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Wet snow accumulation on bridge cables and its shedding due to external phenomena such as rise in temperature, wind, and gravity is a serious threat to the safety of cars and pedestrians crossing the bridge. Commonly the accumulated snow on bridge cables is removed by external means such as mechanical removal or heat treatment which are expensive, time-consuming, and high-risk processes and are conducted based on little or no information available regarding the actual size and shape of the accumulated snow. In addition, cleaning of cables using the mechanical methods can potentially lead to erosion of cable materials when applied over years, resulting in enhanced surface roughness and potentially increased wet snow/ice accumulation during future precipitation events, and sometimes might require replacement of cable stays, which is an extremely costly and complicated task. Optimizing the number of mechanical cleaning procedures such as chain release through predicting the shape and thickness of the accumulated snow on the cable stays reduces the cost, time, and risk associated with the process. In this study, wet snow accumulation on torsionally rigid inclined cylinders of high-density polyethylene (HDPE) has been studied experimentally and numerically. A 2-D numerical model has been developed utilizing weather data to predict the thickness and the shape of the accumulated wet snow on inclined cylindrical surfaces. Outdoor experiments were also conducted to measure the density and thickness of accumulated snow, while monitoring the weather data real time. Overall, snow density was found to be linearly increasing with an increase in wind velocity, during snow precipitation. The maximum thickness and shape of the accumulated snow on cables obtained from the numerical model were found to be in good agreement with the outdoor experimental data. This work aims to provide a mean for prediction of snow accumulation on surfaces for optimizing the efficiency of the costly and high-risk snow removal procedures. [ABSTRACT FROM AUTHOR]

This review paper examines the dynamics of snow accumulation on vehicle surfaces and its impacts on vehicle performance and safety. It focuses on the use of computational fluid dynamics (CFD) to model snow ingress in vehicle air intakes and its interactions with sensors in advanced driver assistance systems (ADAS). Central to these studies is the coefficient of restitution (COR), which measures the elastic properties of snow upon collision with vehicle surfaces. The paper provides an overview of various turbulence models, such as large eddy simulation (LES) and Reynolds-averaged Navier-Stokes (RANS), assessing their effectiveness in simulating the aerodynamic fields that affect snow trajectories. It critically reviews existing models and highlights recent experimental studies related to COR of snowflakes based on different impact velocities and particle conditions. The discussion on the practical implementation of these findings in automotive design underscores their importance in enhancing vehicle safety and reliability under extreme weather conditions. [Display omitted] [ABSTRACT FROM AUTHOR]

Snow accumulation alters the energy budget of engineered (i.e. photovoltaic panels) and natural surfaces (i.e. earth) by affecting the amount of solar energy these surfaces can absorb. Falling of accumulated snow from overhead structures (i.e. telecommunication towers, power lines, wind turbines, and bridge cables) and slipping pedestrians and vehicles on surfaces covered with snow and ice can lead to injuries and safety issues. This review article aimed to provide an overview of snow from its nucleation/formation fundamentals to its interaction with man-made and natural surfaces leading to its accumulation, followed by its removal via shedding and/or melting. Mechanical, thermal, and thermodynamics properties of snow were reviewed providing insights on their impact on snow interaction with surfaces. Finally, currently-available active and passive techniques to mitigate issues associated with snow accumulation on surfaces were reviewed, and perspectives on challenges ahead were provided. [Display omitted] • Attachment kinetics and diffusion of water vapor molecules during formation of snowflakes were reviewed. • Existing models of snowflake interaction with surfaces and their accumulation behavior were reviewed. • Mechanical and thermal properties of snow layers were summarized, highlighting their impact on snow removal from surfaces. • Active and passive snow removal methods were critically reviewed, while providing perspectives for future studies. [ABSTRACT FROM AUTHOR]

• Snow removal from photovoltaic modules were classified into four distinct mechanisms. • A sensor was used to monitor/measure the liquid water content (LWC) of snow. • The surface-mountable sensor enabled real-time and non-destructive measurement of the LWC. • The LWC measurements enabled classification of snow removal mechanisms. • Classification of snow removal mechanisms could lead to efficient snow mitigation strategies. Snow accumulation on photovoltaic (PV) modules causes major economic losses by reducing/preventing solar energy production. To develop mitigation strategies for snow accumulation issues on the PV modules, it is crucial to understand various snow removal mechanisms from them. Here, natural snow accumulation and its removal from the PV modules were investigated over two years using a surface-mountable sensor that measures liquid water content (LWC) at the interface of snow and module's surface real-time. It was found that the snow removal from the PV modules occurs under four distinct categories: melting, shedding (fast sliding), prolonged melting, and melting followed by shedding. During snow melting, the average LWC of snow and the module's surface temperature increased gradually (13.45%/hr and 0.81 °C/hr), while during snow shedding the average LWC and surface temperature sharply increased (14.4%/hr and 1.46 °C/hr). During prolonged melting, the average LWC of snow slowly increased during the day (4 average LWC slopes were 5.88, 3.22, 6.18, and 7.92%/hr), and the module's surface temperature reached 0 °C for a very short period of time, then decreased back to freezing temperatures at night. Lastly, the final snow removal mechanism is melting followed by shedding, during which the average LWC of snow increased gradually indication of snow melting (LWC of 11.25%/hr), followed by a very sharp increase in LWC with an average rate of ∼83.02%/hr, after which snow shedding occurred. These understanding provide means to develop a combined passive and active mitigation strategy for efficient snow removal from the photovoltaic modules. [ABSTRACT FROM AUTHOR]

Ice and snow accumulation on structures can cause serious safety issues due to falling pieces of accumulated ice as well as overloading of the structure. It is crucial to detect ice and the degree of wetness of snow when selecting a mitigation strategy such as applying anti-icing/de-icing techniques and/or notifying personnel to take necessary action. In this study, a sensor was developed to detect the ice presence and determine the liquid water content (LWC) by mass of wet snow accumulated on a surface through measuring the electrical resistance and temperature of the medium covering the surface, namely, the Ice Presence and Snow Liquid Water Content (ISLWC) sensor. This is a lightweight and flexible sensor that can be installed on surfaces such as bridge cables and provides a real-time and non-destructive way to detect ice or measure the LWC of snow in layers as thin as a few millimeters. The detection algorithm used was developed using the thresholds obtained from numerous indoor experiments data. Outdoor tests verified the detection accuracy and reliability as well as the ISLWC sensor durability under extreme weather conditions. Both indoor and outdoor experiment results showed that this sensor is an efficient tool for real-time monitoring and mitigation of icing problems on structures. • A sensor was developed to detect ice and snow presence on the surface of structures. • The sensor is crucial in detection of ice and snow accretion and shedding events. • The sensor is flat, small in size, lightweight, and can adhere to any surface. • Numerous indoor and outdoor experiments conducted to confirm the sensor performance. • The sensor results were verified visually and compared to existing ice/snow sensors. [ABSTRACT FROM AUTHOR]