scholarly journals Relationships between Surface Properties and Snow Adhesion and Its Shedding Mechanisms

2020 ◽  
Vol 10 (16) ◽  
pp. 5407
Author(s):  
Jamie Heil ◽  
Behrouz Mohammadian ◽  
Mehdi Sarayloo ◽  
Kevin Bruns ◽  
Hossein Sojoudi
Keyword(s):  
Surface Energy ◽  
Surface Temperature ◽  
Water Content ◽  
Adhesion Strength ◽  
Liquid Water ◽  
Liquid Water Content ◽  
Snow Accumulation ◽  
Dispersive Surface Energy ◽  
Wet Snow ◽  
Intermediate Surface

Understanding the mechanisms of snow adhesion to surfaces and its subsequent shedding provides means to search for active and passive methods to mitigate the issues caused by snow accumulation on surfaces. Here, a novel setup is presented to measure the adhesion strength of snow to various surfaces without altering its properties (i.e., liquid water content (LWC) and/or density) during the measurements and to study snow shedding mechanisms. In this setup, a sensor is utilized to ensure constant temperature and liquid water content of snow on test substrates, unlike inclined or centrifugal snow adhesion testing. A snow gun consisting of an internal mixing chamber and ball valves for adjusting air and water flow is designed to form snow with controlled LWC inside a walk-in freezing room with controlled temperatures. We report that snow adheres to surfaces strongly when the LWC is around 20%. We also show that on smooth (i.e., RMS roughness of less than 7.17 μm) and very rough (i.e., RMS roughness of greater than 308.33 μm) surfaces, snow experiences minimal contact with the surface, resulting in low adhesion strength of snow. At the intermediate surface roughness (i.e., RMS of 50 μm with a surface temperature of 0 °C, the contact area between the snow and the surface increases, leading to increased adhesion strength of snow to the substrate. It is also found that an increase in the polar surface energy significantly increases the adhesion strength of wet snow while adhesion strength decreases with an increase in dispersive surface energy. Finally, we show that during shedding, snow experiences complete sliding, compression, or a combination of the two behaviors depending on surface temperature and LWC of the snow. The results of this study suggest pathways for designing surfaces that might reduce snow adhesion strength and facilitate its shedding.

scholarly journals Active prevention of snow accumulation on cameras of autonomous vehicles

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Behrouz Mohammadian ◽  
Mehdi Sarayloo ◽  
Jamie Heil ◽  
Haiping Hong ◽  
Sunil Patil ◽  
...  
Keyword(s):  
Water Content ◽  
Wind Velocity ◽  
Liquid Water ◽  
Autonomous Vehicles ◽  
Liquid Water Content ◽  
Snow Accumulation ◽  
Collision Efficiency ◽  
Wet Snow ◽  
Wind Velocities ◽  
The Impact

Abstract Accumulation of atmospheric icing, particularly wet snow, on the visual sensors/navigators of autonomous vehicles (AVs) increases the possibility of accidents by obstructing the lenses of the sensors. Here, two navigator designs were suggested that use airflow across the lens surfaces of the AVs to prevent snow accumulation on them. The impact of airflow intensity across the lens, wind velocity (relative velocity of wind with respect to vehicle), and liquid water content of snow on prevention of snow accumulation on the lenses of the AVs was explored experimentally. Here, artificial snow grains were formed using a novel snow gun and their average sizes at low liquid water content (LWC of ≈ 8%) and high liquid water content (LWC of ≈ 28%) were measured to study the impact of grain sizes on snow accumulation on camera lenses. The effects of wind velocity, snow density, and diameter of the snow grains on their trajectory in the testing section were also studied numerically. The results indicated that the snow grains with higher velocity, density, or diameter possessed higher inertia forces and were more prone to collide with the navigator, increasing collision efficiency of snow grains. We realized that the airflow across the lens effectively prevented snow accumulation on the lens at vehicle/wind velocities of up to 20 mph. The proposed designs actively reduced the snow accumulation on the camera lens, promising to be applied in future AVs. Graphic abstract

scholarly journals Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

2019 ◽  
Vol 55 (5) ◽  
pp. 4465-4487 ◽  
Author(s):  
Franziska Koch ◽  
Patrick Henkel ◽  
Florian Appel ◽  
Lino Schmid ◽  
Heike Bach ◽  
...  
Keyword(s):  
Time Delay ◽  
Water Content ◽  
Liquid Water ◽  
Snow Water Equivalent ◽  
Liquid Water Content ◽  
Signal Attenuation ◽  
Wet Snow ◽  
Snow Water

scholarly journals A portable calorimeter for measuring liquid-water content of wet snow

