http://research.engr.oregonstate.edu/immiscibles/sites/research.engr.oregonstate.edu.immiscibles/files/Papers/Wildenschild_Sheppard_AWR_2012.pdf

X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems

[1] Snow avalanches are a major natural hazard, endangering human life and infrastructure in mountainous areas throughout the world. In many countries with seasonally snow-covered mountains, avalanche-forecasting services reliably warn the public by issuing occurrence probabilities for a certain region. However, at present, a single avalanche event cannot be predicted in time and space. Much about the release process remains unknown, mainly because of the highly variable, layered character of the snowpack, a highly porous material that exists close to its melting point. The complex interaction between terrain, snowpack, and meteorological conditions leading to avalanche release is commonly described as avalanche formation. It is relevant to hazard mapping and essential to short-term forecasting, which involves weighting many contributory factors. Alternatively, the release process can be studied and modeled. This approach relies heavily on snow mechanics and snow properties, including texture. While the effect of meteorological conditions or changes on the deformational behavior of snow is known in qualitative or semiquantitative manner, the knowledge of the quantitative relation between snow texture and mechanical properties is limited, but promising developments are under way. Fracture mechanical models have been applied to explain the fracture propagation, and micromechanical models including the two competing processes (damage and sintering) have been applied to explain snow failure. There are knowledge gaps between the sequence of processes that lead to the release of the snow slab: snow deformation and failure, damage accumulation, fracture initiation, and fracture propagation. Simultaneously, the spatial variability that affects damage, fracture initiation, and fracture propagation has to be considered. This review focuses on dry snow slab avalanches and shows that dealing with a highly porous media close to its melting point and processes covering several orders of scale, from the size of a bond between snow grains to the size of a mountain slope, will continue to be very challenging.

/pdf/snow-avalanche-formation-2blcpjkk9n.pdf

Snow avalanche formation

This review highlights recent advances in X-ray microcomputed tomography (microCT) as applied to materials, specifically advances made since the first materials microCT review appeared in Internati...

Recent advances in X-ray microtomography applied to materials

Multiple-relaxation-time lattice Boltzmann model for the convection and anisotropic diffusion equation

The specific surface area (SSA) is considered an essential microstructural parameter for the characterization of snow. Photography in the near-infrared (NIR) spectrum is sensitive to the SSA. We calculated the snow reflectance from calibrated NIR images of snow-pit walls and measured the SSA of samples obtained at the same locations. This new method is used to map the snow stratigraphy. The correlation between reflectance and SSA was found to be 90%. Calibrated NIR photography allows quantitative determination of SSA and its spatial variation in a snow profile in two dimensions within an uncertainty of 15%. In an image covering 0.5-1.0 m 2 , even layers of 1 mm thickness can be documented and measured. Spatial maps of SSA are an important tool in initializing and validating physical and chemical models of the snowpack.

/pdf/measuring-specific-surface-area-of-snow-by-near-infrared-2fxhj4u56m.pdf

Measuring specific surface area of snow by near-infrared photography

Estimating the normal vector field on the boundary of discrete three-dimensional objects is essential for rendering and image measurement problems. Most of the existing algorithms do not provide an accurate determination of the normal vector field for shapes that present edges. Here, we propose a new and simple computational method in order to obtain accurate results on all types of shapes, whatever their local convexity degree. The presented method is based on the gradient vector field analysis of the object distance map. This vector field is adaptively filtered around each surface voxel using angle and symmetry criteria so that as many relevant contributions as possible are accounted for. This optimizes the smoothing of digitization effects while preserving relevant details of the processed numerical object. Thanks to the precise normal field obtained, a projection method can be proposed to immediately derive the surface area from a raw discrete object. An empirical justification of the validity of such an algorithm in the continuous limit is also provided. Some results on simulated data and snow images from X-ray tomography are presented, compared to the Marching Cubes and Convex Hull results, and discussed.

https://hal.archives-ouvertes.fr/hal-00185086/document

Adaptive estimation of normals and surface area for discrete 3-D objects: application to snow binary data from X-ray tomography

Simplified Marching Cubes: an efficient discretization scheme for simulations of deposition/ablation in complex media

Once fallen on the ground, snow undergoes a structure metamorphism governed by local temperature and humidity fields. Local 3D curvature is a governing parameter of metamorphism, whereas surface area, directly related to surface energy, can provide a valuable bridge between 3D fine-scale and 1D field-scale snow cover models. X-ray tomography was applied to natural snow at ESRF in order to parameterise the fine-scale behaviour of snow in avalanche risk prediction models. Starting from raw data consisting of a B/W 3D data file, two main approaches are considered. The former uses a triangulation procedure derived from the “marching cube” algorithm. Before triangulation, the B/W data are converted into grey levels and preprocessed in order to smooth the final mesh. The other uses a distance map of the object, from which the field of normal vectors is computed. Assuming the tangent plane approximation, the effective projected area of each surface voxel is derived from simple geometric laws. A cross-validation of both methods is provided on a natural snow image.

/pdf/computation-of-the-surface-area-of-natural-snow-3d-images-fct5tg7bgn.pdf

Computation of the surface area of natural snow 3d images from x-ray tomography: two approaches

A new data structure for the computation of equivalent properties in 3D porous media

Modern X-ray Computerized Micro-Tomography (CMT) facilities allow researchers interested in composite materials and porous media to image their samples in 3D with micrometer resolution. The datasets obtained for representative samples are frequently very large (10243 voxels in gray-scale levels). Performing a tessellation on such datasets would produce hundreds millions facets, which would be impossible to handle in memory on rather powerful computers.Various numerical methods are classical for the prediction of some effective properties of porous and other composite media from the phase properties and the micro-structure (diffusivities, conductivities). The choice of a Monte-Carlo random walk scheme is justified by its minimal memory cost in addition to image storage. In order to employ it, one must be able to perform ray-tracing in large and precise 3D images. The new framework we present allows that feature by using a memory-sparing data structure dedicated to such algorithms.We only store in memory the vertices provided by the marching cube algorithm. So, since the facets are not stored, the needed memory size is divided by a factor of five, without any significant increasing of the computation time: the extraction of properties from very large micro-porous media samples is now possible.This study allows us to claim that a simulation making an intensive use of ray-tracing in tessellated media obtained with the marching-cube algorithm is not as expensive (in terms of memory and time cost) as it could seem. We show that the marching-cube algorithm, when it is used dynamically to connect vertices upon request, is still a very powerful mesh generator since it consumes then very few memory, and that it can be trivially implemented.

Jean-François Delesse

Papers

Adaptive estimation of normals and surface area for discrete 3-D objects: application to snow binary data from X-ray tomography

Simplified Marching Cubes: an efficient discretization scheme for simulations of deposition/ablation in complex media

Computation of the surface area of natural snow 3d images from x-ray tomography: two approaches

A new data structure for the computation of equivalent properties in 3D porous media

An efficient data structure for random walk algorithms in faceted porous media