3D Analysis of Deformation and Porosity of Dry Natural Snow during Compaction

Eppanapelli, Lavan Kumar; Forsberg, Fredrik; Casselgren, Johan; Lycksam, Henrik

doi:10.3390/ma12060850

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…This figure offers a clear overview of the comprehensive process involved in understanding and utilizing three-dimensional solid modeling in snow research [121]. [125]. (e) Typical snow types presented as snow grains (left) and as surface renderings (right) [126].…”

Section: Three-dimensional Solid Modeling Of Snowmentioning

confidence: 99%

“…(e) Typical snow types presented as snow grains (left) and as surface renderings (right) [126]. (f) Two images separated by 2 days from a time-lapse movie [126], image (A) at time 577 h, image (B) at time 625 h. (g) Three-dimensional images of the distribution of snow grains [125]. (h) µCT detect micro compression devices [127].…”

Section: Three-dimensional Solid Modeling Of Snowmentioning

confidence: 99%

“…(c) The SkyScan material test bench for the micro-CT system for scanning snow blocks [124]. (d) The experimental arrangement of the micro-CT system [125]. (e) Typical snow types presented as snow grains (left) and as surface renderings (right) [126].…”

Section: Avalanche Behavior In Snow Failure and Simulation In Practic...mentioning

confidence: 99%

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Antarctic Snow Failure Mechanics: Analysis, Simulations, and Applications

Xiao,

Li,

Matin Nazar

et al. 2024

Materials

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Snow failure is the process by which the stability of snow or snow-covered slopes is destroyed, resulting in the collapse or release of snow. Heavy snowfall, low temperatures, and volatile weather typically cause consequences in Antarctica, which can occur at different scales, from small, localized collapses to massive avalanches, and result in significant risk to human activities and infrastructures. Understanding snow damage is critical to assessing potential hazards associated with snow-covered terrain and implementing effective risk mitigation strategies. This review discusses the theoretical models and numerical simulation methods commonly used in Antarctic snow failure research. We focus on the various theoretical models proposed in the literature, including the fiber bundle model (FBM), discrete element model (DEM), cellular automata (CA) model, and continuous cavity-expansion penetration (CCEP) model. In addition, we overview some methods to acquire the three-dimensional solid models and the related advantages and disadvantages. Then, we discuss some critical numerical techniques used to simulate the snow failure process, such as the finite element method (FEM) and three-dimensional (3D) material point method (MPM), highlighting their features in capturing the complex behavior of snow failure. Eventually, different case studies and the experimental validation of these models and simulation methods in the context of Antarctic snow failure are presented, as well as the application of snow failure research to facility construction. This review provides a comprehensive analysis of snow properties, essential numerical simulation methods, and related applications to enhance our understanding of Antarctic snow failure, which offer valuable resources for designing and managing potential infrastructure in Antarctica.

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Section: Three-dimensional Solid Modeling Of Snowmentioning

confidence: 99%

Section: Three-dimensional Solid Modeling Of Snowmentioning

confidence: 99%

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Antarctic Snow Failure Mechanics: Analysis, Simulations, and Applications

Xiao,

Li,

Matin Nazar

et al. 2024

Materials

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“…Specifically, a negative correlation was observed between compacted snow hardness and equivalent particle size, while a positive correlation was observed between compacted snow hardness and fractal dimension. The compacted snow hardness prediction equation is shown in Equation ( 16): H = −4127.453 + 4504.846D f − 721.638D p r = 0.973 (16) where H is the predicted value of the compacted snow hardness regression equation and D f and D p are the average fractal dimension and average equivalent particle size of compacted snow crystal particles, respectively. The range of D f is 1.05-1.25 and the range of D p is 0.25-1.05.…”

Section: Effect Of Particle Morphology On Compacted Snow Hardnessmentioning

confidence: 99%

“…Datt et al [15] measured various acoustical properties of natural snow such as the acoustic absorption coefficient, surface impedance, and transmission losses across different snow samples, followed by the inverse characterization of different geometrical parameters of snow. Eppanapelli et al [16] conducted in situ experiments using an X-ray-computed microtomography (micro-CT) system at four load states (0 MPa, 0.3 MPa, 0.6 MPa, and 0.8 MPa) to investigate the effect of compaction on the structural features of snow grains and the vertical heterogeneity of porosity distribution of snow samples under applied stress. Singh et al [17] discussed the methodology of Synthetic Aperture Radar (SAR) data analysis for studying snow porosity and the effect of snow porosity on snow wetness, snow density, and the backscattering coefficient, with an absolute error of only 0.045 from the measured porosity.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on the Morphology of Snow Crystal Particles and Its Influence on Compacted Snow Hardness