1998 ◽  
Vol 26 ◽  
pp. 103-106 ◽  
Author(s):  
Katsuhisa Kawashima ◽  
Toru Endo ◽  
Yukari Takeuchi
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Liquid Water Content ◽  
Total Weight ◽  
Light Weight ◽  
High Mountains ◽  
Insulating Materials ◽  
Wet Snow ◽  
Snow Water

In order to facilitate the measurement of liquid-water content of snow in high mountains, a portable calorimeter named “Endo-type snow-water content meter” was developed. It is composed of a metal-coated container made of insulating materials and a lid of the container with a small-thermistor thermometer. Its strong points are its light weight, small size and easy fabrication with cheap materials. The total weight of the device is as light as 250 g, which is less than 10% of the snow-water content meter widely used in Japan (Akitaya-type snow-water content meter). The results of experiments have revealed that the device is capable of measuring the liquid-water content within 2 minutes with an accuracy of 2% by weight.

Snow Wetness Influence on Impulse Radar Snow Surveys Theoretical and Laboratory Study

2000 ◽  
Vol 31 (2) ◽  
pp. 89-106 ◽  
Author(s):  
A. Lundberg ◽  
H. Thunehed
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Snow Water Equivalent ◽  
Liquid Water Content ◽  
Late Winter ◽  
Propagation Time ◽  
Reservoir Water ◽  
A Value ◽  
Wet Snow ◽  
Snow Water

The snow-water equivalent of late-winter snowpack is of utmost importance for hydropower production in areas where a large proportion of the reservoir water emanates from snowmelt. Impulse radar can be used to estimate the snow-water equivalent of the snowpack and thus the expected snowmelt discharge. Impulse radar is now in operational use in some Scandinavian basins. With radar technology the radar wave propagation time in the snowpack is converted into snow-water equivalent with help of a parameter usually termed the a-value. Use of radar technology during late winter brings about risk for measurements on wet snow. The a-value for dry snow cannot be used directly for wet snow. We have found that a liquid-water content of 5% (by volume) reduces the a-value by approximately 20%. In this paper an equation, based on snow density and snow liquid water content, for calculation of wet-snow a-value is presented.

scholarly journals Investigation on Wet-Snow Metamorphism in Respect of Liquid-Water Content

1989 ◽  
Vol 13 ◽  
pp. 22-26 ◽  
Author(s):  
E. Brun
Keyword(s):  
Growth Rate ◽  
Water Content ◽  
Liquid Water ◽  
Liquid Water Content ◽  
Snow Metamorphism ◽  
Wet Snow ◽  
The Mean ◽  
Water Saturated ◽  
Warming Device ◽  
Snow Samples

Up to the present time, quantitative investigations on wet-snow metamorphism have mostly been conducted on water-saturated snow, because of the difficulty in getting large enough wet-snow samples at a uniformly low liquid-water content. Using the dielectric properties of snow at a frequency in the range 20–100 kHz, a warming device has been developed which has enabled us to bring samples of 7 × 10−3 m3 snow to any desired liquid-water content. A maximum value of 8% by volume was reached within 2 h.The warming device was used to reproduce natural wetness conditions in the laboratory in order to investigate wet snow metamorphism at low liquid-water content. Snow samples were brought to different liquid-water contents and held in that condition for about 2 weeks, during which grain-size was characterized using a picture-analysis system able to derive the mean radius of curvature of the cluster circumference. At any given liquid-water content value, the growth rate of the mean volume of the crystals building the clusters was constant, a pattern which has also been observed in water-saturated snow by previous investigators. This growth rate is well described by a power function of liquid-water content.

scholarly journals Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

2021 ◽  
Vol 13 (21) ◽  
pp. 4223
Author(s):  
Randall Bonnell ◽  
Daniel McGrath ◽  
Keith Williams ◽  
Ryan Webb ◽  
Steven R. Fassnacht ◽  
...  
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Electromagnetic Waves ◽  
Snow Water Equivalent ◽  
Canopy Cover ◽  
Liquid Water Content ◽  
Spatiotemporal Variability ◽  
Snow Density ◽  
Wet Snow ◽  
Observation Method

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE.

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