Numerical and experimental investigations of the effective thermal conductivity of snow

Abstract

[1] We carried out numerical simulations of the conductivity of snow using microtomographic images. The full tensor of the effective thermal conductivity (k eff ) was computed from 30 three-dimensional images of the snow microstructure, spanning all types of seasonal snow. Only conduction through ice and interstitial air were considered. The obtained values are strongly correlated to snow density. The main cause for the slight scatter around the regression curve to snow density is the anisotropy of k eff : the vertical component of k eff of facetted crystals and depth hoar samples is up to 1.5 times larger than the horizontal component, while rounded grains sampled deeply in the snowpack exhibit the inverse behavior. Results of simulations neglecting the conduction in the interstitial air indicate that this phase plays a vital role in heat conduction through snow. The computed effective thermal conductivity is found to increase with decreasing temperature, mostly following the temperature dependency of the thermal conductivity of ice. The results are compared to experimental data obtained either with the needle-probe technique or using combined measurements of the vertical heat flux and the corresponding temperature gradient. Needle-probe measurements are systematically significantly lower than those from the two other techniques. The observed discrepancies between the three methods are investigated and briefly discussed.

Thermal diffusivity of seasonal snow determined from temperature profiles

“…[20,12]). This methodology uses tomographic 3D images of snow microstructure and the steady-state heat transport equation to determine the effective thermal conductivity by separating the heat transfer over the ice matrix and pore spaces for a volumetric snow sample [12]. Beyond the specialized equipment necessary for these types of analyses, the methods are relatively labor intensive and require careful extraction and storage of snow samples.…”

“…Another highly technical approach for determining thermal conductivity is based on microstructure tomography (e.g. [20,12]). This methodology uses tomographic 3D images of snow microstructure and the steady-state heat transport equation to determine the effective thermal conductivity by separating the heat transfer over the ice matrix and pore spaces for a volumetric snow sample [12].…”

“…It continuously undergoes morphological changes in grain size and shape, density, and bonding due to metamorphism, all of which affect the snow thermal properties [3]. Thermal conductivity of snow is also anisotropic, having different values in horizontal and vertical directions [12]. A better understanding of heat transfer processes in snow and their natural impacts requires a better understanding of how thermophysical properties vary in time [13].…”

Microstructure-based modeling of snow mechanics: a discrete element approach

“…In contrast, in this study, density appears to be a very good indicator of the resistance of snow under compression, with only a little scatter attributable to the snow type. In other microstructure-based models, a similar strong dependence on density was also observed for thermal conductivity (Calonne et al, 2011), tensile strength (Hagenmuller et al, 2014c) or the Young's modulus (Kochle and Schneebeli, 2014). The main difference between the microstructure-based simulations and direct experimental measurements is the size of the volume tested.…”

“…These images were obtained from controlled cold-room experiments (samples s-DFRG, s-RG0, s-RG1 measured by Flin et al (2004) during an isothermal metamorphism experiment; sample s-FC measured by Calonne et al (2011) during a temperaturegradient experiment) or field sampling (sample s-DF measured by Flin et al (2011) and samples s-FCDF and s-FCDH measured by Hagenmuller et al, 2013). All samples were scanned with X-ray absorption tomography after impregnation of the material with 1-chloronaphtalene .…”