Hu,

Li,

et al. 2024

Water

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In their natural state, snow crystals are influenced by the atmosphere during formation and multiple factors after landing, resulting in varying particle sizes and unstable particle morphologies that are challenging to quantify. The current research mainly focuses on the relationship between the porosity of compacted snow samples or qualitatively describes snow crystals and their macroscopic physical properties, ignoring that the significant differences in the morphology of snow crystals also affect their physical properties. To quantitatively evaluate the morphology of snow crystals, we employed optical microscopy to obtain digital images of snow crystals in Harbin, utilizing the Sobel and Otsu algorithms to determine the equivalent particle size and fractal dimension of individual snow particles. In addition, the hardness of snow with a density of 0.4 g/cm3 was measured through a penetration test, with an analysis of its correlation relative to particle size and fractal dimension. The results indicated the fractal dimension as an effective parameter for characterizing particle shape, which decreased rapidly over time and then fluctuated within the range of 1.10 to 1.15. During the initial period, natural snow crystals broke down rapidly, leading to an increase in the percentage of natural snow crystals with an equivalent particle size of 0.2–0.4 mm up to 51.86%. After three days, the sintering effect between snow crystals was enhanced, resulting in an even distribution of the equivalent particle size. Finally, multiple linear regression analysis showed a positive correlation between compacted snow hardness and fractal dimension, with a negative correlation between compacted snow hardness and equivalent particle size. These findings offer valuable technical support and data reference for exploring the relationship between snow’s mechanical properties and its microscopic particle shape.

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Mapping of density-dependent material properties of dry manufactured snow using $$\mu$$CT

Bahaloo,

Forsberg,

Casselgren

et al. 2023

Appl. Phys. A

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Despite the significance of snow in various cryospheric, polar, and construction contexts, more comprehensive studies are required on its mechanical properties. In recent years, the utilization of $$\mu$$ μ CT has yielded valuable insights into snow analysis. Our objective is to establish a methodology for mapping density-dependent material properties for dry manufactured snow within the density range of 400–600 kg/m$$^3$$ 3 utilizing $$\mu$$ μ CT imaging and step-wise, quasi-static, mechanical loading. We also aim to investigate the variations in the structural parameters of snow during loading. The three-dimensional (3D) structure of snow is captured using $$\mu$$ μ CT with 801 projections at the beginning of the experiments and at the end of each loading step. The sample is compressed at a temperature of − 18 $$\mathrm {^o}$$ o C using a constant rate of deformation (0.2 mm/min) in multiple steps. The relative density of the snow is determined at each load step using binary image segmentation. It varies from 0.44 in the beginning to nearly 0.65 at the end of the loading, which corresponds to a density range of 400–600 kg/m$$^3$$ 3 . The estimated modulus and viscosity terms, obtained from the Burger’s model, show an increasing trend with density. The values of the Maxwell and Kelvin–Voigt moduli were found to range from 60 to 320 MPa and from 6 to 40 MPa, respectively. Meanwhile, the viscosity values for the Maxwell and Kelvin–Voigt models varied from 0.4 to 3.5 GPa-s, and 0.3–3.2 GPa-s, respectively, within the considered density range. In addition, Digital Volume Correlation (DVC) was used to calculate the full-field strain distribution in the specimen at each load step. The image analysis results show that, the particle size and specific surface area (SSA) do not change significantly within the studied range of loading and densities, while the sphericity of the particles is increased. The grain diameter ranges from approximately 100 $$\upmu$$ μ m to nearly 400 $$\upmu$$ μ m, with a mode of nearly 200 $$\upmu$$ μ m. The methodology presented in this study opens up a path for an extensive statistical analysis of the material properties by experimenting more snow samples.

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3D Analysis of Deformation and Porosity of Dry Natural Snow during Compaction

Cited by 7 publications

References 36 publications

Antarctic Snow Failure Mechanics: Analysis, Simulations, and Applications

Antarctic Snow Failure Mechanics: Analysis, Simulations, and Applications

Experimental Study on the Morphology of Snow Crystal Particles and Its Influence on Compacted Snow Hardness

Mapping of density-dependent material properties of dry manufactured snow using $$\mu$$CT

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