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  • 1
    Language: English
    In: Computers and Geosciences, 2009, Vol.35(9), pp.1789-1801
    Description: This study is the follow-up to a previous one devoted to soil pore space modelling. In the previous study, we proposed algorithms to represent soil pore space by means of optimal piecewise approximation using simple 3D geometrical primitives: balls, cylinders, cones, etc. In the present study, we use the ball-based piecewise approximation to simulate biological activity. The basic idea for modelling pore space consists in representing pore space using a minimal set of maximal balls (Delaunay spheres) recovering the shape skeleton. In this representation, each ball is considered as a maximal local cavity corresponding to the “intuitive” notion of a pore as described in the literature. The space segmentation induced by the network of balls (pores) is then used to spatialise biological dynamics. Organic matter and microbial decomposers are distributed within the balls (pores). A valuated graph representing the pore network, organic matter and microorganism distribution is then defined. Microbial soil organic matter decomposition is simulated by updating this valuated graph. The method has been implemented and tested on real data. As far as we know, this approach is the first one to formally link pore space geometry and biological dynamics. The long-term goal is to define geometrical typologies of pore space shape that can be attached to specific biological dynamic properties. This paper is a first attempt to achieve this goal.
    Keywords: 3d Computer Vision ; Biological Dynamics Simulation ; Computed Tomography ; Computational Geometry ; Microbial Decomposition ; Pore Space Modelling ; Geology
    ISSN: 0098-3004
    E-ISSN: 1873-7803
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  • 2
    Language: English
    In: Computers and Geosciences, February 2019, Vol.123, pp.20-37
    Description: In recent years, technological advances have stimulated researchers to try to unravel the extremely complex microscale processes that control the activity of microorganisms in soils. In particular, significant work has been carried out on the development of models able to accurately predict the microscale distribution of water, and the location of air–water interfaces in pores. A comparison, by Pot et al. (2015), of two different modeling approaches with actual synchrotron-based tomography data, shows that a two-phase lattice Boltzmann model (LBM) is able to predict remarkably well the location of air–water interfaces but is extremely slow, whereas a morphological model (MOSAIC), representing the pore space as a collection of spherical balls, provides a reasonable approximation of the observed air–water interfaces when each ball is allowed to drain independently, but does so blazingly fast. Interfaces predicted by MOSAIC, however, tend to have nonphysical shapes. In that general context, the key objective of the research described in the present article, based on the same tomography data as Pot et al. (2015), was to find out to what extent the use of ellipsoids instead of spherical balls in MOSAIC could not appreciably speed up computations, or at least, at equal computational time, provide a quantitatively better approximation of water-air interfaces. As far as we know, this is the first time an ellipsoids-based approximation of the soil pore space is proposed. A secondary objective was to assess whether ellipsoids might yield smoother, more physical, interfaces. Simulation results indicate that the use of ellipsoids provides a sizeable increase in accuracy in the prediction of air-water interfaces, an approximately 6-fold drop in computation time, and much more realistic-looking interfaces, compared to what is obtained with spherical balls. These observations are encouraging for the use of models based on geometric primitives to describe a range of microscale processes, and to address the still daunting issue of upscaling to the macroscopic scale.
    Keywords: Pore Scale ; Synchroton X-Ray Micro Computed Tomography ; Soil Air-Water Interfaces ; Computational Geometry ; 3d Volume Segmentation ; Morphological Modeling ; Geology
    ISSN: 0098-3004
    E-ISSN: 1873-7803
    Library Location Call Number Volume/Issue/Year Availability
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