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Material mapping strategy to identify the density-dependent properties of dry natural snow
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.ORCID iD: 0000-0001-6231-8944
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.ORCID iD: 0000-0001-7395-3302
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.ORCID iD: 0000-0001-8225-989x
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2024 (English)In: Applied Physics A: Materials Science & Processing, ISSN 0947-8396, E-ISSN 1432-0630, Vol. 130, no 2, article id 141Article in journal (Refereed) Published
Abstract [en]

The mechanical properties of natural snow play a crucial role in understanding glaciers, avalanches, polar regions, and snow-related constructions. Research has concentrated on how the mechanical properties of snow vary, primarily with its density; the integration of cutting-edge techniques like micro-tomography with traditional loading methods can enhance our comprehension of these properties in natural snow. This study employs CT imaging and uniaxial compression tests, along with the Digital Volume Correlation (DVC) to investigate the density-dependent material properties of natural snow. The data from two snow samples, one initially non-compressed (test 1) and the other initially compressed (test 2), were fed into Burger’s viscoelastic model to estimate the material properties. CT imaging with 801 projections captures the three-dimensional structure of the snow initially and after each loading step at -18C, using a constant deformation rate (0.2 mm/min). The relative density of the snow, ranging from 0.175 to 0.39 (equivalent to 160–360 kg/m), is determined at each load step through binary image segmentation. Modulus and viscosity terms, estimated from Burger’s model, exhibit a density-dependent increase. Maxwell and Kelvin–Voigt moduli range from 0.5 to 14 MPa and 0.1 to 0.8 MPa, respectively. Viscosity values for the Maxwell and Kelvin–Voigt models vary from 0.2 to 2.9 GPa-s and 0.2 to 2.3 GPa-s within the considered density range, showing an exponent between 3 and 4 when represented as power functions. Initial grain characteristics for tests 1 and 2, obtained through image segmentation, reveal an average Specific Surface Area (SSA) of around 55 1/mm and 40 1/mm, respectively. The full-field strain distribution in the specimen at each load step is calculated using the DVC, highlighting strong strain localization indicative of non-homogeneous behavior in natural snow. These findings not only contribute to our understanding of natural snow mechanics but also hold implications for applications in fields such as glacier dynamics and avalanche prediction.

Place, publisher, year, edition, pages
Springer Nature, 2024. Vol. 130, no 2, article id 141
Keywords [en]
Material mapping, Micro tomography, Compression test, Digital volume correlation, Snow and ice
National Category
Other Materials Engineering
Research subject
Experimental Mechanics; Fluid Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-104236DOI: 10.1007/s00339-024-07288-yISI: 001153419300002Scopus ID: 2-s2.0-85183678465OAI: oai:DiVA.org:ltu-104236DiVA, id: diva2:1836846
Note

Validerad;2024;Nivå 2;2024-02-12 (joosat);

CC BY Full text license

Available from: 2024-02-12 Created: 2024-02-12 Last updated: 2024-05-06Bibliographically approved
In thesis
1. Mechanics of Ice and Snow as a Granular Material
Open this publication in new window or tab >>Mechanics of Ice and Snow as a Granular Material
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis, the mechanical properties of ice and dry snow as a class of granular materials are investigated through a series of experiments, analyses, and simulations. The primary focus is on understanding the intricate details of ice sintering, capillary bridge formation, and the behavior of snow under varying conditions.

The investigation into ice sintering reveals a formulation of the sintering force, considering temperature, pressing force, contact duration, and particle size during the primary sintering stage. The results indicate a nearly linear increase in sintering force with external pressing force, while dependency on contact duration and particle size follows a nonlinear power-law relationship. The temperature dependence of the sintering force is nonlinear, aligning with the Arrhenius equation. The ultimate tensile strength of ice and the axial stress concentration factor are identified as crucial factors in determining the sintering force. Additionally, observations near the melting point reveal the formation of a liquid bridge between contacted ice particles.

Moving on to capillary bridge formation, the experiments demonstrate the presence of a liquid bridge between an ice particle and a smooth (or rough) aluminum surface at controlled temperature conditions. The separation distance is found to be proportional to the cube root of the bridge volume, which decreases with decreasing temperature. Notably, for a rough surface, capillary bridge formation diminishes under the considered experimental conditions.

The significance of snow in various contexts prompts an exploration of its mechanical properties. Utilizing micro-computed tomography imaging and quasi-static mechanical loading, a methodology for mapping the density-dependent material properties of manufactured snow is established. The study investigates structural parameter variations during loading, revealing insights into the three-dimensional structure, relative density, and mechanical behavior of snow. Results from Burger’s model show an increasing trend in modulus and viscosity terms with density. Digital volume correlation aids in calculating full-field strain distribution, highlighting particle characteristics and changes in specific surface areas during loading.

Expanding the scope to natural snow, cutting-edge techniques like micro-tomography are integrated with traditional loading methods. Employing CT imaging and uniaxial compression tests, along with digital volume correlation, density-dependent material properties are analyzed. The study incorporates two snow samples, revealing density-dependent trends in modulus and viscosity terms. The results provide valuable insights into the non-homogeneous behavior of natural snow and contribute to fields such as glacier dynamics and avalanche prediction.

Finally, the discrete element method with a variable bond model is used to simulate the behavior of granular materials, specifically focusing on snow. The model incorporates temperature dependent cohesion and effectively captures the angle of repose and stress-strain behavior of snow.

In summary, this thesis presents an investigation into the mechanical properties of ice, capillary bridge formation, manufactured snow, natural snow, and granular materials, providing insights and contributing to the understanding of ice and snow in various environmental and engineering contexts.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2024
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
micro tomography, mechanics, ice and snow, sintering force, thin liquid layer, discrete element method
National Category
Geotechnical Engineering
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-105285 (URN)978-91-8048-558-6 (ISBN)978-91-8048-559-3 (ISBN)
Public defence
2024-06-12, E632, Luleå University of Technology, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2024-04-29 Created: 2024-04-29 Last updated: 2024-05-22Bibliographically approved

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Bahaloohoreh, HassanForsberg, FredrikLycksam, HenrikCasselgren, JohanSjödahl, Mikael

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