Modeling Nonstationary Non-Gaussian Hurricane Wind Velocity and Gust Factor

2022 ◽  
Vol 148 (2) ◽  
Author(s):  
M. Y. Xiao ◽  
H. P. Hong
Keyword(s):  
Wind Velocity ◽  
Gust Factor ◽  
Hurricane Wind ◽  
Non Gaussian

scholarly journals Rare Weather Phenomena and the Work of Large-Format Roof Coverings

2019 ◽  
Vol 29 (3) ◽  
pp. 123-133 ◽  
Author(s):  
Barbara Ksit ◽  
Anna Szymczak-Graczyk
Keyword(s):  
Wind Velocity ◽  
Large Scale ◽  
Wind Load ◽  
Unique Combination ◽  
Large Format ◽  
Cover Layer ◽  
Wind Action ◽  
Hurricane Wind ◽  
Fundamental Value ◽  
Variable Wind

Abstract Wind action belongs to loads that are environmentally variable. Wind action is included in the basic combination of loads, whereas hurricane wind action is classified as a unique combination. Due to large gusts of wind, the roof coverings of large-scale buildings are exposed to detachment of their cover layer. The article presents the effects of over-normative wind, which occurred on January 19, 2018 and was named the cyclone Frederic/David. The purpose of the article is to show that in design of roof coverings made of large-format materials, such as membranes, it is crucial to accept wind load values properly in order to ensure a right spacing between fasteners. The presented results might be an important contribution to the debate on the necessity for increasing the fundamental value of the basic wind velocity in the perimeter and corner zones of large-format roofs.

Field measurement of near-surface typhoon characteristics using a smart monitoring system on a long-span arch bridge site

2019 ◽  
Vol 22 (8) ◽  
pp. 1977-1987
Author(s):  
Xu Wang ◽  
Huaqiang Li ◽  
Zengshun Chen ◽  
Yuanhao Qian ◽  
Yanru Wang ◽  
...  
Keyword(s):  
Monitoring System ◽  
Wind Velocity ◽  
Turbulence Intensity ◽  
Eastern China ◽  
Mean Flow ◽  
Time Duration ◽  
Lateral Direction ◽  
Near Surface ◽  
Gust Factor ◽  
The Mean

During landfall of Typhoon Haikui in Eastern China in 2012, ground level wind data were recorded using a smart monitoring system installed on JiuBao Bridge in Hangzhou, China. This article documents the mean flow and turbulence characteristics from data recorded during the storm. The results show that both turbulence intensity and gust factor decrease with the increase in the mean wind velocity. However, as the mean wind velocity increases, this trend gradually attenuates. The peak factor distribution with gust averaging time duration derived with the Typhoon Haikui data agrees well with the Durst curve. However, the longitudinal gust factor derived from the typhoon wind-speed record in this study is higher compared with the curves proposed by Durst and Krayer-Marshall. Analyses of the gust factor distribution with the turbulence intensity during the passage of the storm reveal a similarity to the empirical curves of Ishizaki and Choi. Results show that the relationship between lateral turbulence and gust factors can be well represented by a quadratic polynomial. Turbulence scale increased with mean wind velocity. The values of autocorrelation coefficients in longitudinal direction are larger than those in lateral direction. There has no obvious dependency of cross-correlation coefficients with mean wind velocity. In general, the wind characteristics in this study are shown to be very similar to those of winds under normal circumstance.

Boundary Layer Wind Characteristics over Urban Area

2014 ◽  
Vol 501-504 ◽  
pp. 2297-2300
Author(s):  
Lun Hai Zhi
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Urban Area ◽  
Atmospheric Turbulence ◽  
Wind Velocity ◽  
Three Dimensional ◽  
Tall Buildings ◽  
Gust Factor ◽  
Mean Wind Speed ◽  
Wind Characteristics

