Compacted Snow Testing Methodology and Instrumentation

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Date

2024-03-05

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Publisher

Virginia Tech

Abstract

Snow is a complex material that is difficult to characterize especially due to its high compressibility and temperature-sensitive nonlinear viscoelasticity. Snow mechanics has been intensively investigated by avalanche and army researchers for decades. However, fewer research studies have been published for compacted snow, commonly defined as snow with a density in the range of 370-560 kg/m3. From a mobility perspective, the tires are the primary point of force and motion generation and their interaction with the terrain causes an increased reliance on the skill of the driver for safer mobility. Thus, standards like ASTM F1805 are implemented for the evaluation of winter tires which can be used in harsh conditions like ice and snow. This work focuses on evaluating the prior efforts performed for the measurement of snow properties. In addition, analysis using regression models and principal component analysis is performed to understand the extent to which specific measurements related to snow affect the traction of the tire. It was found that the compressive and shear properties of snow contribute more than 90% to the variation in the traction coefficient of a tire when evaluated on a compacted snow domain per ASTM F1805. Identification of this phenomenon allowed the enhancement of an existing device that can be used for measuring the compaction and shear properties of snow. The device hence conceptualized was manufactured in-house and tested at the Smithers Winter Test Center to benchmark against existing devices available commercially. Further, a more analytical method for evaluating the resistive pressure for the penetration of the device was formulated. Based on this, a possible framework for the determination of the bevameter parameters using measurements of the new device has been proposed which needs to be validated experimentally and computationally.

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Keywords

compacted snow, snow testing, Clegg Hammer, Cone Penetrometer, snow material properties, regression learning

Citation