scholarly journals Experimental and Theoretical Studies of Wet Snow Accumulation on Inclined Cylindrical Surfaces

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Behrouz Mohammadian ◽  
Mehdi Sarayloo ◽  
Ahmed Abdelaal ◽  
Asif Raiyan ◽  
Douglas K. Nims ◽  
...  
Keyword(s):  
High Risk ◽  
Numerical Model ◽  
Snow Accumulation ◽  
Weather Data ◽  
Mechanical Removal ◽  
Ice Accumulation ◽  
Mechanical Methods ◽  
Wet Snow ◽  
Cable Stays ◽  
Risk Processes

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.

scholarly journals Development of a meteorological forecast for snow accumulation on transmission lines

1993 ◽  
Vol 18 ◽  
pp. 107-112
Author(s):  
Tatsuhito Ito ◽  
Masaru Yamaoka ◽  
Hisayuki Ohura ◽  
Takashi Taniguchi ◽  
Gorow Wakahama
Keyword(s):  
Air Temperature ◽  
Transmission Lines ◽  
Wind Direction ◽  
Mesoscale Model ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Surface Winds ◽  
Temperature And Precipitation ◽  
Wet Snow

In Hokkaido we have often experienced hazardous accidents, such as tower collapses and conductor breakage, caused by wet-snow accretion on transmission lines, and over many years have developed countermeasures for wet-snow accretion. Recently we have been developing a system to forecast areas where snow accretion may occur. We used the southern part of Hokkaido, divided into 5 km × 5 km meshes, as a forecast area; our predictions were hourly, 3–24 hours in advance. A method of predicting meteorological data which forms an important part of the system predicts three elements which influence wet-snow accretion: air temperature, precipitation, and wind direction and speed. We used an interpolation for predicting temperature and precipitation and a one-level, mesoscale model for diagnosing surface winds for wind direction and speed. By applying the method to many examples of wet-snow accretion, we checked the prediction of weather elements.

Simulation and Optimization of a Concentrated Photovoltaic System

2005 ◽  
Vol 128 (2) ◽  
pp. 139-145 ◽  
Author(s):  
I. Mahderekal ◽  
C. K. Halford ◽  
R. F. Boehm
Keyword(s):  
Numerical Model ◽  
Conversion Efficiency ◽  
Cooling System ◽  
Photovoltaic System ◽  
Weather Data ◽  
Entire System ◽  
Simulation And Optimization ◽  
Size Number ◽  
Input Parameters ◽  
Fan Speed

Reported here is the development, using results of analysis and experiments, and optimization of a numerical model for a concentrated photovoltaic system. Models for the two major components of the system (cooling system and receiver) are developed separately from one another and then linked to simulate the performance for the entire system. The model is linked to yearly weather data and the optimization routines included in MATLAB are then used to select the input parameters (pump size, number of radiators, fan speed, etc.) which maximize the solar to electrical conversion efficiency of the system.

A Monte Carlo based formalism to identify potential locations at high risk of tumor recurrence with a numerical model for glioblastoma multiforme

2012 ◽  
Vol 39 (11) ◽  
pp. 6682-6691 ◽  
Author(s):  
Pier-Yves Trépanier ◽  
Israël Fortin ◽  
Carole Lambert ◽  
Frédéric Lacroix
Keyword(s):  
Monte Carlo ◽  
Glioblastoma Multiforme ◽  
High Risk ◽  
Numerical Model ◽  
Tumor Recurrence

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 Development of a meteorological forecast for snow accumulation on transmission lines

1993 ◽  
Vol 18 ◽  
pp. 107-112
Author(s):  
Tatsuhito Ito ◽  
Masaru Yamaoka ◽  
Hisayuki Ohura ◽  
Takashi Taniguchi ◽  
Gorow Wakahama
Keyword(s):  
Air Temperature ◽  
Transmission Lines ◽  
Wind Direction ◽  
Mesoscale Model ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Surface Winds ◽  
Temperature And Precipitation ◽  
Wet Snow

In Hokkaido we have often experienced hazardous accidents, such as tower collapses and conductor breakage, caused by wet-snow accretion on transmission lines, and over many years have developed countermeasures for wet-snow accretion. Recently we have been developing a system to forecast areas where snow accretion may occur. We used the southern part of Hokkaido, divided into 5 km × 5 km meshes, as a forecast area; our predictions were hourly, 3–24 hours in advance. A method of predicting meteorological data which forms an important part of the system predicts three elements which influence wet-snow accretion: air temperature, precipitation, and wind direction and speed. We used an interpolation for predicting temperature and precipitation and a one-level, mesoscale model for diagnosing surface winds for wind direction and speed. By applying the method to many examples of wet-snow accretion, we checked the prediction of weather elements.