This paper presents statistical analysis results of wind speed and atmospheric turbulence data measured from a meteorological station in Beijing and is primarily intended to provide useful information on boundary layer wind characteristics for wind-resistant design of tall buildings and high-rise structures. Wind velocity data in longitudinal, lateral and vertical directions, which were recorded from an ultrasonic anemometer during windstorms, are analyzed and discussed. Atmospheric turbulence information such as turbulence intensity, gust factor, turbulence integral length scale and power spectral densities of the three-dimensional fluctuating wind velocity are presented and used to evaluate the adequacy of existing theoretical and empirical models. The objective of this study is to investigate the profiles of mean wind speed and atmospheric turbulence characteristics over a typical urban area.

scholarly journals Study on Typhoon Characteristic Based on Bridge Health Monitoring System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Xu Wang ◽  
Bin Chen ◽  
Dezhang Sun ◽  
Yinqiang Wu
Keyword(s):  
Wind Speed ◽  
Monitoring System ◽  
Wind Velocity ◽  
Gaussian Distribution ◽  
Full Range ◽  
Integral Scale ◽  
Gust Factor ◽  
Wind Speeds ◽  
Bridge Health Monitoring ◽  
Mean Wind Speed

Through the wind velocity and direction monitoring system installed on Jiubao Bridge of Qiantang River, Hangzhou city, Zhejiang province, China, a full range of wind velocity and direction data was collected during typhoon HAIKUI in 2012. Based on these data, it was found that, at higher observed elevation, turbulence intensity is lower, and the variation tendency of longitudinal and lateral turbulence intensities with mean wind speeds is basically the same. Gust factor goes higher with increasing mean wind speed, and the change rate obviously decreases as wind speed goes down and an inconspicuous increase occurs when wind speed is high. The change of peak factor is inconspicuous with increasing time and mean wind speed. The probability density function (PDF) of fluctuating wind speed follows Gaussian distribution. Turbulence integral scale increases with mean wind speed, and its PDF does not follow Gaussian distribution. The power spectrum of observation fluctuating velocity is in accordance with Von Karman spectrum.

Simulation of wind velocity time histories on long span structures modeled as non-Gaussian stochastic waves

2020 ◽  
Vol 59 ◽  
pp. 103016
Author(s):  
Haijun Zhou ◽  
George Deodatis ◽  
Michael Shields ◽  
Brett Benowitz
Keyword(s):  
Wind Velocity ◽  
Long Span ◽  
Time Histories ◽  
Stochastic Waves ◽  
Non Gaussian

Predicting tree damage in fragmented landscapes using a wind risk model coupled with an airflow model

2015 ◽  
Vol 45 (8) ◽  
pp. 1065-1076 ◽  
Author(s):  
Sylvain Dupont ◽  
Veli-Pekka Ikonen ◽  
Hannu Väisänen ◽  
Heli Peltola
Keyword(s):  
Wind Velocity ◽  
Risk Model ◽  
Wind Flow ◽  
Risk Models ◽  
Tree Damage ◽  
Gust Factor ◽  
Wind Speeds ◽  
Heterogeneous Landscapes ◽  
The Impact ◽  
Airflow Model

Forest mechanistic wind risk models are widely applied on heterogeneous landscapes, whereas their wind load parameterizations are often derived either from homogeneous stand conditions or from simple forest edge conditions. To evaluate the impact of improving the wind flow representation of the mechanistic wind risk model HWIND on tree damage predictions when applied on heterogeneous environments, we coupled HWIND with the airflow model Aquilon. Aquilon provides to HWIND the velocity profiles and the gust factor (deduced from an approach based on the probability distribution of the wind velocity and on the turbulent kinetic energy). HWIND–Aquilon is compared with HWIND alone on different stand configurations of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) comprising newly clearcuts or shelter stands. Although both models showed the same pattern of differences in edge-tree critical wind speeds with differences in clear-cut length and shelter stand height, the model comparison reveals significant differences in the magnitude of critical wind speeds between them. This discrepancy is explained by the wind velocity and gust factor parameterizations used in HWIND alone, as in other wind risk models that exhibit weaknesses in heterogeneous configurations. This result confirms the need for improving the wind flow representation in mechanistic wind risk models when applied to heterogeneous landscapes.

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