Impact of mesh quality on the numerical estimation of saturated water conductivity of pore media
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Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
Departament of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
Department of Civil Engineering, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan
Final revision date: 2020-11-04
Acceptance date: 2020-11-10
Publication date: 2020-12-08
Corresponding author
Bartłomiej Gackiewicz   

Department of Metrology and Modelling of Agrophysical Processes, Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290, Lublin, Poland
Int. Agrophys. 2020, 34(4): 473-483
The numerical modelling of transport phenomena in porous media often requires a compromise between grid precision and the accuracy of simulation results. This study demonstrates the impact of errors on the accuracy of the reproduction of the actual pore space by the numerical grid on the estimated values of the saturated water conductivity. Four types of computational grids with varying levels of complexity were prepared for each of the 12 tomographic images of the porous specimens. The specific surfaces and total porosities were calculated for each of the meshes and compared with those parameters calculated for binarized tomographic images. Simulations of steady flow were performed on the computational grids, and the saturated water conductivity values were calculated. It has been shown that an insufficiently accurate mesh only reproduces the largest pore spaces in the analysed sample, which most often leads to an underestimation of the water conductivity coefficient. The following criterion for the optimal accuracy of the computational grid is proposed, it is based on the voxel size of the tomographic images of the porous media: the minimum size of the cell in the mesh used for simulations has to be at most two times the size of the voxel used in the tomographic scans of the porous medium.
Andrä H., Combaret N., Dvorkin J., Glatt E., Han J., Kabel M., Keehm Y., Krzikalla F., Lee M., Madonna C., Marsh M., Mukerji T., Saenger E.H., Sain R., Saxena N., Ricker S., Wiegmann A., and Zhan X., 2013. Digital rock physics benchmarks-part II: Computing effective properties. Comput. Geosci., 50, 33-43.
Baveye P.C., Laba M., Otten W., Bouckaert L., Dello Sterpaio P., Goswami R.R., Grinev D., Houston A., Hu Y., Liu J., Mooney S., Pajor R., Sleutel S., Tarquis A., Wang W., Wei Q., and Sezgin M., 2010. Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray microtomography data. Geoderma, 157, 51-63.
Baveye P.C., Pot V., and Garnier P., 2017. Accounting for sub-resolution pores in models of water and solute transport in soils based on computed tomography images: Are we there yet? J. Hydrol., 555, 253-256.
Bazaikin Y., Gurevich B., Iglauer S., Khachkova T., Kolyukhin D., Lebedev M., Lisitsa V., Reshetova G., 2017. Effect of CT image size and resolution on the accuracy of rock property estimates. J. Geophys. Res. Solid Earth, 122, 3635-3647.
Bieganowski A., Chojecki T., Ryżak M., Sochan A., and Lamorski K., 2013. Methodological Aspects of Fractal Dimension Estimation on the Basis of Particle Size Distribution. Vadose Zo. J., 12, 0.
Borujeni A.T., Lane N.M., Thompson K., and Tyagi M., 2013. Effects of image resolution and numerical resolution on computed permeability of consolidated packing using LB and FEM pore-scale simulations. Comput. Fluids, 88, 753-763.
Chen S. and Doolen G.D., 1998. Lattice Boltzmann method for fluid flows. Ann. Rev. Fluid Mech., 30, 329-364.
Chen S., Yang B., and Zheng C., 2017. A lattice Boltzmann model for heat transfer in porous media. Int. J. Heat Mass Transf., 111, 1019-1022.
Dal Ferro N., Strozzi A.G., Duwig C., Delmas P., Charrier P., and Morari F., 2015. Application of smoothed particle hydrodynamics (SPH) and pore morphologic model to predict saturated water conductivity from X-ray CT imaging in a silty loam Cambisol. Geoderma, 255-256, 27-34.
Gackiewicz B., Lamorski K., and Sławiński C., 2019. Saturated water conductivity estimation based on X-ray CT images – evaluation of the impact of thresholding errors. Int. Agrophys., 33, 49-60.
Guan K.M., Nazarova M., Guo B., Tchelepi H., Kovscek A.R., and Creux P., 2019. Effects of image resolution on sandstone porosity and permeability as obtained from X-Ray microscopy. Transp. Porous Media, 127, 233-245.
Guibert R., Nazarova M., Horgue P., Hamon G., Creux P., and Debenest G., 2015. Computational permeability determination from pore-scale imaging: Sample Size, Mesh and Method Sensitivities. Transp. Porous Media, 107, 641-656.
