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Quantitative characterization of the pore network of a macroporous soil using µ X-ray CT

Quantitative characterization of the pore network of a macroporous soil using µ X-ray CT. Sofie Herman, department of Land Management, K.U. Leuven Sofie.Herman@agr.kuleuven.ac.be. Introduction. Geometry of pore space: understand water flow Richards’ eq and effective hydraulic properties

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Quantitative characterization of the pore network of a macroporous soil using µ X-ray CT

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  1. Quantitative characterization of the pore network of a macroporous soil using µ X-ray CT Sofie Herman, department of Land Management, K.U. Leuven Sofie.Herman@agr.kuleuven.ac.be

  2. Introduction • Geometry of pore space: understand water flow Richards’ eq and effective hydraulic properties • Macropores (cracks, root channels,…) Preferential flow Pore network models • Need to quantify soil structure and pore network of a macroporous soil

  3. General research outline Field and laboratory methods: e.g. multistep outflow method, tensio-infiltrometer measurements µCT and image analysis sandy loam macroporous soil Hydraulic characterization Characterization of porous structure and derivation of macropore network K(), h() Comparison between measured and simulated variables Simulation of flow (and transport) in a pore scale model K(), h() Interaction between different flow domains

  4. Microfocus X-ray CT • Sample: 5 cm diameter, 5 cm height • Scan parameters: • 135 kV and 0.1 mA • Cu-filter (0.82 mm) to reduce beam-hardening • Resolution: • 0.1 mm

  5. Determination and characterization of the pore network • Macropores-matrix separation by binarization • Macropore volume: 10 % • Pore size distribution and connectivity function by means of mathematical morphology

  6. Pore size distribution • Opening of the image with spheres of increasing diameter • Opening: erosion followed by dilation Original image Erosion of the original image Dilation of the eroded image: Smaller parts removed Struct. Elem.

  7. D>0.11mm D>1.02 mm Pore size distribution • Result: cumulative PSD, pore size classes depend on pixel size D>1.92 mm D>2.83 mm D>3.5 mm

  8. Connectivity: Euler-Poincaré-characteristic: N: number of isolated components C: total number of redundant connections H: number of holes as a function of the pore size class Connectivity function

  9. Determination of soil hydraulic properties • Generation of a pore network with the same pore size distribution and connectivity function by the Topnet model (Vogel, 1998) • Drainage is simulated (initial state: saturation) by applying pressure steps that correspond to a given pore size (Young-Laplace) within the model. • Water retention and hydraulic conductivity curves are estimated under drainage

  10. Pore network generated by the Topnet model based on the PSD and connectivity data Face-centered cubic grid Cylindrical pores with fixed radius r Pores drained at P=-2cm

  11. - = µwater Distribution of water content Moisture content calculated=0.27 cm3cm-3 <-> measured=0.32 cm3cm-3 µwet µdry low high

  12. Swelling/shrinking • Variable aperture of macropores depending on the degree of saturation dry wet FWHMdry=0.48mm FWHMwet=0.33mm

  13. Conclusions • The macropore network was characterized quantitatively in terms of the pore size distribution and connectivity by µCT • Effective hydraulic properties were estimated from a static pore network model • µCT offers the potential to visualize dynamic phenomena that occur during wetting/drying cycles such as shrinking and swelling of pores

  14. Future objectives • Describe and measure swelling of pores as a function of moisture content • Simulate drainage/imbibition of soil by a dynamic model • Incorporate swelling into the model

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