompared: direct modeling using the Navier-Stokes (NS) approach and simplified geometry pore network (PN) modeling. Both modeling approaches rely on pore media geometry which was determined using an X-ray CT scans with voxel size 2 μm. An estimate of the saturated conductivity using both methods was calculated for 20 samples prepared from sand with diverse particle size distributions. PN-estimated saturated conductivity was found to be statistically equivalent to the NS-determined saturated conductivity values. The average value of the ratio of the PN-determined conductivity to the NS-determined conductivity (KsatPN/NS) was equal to 0.927. In addition to the NS and PN modeling approaches, a simple Kozeny-Carman (KC) equation-based estimate was made. The comparison showed that the KC estimate overestimated saturated conductivity by more than double (2.624) the NS estimate. A relationship was observed between the porous media specific surface and the KsatPN/NS ratio. The tortuosity of analyzed samples was estimated, the correlation between the porous media tortuosity and the specific surface of the samples was observed. In case of NS modelling approach the difference between pore media total porosity and total porosity of meshes, which were lower, generated for simulations were observed. The average value of the differences between them was 0.01. The method of NS saturated conductivity error estimation related to pore media porosity underestimation by numerical meshes was proposed. The error was on the average 10% for analyzed samples. The minimum value of the error was 4.6% and maximum 19%.

Entities:  

Year:  2021        PMID: 33712708      PMCID: PMC7955099          DOI: 10.1038/s41598-021-85325-z

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  10 in total

1.  Hydraulic tortuosity in arbitrary porous media flow.

Authors:  Artur Duda; Zbigniew Koza; Maciej Matyka
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2011-09-30

2.  Pore-network extraction from micro-computerized-tomography images.

Authors:  Hu Dong; Martin J Blunt
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2009-09-14

3.  Prediction of relative permeability in simple porous media.

Authors: 
Journal:  Phys Rev A       Date:  1992-08-15       Impact factor: 3.140

4.  Evaluation of a new solid boundary implementation in the lattice Boltzmann method for porous media considering permeability and apparent slip.

Authors:  Hamed Moqtaderi; Vahid Esfahanian
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2011-06-13       Impact factor: 4.226

5.  Predictions of dynamic changes in reaction rates as a consequence of incomplete mixing using pore scale reactive transport modeling on images of porous media.

Authors:  Z Alhashmi; M J Blunt; B Bijeljic
Journal:  J Contam Hydrol       Date:  2015-06-11       Impact factor: 3.188

6.  Predictions of non-Fickian solute transport in different classes of porous media using direct simulation on pore-scale images.

Authors:  Branko Bijeljic; Ali Raeini; Peyman Mostaghimi; Martin J Blunt
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2013-01-10

Review 7.  Review of pore network modelling of porous media: Experimental characterisations, network constructions and applications to reactive transport.

Authors:  Qingrong Xiong; Todor G Baychev; Andrey P Jivkov
Journal:  J Contam Hydrol       Date:  2016-07-12       Impact factor: 3.188

8.  Effect of wettability on two-phase quasi-static displacement: Validation of two pore scale modeling approaches.

Authors:  Rahul Verma; Matteo Icardi; Maša Prodanović
Journal:  J Contam Hydrol       Date:  2018-01-06       Impact factor: 3.188

9.  Generalized network modeling: Network extraction as a coarse-scale discretization of the void space of porous media.

Authors:  Ali Q Raeini; Branko Bijeljic; Martin J Blunt
Journal:  Phys Rev E       Date:  2017-07-20       Impact factor: 2.529

10.  BoneJ: Free and extensible bone image analysis in ImageJ.

Authors:  Michael Doube; Michał M Kłosowski; Ignacio Arganda-Carreras; Fabrice P Cordelières; Robert P Dougherty; Jonathan S Jackson; Benjamin Schmid; John R Hutchinson; Sandra J Shefelbine
Journal:  Bone       Date:  2010-09-15       Impact factor: 4.398

  10 in total

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