Hapca S.M., Houston A.N., Otten W., and Baveye P.C., 2013. New local thresholding method for soil images by minimizing grayscale intra-class variance. Vadose Zo. J., 12, 1-13.
Horgue P., Soulaine C., Franc J., Guibert R., and Debenest G., 2015. An open-source toolbox for multiphase flow in porous media. Comput. Phys. Commun., 187, 217-226.
Houston A.N., Schmidt S., Tarquis A.M., Otten W., Baveye P.C., and Hapca S.M., 2013. Effect of scanning and image reconstruction settings in X-ray computed microtomography on quality and segmentation of 3D soil images. Geoderma, 207-208, 154-165.
Iassonov P., Gebrenegus T., and Tuller M., 2009. Segmentation of X-ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures. Water Resour. Res., 45.
Jarvis N.J., 2007. A review of non-equilibrium water flow and solute transport in soil macropores: principles, controlling factors and consequences for water quality. Eur. J. Soil Sci., 58, 523-546.
Khan F., Enzmann F., Kersten M., Wiegmann A., and Steiner K., 2012. 3D simulation of the permeability tensor in a soil aggregate on basis of nanotomographic imaging and LBE solver. J. Soils Sediments, 12, 86-96.
Kuang X., Sansalone J., Ying G., and Ranieri V., 2011. Pore-structure models of hydraulic conductivity for permeable pavement. J. Hydrol., 399, 148-157.
Larsbo M., Koestel J., and Jarvis N., 2014. Relations between macropore network characteristics and the degree of preferential solute transport. Hydrol. Earth Syst. Sci., 18, 5255-5269.
Lesueur M., Casadiego M.C., Veveakis M., and Poulet T., 2017. Modelling fluid-microstructure interaction on elasto-visco-plastic digital rocks. Geomech. Energy Environ., 12, 1-13.
Leu L., Berg S., Enzmann F., Armstrong R.T., and Kersten M., 2014. Fast X-ray Micro-Tomography of Multiphase Flow in Berea Sandstone: A Sensitivity Study on Image Processing. Transp. Porous Media, 105, 451-469.
Mostaghimi P., Blunt M.J., and Bijeljic B., 2013. Computations of absolute permeability on micro-CT images. Math. Geosci., 45, 103-125.
Patankar S. V., Spalding D.B., and Road E., 1972. A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows. Int. J. Heat Mass Transf., 15, 1787-1806.
Pereira Nunes J.P., Bijeljic B., and Blunt M.J., 2015. Time-of-flight distributions and breakthrough curves in hetero-geneous porous media using a pore-scale streamline tracing algorithm. Transp. Porous Media 109, 317-336.
Pot V., Zhong X., and Baveye P.C., 2020. Effect of resolution, reconstruction settings, and segmentation methods on the numerical calculation of saturated soil hydraulic conductivity from 3D computed tomography images. Geoderma, 362, 114089.
Ramandi H.L., Mostaghimi P., and Armstrong R.T., 2017. Digital rock analysis for accurate prediction of fractured media permeability. J. Hydrol., 554, 817-826.
Ridler T.W. and Calvard S., 1978. Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cybern., 8, 630-632.
Schläuter S., Sheppard A., Brown K., and Wildenschild D., 2014. Image processing of multiphase images obtained via X-ray microtomography: A review. Water Resour. Res., 50, 3615-3639.
Shah S.M., Gray F., Crawshaw J.P., and Boek E.S., 2016. Micro-computed tomography pore-scale study of flow in porous media: Effect of voxel resolution. Adv. Water Resour., 95, 276-287.
Starnoni M., Pokrajac D., and Neilson J.E., 2017. Computation of fluid flow and pore-space properties estimation on micro-CT images of rock samples. Comput. Geosci., 106, 118-129.
Taylor H.F., O’Sullivan C., Sim W.W., and Carr S.J., 2017. Sub-particle-scale investigation of seepage in sands. Soils Found., 57, 439-452.
Wang W., Kravchenko A.N., Smucker A.J.M., and Rivers M.L., 2011. Comparison of image segmentation methods in simulated 2D and 3D microtomographic images of soil aggregates. Geoderma, 162, 231-241.
Zuo L., Ajo-Franklin J.B., Voltolini M., Geller J.T., and Benson S.M., 2017. Pore-scale multiphase flow modeling and imaging of CO2 exsolution in Sandstone. J. Pet. Sci. Eng. 155, 63-77.
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