Stochastic reconstruction of the microstructure of equilibrium form snow and computation of effective elastic properties

Abstract

Three-dimensional geometric descriptions of microstructure are indispensable to obtain the structure-property relationships of snow. Because snow is a random heterogeneous material, it is often helpful to construct stochastic geometric models that can be used to model physical and mechanical properties of snow. In the present study, the Gaussian random field-based stochastic reconstruction of the sieved and sintered dry-snow sample with grain size less than 1 mm is investigated. The one-and two-point correlation functions of the snow samples are used as input for the stochastic snow model. Several statistical descriptors not used as input to the stochastic reconstruction are computed for the real and reconstructed snow to assess the quality of the reconstructed images. For the snow samples and the reconstructed snow microstructure, we also estimate the mechanical properties and the size of the associated representative volume element using numerical simulations as additional assessment of the quality of the reconstructed images. The results indicate that the stochastic reconstruction technique used in this paper is reasonably accurate, robust and highly efficient in numerical computations for the high-density snow samples we consider.

Finite element simulation based‐on atomic force microscopy and nanoindentation for spruce wood microstructure analysis

“…Although direct measurement techniques such as SEM and AFM can be applied to determine the morphology changes of several microstructures (Ghisleni et al, ; Kawasaki et al, ; Zhu, Zhang, & Zhao, ), they are commonly hard to perform and it takes a lot of time. Furthermore, the continuous irregular ordering of the microstructure throughout the real sample make simulation more practical; since simulations methods do not require any additional experiments to iterate the mechanical properties or boundary conditions (Samak, Fischer, & Rittel, ; Yuan, Lee, & Guilkey, ).…”

Slip-based experimental studies of a vehicle interacting with natural snowy terrain

“…Similar trend exists for the right front and right rear tires [c and d of Figs. [8][9][10] except that the traction forces for the two tires are similar in magnitude.…”

“…The depth of snow as well as any distinction among different snow layers are obtained using the snow micropenetrometer. The density of snow, important for the mechanical properties of snow, can be obtained using 3-D Microtomography in the laboratory with high accuracy [9].…”

“…Recent modeling of tire-snow interactions takes into account the significance of the effects of longitudinal slip, snow depth and vertical load on the interfacial forces between tire and snow [1,[3][4][5][6][7][8]. The properties of natural snow cover are best and more realistically modeled stochastically [9] rather than deterministically, necessitating the development of accurate stochastic tiresnow interaction models [6][7][8]. In these models, the statistical distributions of select parameters, viewed as uncertainties, are used as inputs to a deterministic tire-snow interaction model.…”

Determination of failure envelope for faceted snow through numerical simulations

“…Lately, direct 3D reconstruction of snow microstructure (Brzoska et al, 1999;Schneebeli and Sokratov, 2004) at resolutions down to a few microns has become possible with X-ray micro-computed tomography (μ-CT). Schneebeli (2004), Srivastava et al (2010), Yuan et al (2010) and Theile et al (2011) applied different numerical techniques on the 3D microstructure of snow to obtain its mechanical properties/behavior. The constitutive behavior of ice determines the mechanical behavior of snow, which in turn helps to understand the slab avalanche release mechanism and hence snowpack stability.…”