Development of a system for predicting snow distribution in built-up environments: Combining a mesoscale meteorological model and a CFD model

Tominaga, Yoshihide; Mochida, Akashi; Okaze, Tsubasa; Sato, Takeshi; Nemoto, Masaki; Motoyoshi, Hiroki; Nakai, S.; Tsutsumi, Takuya; Otsuki, Masaya; Uamatsu, Takahiko; Yokota, Hiroshi

doi:10.1016/j.jweia.2010.12.004

Cited by 52 publications

(26 citation statements)

References 20 publications

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“…Tachiiri et al (2008) evaluated the economic loss of snow disasters in arid inland pastoral areas of Mongolia using a tree-based regression model with parameters including livestock mortality rate of current year, grassland NDVI (normalized difference vegetation index), SWE (snow water equivalent), and livestock numbers and mortality rates of previous years. In addition, Tominaga et al (2011) predicted snow cover area and potential extent of snow disasters in built-up environments by combining a meteorological model and a computational fluid dynamics model. Nakai et al (2012) established a snow disaster early warning system using meteorological factors (i.e., precipitation, wind speed, temperature), which would predict avalanche potential, visibility in blowing snow, and snow conditions on roads.…”

Section: Introductionmentioning

confidence: 99%

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Wang

Liang

Huang

et al. 2013

Nat. Hazards Earth Syst. Sci.

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This study develops a model for early warning of snow-caused livestock disasters on a county basis and proposes a method of qualitative risk assessment of snow disasters at 500 m resolution for pastoral areas on the Tibetan Plateau (TP). Data used for the model development include remote sensing data, statistical data of weather, livestock, and social economy, and 45 typical snow disaster cases from 2000 to 2010. The principal component analysis (PCA) approach is used to choose 7 crucial factors that contribute over 85% of information for early warning snow disasters on the TP. They are mean annual probability of snow disaster, number of snow-covered days, livestock stocking rate, continual days of mean daily temperature below −10 °C, grassland burial index, rate of snow-covered grassland, and per livestock gross domestic product. The chosen 411 cases from 2008 to 2010 are used to validate the prediction results from the developed early warning model, with an overall accuracy of 85.64% in predicting snow disasters and no disasters. This suggests that the early warning approach developed in the study has operational potential for predicting snow disasters on the TP

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Section: Introductionmentioning

confidence: 99%

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Wang

Liang

Huang

et al. 2013

Nat. Hazards Earth Syst. Sci.

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“…Tominaga et al. 8 predicted the snow distribution and accumulation around buildings by CFD methods and the results were found to be in a good agreement with field test. Cao et al.…”

Section: Introductionmentioning

confidence: 75%

“…Tominaga et al. 13 had experimentally obtained that the snow particles' average density was 100 kg/m 3 . In this section, the snow particles were set to 100 kg/m 3 , the snow particles' diameter was set to 0.2 mm.…”

Section: Introductionmentioning

confidence: 99%

A numerical study of snow accumulation on the bogies of high-speed trains based on coupling improved delayed detached eddy simulation and discrete phase model

Liu

Wang

Zhu

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part F:

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A numerical simulation method based on the improved delayed detached eddy simulation coupled with a discrete phase model is used to study the influence of the snow on the performance of bogies of a high-speed train running in snowy weather. The snow particle trajectories, mass of snow packing on the bogie, and thickness of snow accumulation have been analyzed to investigate the flow mechanisms of snow accumulation on different parts of the bogies. The results show that the snow accumulation on the first bogie of the head vehicle is almost the same as that of the second bogie, but the total accumulated snow on the top side of the second bogie is more than 74% higher than that of the first bogie. Among all the components of the bogies, the motors were found to be strongly influenced by the snow accumulation. The underlying flow mechanisms responsible for the snow accumulations are discussed.

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“…The experimental results shown that the initial density range of snow particles is 36-132 kg/m 3 , and it was set to 100 kg/m 3 . Tominaga et al (2011a) adopted numerical simulation method to simulate the snow particles accumulation and erosion conditions, and the results were compared with the test that numerical simulation and the experimental results were in good agreement. The experiment shown that the snow particle density is 100 kg/m 3 .…”

Section: Fig 2 Bogies Covered With Snow and Icementioning

confidence: 99%

Study of Snow Accumulation on a High-Speed Train’s Bogies Based on the Discrete Phase Model

Xie¹,

Zhang²,

Gao³

et al. 2017

JAFM

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During winters, the high-speed train travels in the northern of China is struck by to the snow, ice and coldness, massive snow accumulating on the bogies. To understand the cause of snow packing on the highspeed train's bogies clearly, the 3-D unsteady Reynolds-averaged Navier-Stokes equations with a RNG double-equations turbulence model and a DPM discrete phase model were used to investigate the flow field carried snow particles in a single high-speed train bogie region and monitor the movement of snow particles. And, the numerical simulation was verified by the wind tunnel test. The results show that when air flows into the region, the airflow will rise and impact on the wheels, brakes, electromotors and other parts of bogie regions. The snow particles will follow the air, while the air direction changes sharply the particles will keep the movement due to the inertia. Afterwards, the snow packs on the bogie. In front of the bogies the streamlines of the air and the particle path lines are basically the same. However, due to the inertia of mass particles, the following characteristics of the snow particles with the air are not obvious in the bogie leeward side. Different structures of the end plates will affect the snow accumulation in the bogie regions.

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Development of a system for predicting snow distribution in built-up environments: Combining a mesoscale meteorological model and a CFD model

Cited by 52 publications

References 20 publications

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

A numerical study of snow accumulation on the bogies of high-speed trains based on coupling improved delayed detached eddy simulation and discrete phase model

Study of Snow Accumulation on a High-Speed Train’s Bogies Based on the Discrete Phase Model

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