scholarly journals A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting

1992 ◽  
Vol 38 (128) ◽  
pp. 13-22 ◽  
Author(s):  
E. Brun ◽  
P. David ◽  
M. Sudul ◽  
G. Brunot
Keyword(s):  
Numerical Model ◽  
Snow Cover ◽  
Winter Season ◽  
Weather Conditions ◽  
Internal Layers ◽  
Avalanche Forecasting ◽  
Snow Metamorphism ◽  
Wet Snow ◽  
Made In ◽  
Snow Samples

AbstractLaws of snow metamorphism have been introduced in a numerical model which simulates the evolution of temperature, density and liquid-water profiles of snow cover as a function of weather conditions.To establish these laws, the authors have summarized previous studies on temperature gradient and on wet-snow metamorphism and they have also conducted metamorphism experiments on dry or wet fresh-snow samples. An original formalism was developed to allow a description of snow with parameters evolving continuously throughout time.The introduction of laws of metamorphism has improved significantly the derivation of the settlement of internal layers and of snow-covered albedo, which depend on the simulated stratigraphy, i.e. the type and size of snow grains of different layers of the snow cover.The model was tested during a whole winter season without any re-initialization. Comparison between the simulated characteristics of the snow cover and the observations made in the field are described in detail. The model proved itself to be very efficient in simulating accurately the evolution of the snow-cover stratigraphy throughout the whole winter season.

scholarly journals High-risk regions and outbreak modelling of tularemia in humans

2016 ◽  
Vol 145 (3) ◽  
pp. 482-490 ◽  
Author(s):  
A. DESVARS-LARRIVE ◽  
X. LIU ◽  
M. HJERTQVIST ◽  
A. SJÖSTEDT ◽  
A. JOHANSSON ◽  
...  
Keyword(s):  
High Risk ◽  
Disease Surveillance ◽  
Negative Binomial ◽  
Warning System ◽  
Negative Binomial Regression ◽  
Weather Data ◽  
Annual Number ◽  
Mosquito Abundance ◽  
Annual Variations ◽  
Populations At Risk

SUMMARYSweden reports large and variable numbers of human tularemia cases, but the high-risk regions are anecdotally defined and factors explaining annual variations are poorly understood. Here, high-risk regions were identified by spatial cluster analysis on disease surveillance data for 1984–2012. Negative binomial regression with five previously validated predictors (including predicted mosquito abundance and predictors based on local weather data) was used to model the annual number of tularemia cases within the high-risk regions. Seven high-risk regions were identified with annual incidences of 3·8–44 cases/100 000 inhabitants, accounting for 56·4% of the tularemia cases but only 9·3% of Sweden's population. For all high-risk regions, most cases occurred between July and September. The regression models explained the annual variation of tularemia cases within most high-risk regions and discriminated between years with and without outbreaks. In conclusion, tularemia in Sweden is concentrated in a few high-risk regions and shows high annual and seasonal variations. We present reproducible methods for identifying tularemia high-risk regions and modelling tularemia cases within these regions. The results may help health authorities to target populations at risk and lay the foundation for developing an early warning system for outbreaks.

scholarly journals Study on Wind Simulations Using Deep Learning Techniques during Typhoons: A Case Study of Northern Taiwan

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 684 ◽  
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Numerical Model ◽  
Wind Velocity ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Global Analysis ◽  
Wrf Model ◽  
Weather Data ◽  
Northern Taiwan

A scheme for wind-speed simulation during typhoons in Taiwan is highly desirable, considering the effects of the powerful winds accompanying the severe typhoons. The developed combination of deep learning (DL) algorithms with a weather-forecasting numerical model can be used to determine wind speed in a rapid simulation process. Here, the Weather Research and Forecasting (WRF) numerical model was employed as the numerical simulation-based model for precomputing solutions to determine the wind velocity at arbitrary positions where the wind cannot be measured. The deep neural network (DNN) was used for constructing the DL-based wind-velocity simulation model. The experimental area of Northern Taiwan was used for the simulation. Regarding the complex typhoon system, the collected data comprised the typhoon tracks, FNL (Final) Operational Global Analysis Data for the WRF model, typhoon characteristics, and ground weather data. This study included 47 typhoon events that occurred over 2000–2017. Three measures were used to analyze the models for identifying optimal performance levels: Mean absolute error, root mean squared error, and correlation coefficient. This study compared observations with the WRF numerical model and DNN model. The results revealed that (1) simulations by using the WRF-based models were satisfactorily consistent with the observed data and (2) simulations by using the DNN model were considerably consistent with those of the WRF-based model. Consequently, the proposed DNN combined with WRF model can be effectively used in simulations of wind velocity at arbitrary positions of study area.

Sign in / Sign up

Export Citation Format

Share Document