• Wind and Structures
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• v.19 no.5
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• pp.467-480
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• 2014

The estimation of wind speed values used in codes and standards is an integral part of the wind load evaluation process. In a number of codes and standards, wind speeds outside of tropical cyclone prone regions are estimated using a single probability distribution developed from observed wind speed data, with no distinction made between the types of causal wind hazard (e.g., thunderstorm). Non-tropical cyclone wind hazards (i.e., thunderstorm, non-thunderstorm) have been shown to possess different probability distributions and estimation of non-tropical cyclone wind speeds based on a single probability distribution has been shown to underestimate wind speeds. Current treatment of non-tropical cyclone wind hazards in worldwide codes and standards is touched upon in this work. Meteorological data is available at a considerable number of United States (U.S.) stations that have information on wind speed as well as the type of causal wind hazard. In this paper, probability distributions are fit to distinct storm types (i.e., thunderstorm and non-thunderstorm) and the results of these distributions are compared to fitting a single probability distribution to all data regardless of storm type (i.e., co-mingled). Distributions fitted to data separated by storm type and co-mingled data will also be compared to a derived (i.e., "mixed") probability distribution considering multiple storm types independently. This paper will analyze two extreme value distributions (e.g., Gumbel, generalized Pareto). It is shown that mixed probability distribution, on average, is a more conservative measure for extreme wind speed estimation. Using a mixed distribution is especially conservative in situations where a given wind speed value for either storm type has a similar probability of occurrence, and/or when a less frequent storm type produces the highest overall wind speeds. U.S. areas prone to multiple non-tropical cyclone wind hazards are identified.

• Wind and Structures
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• v.31 no.4
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• pp.299-309
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• 2020

In this study, wind data such as speeds, loads and potential of Muğla which is located in the southwest of Turkey were statistically analyzed. The wind data which consists of hourly wind speed between 2010 and 2013 years, was measured at the 10-meters height in four different ground stations (Datça, Fethiye, Marmaris, Köyceğiz). These stations are operated by The Turkish State Meteorological Service (T.S.M.S). Furthermore, wind data was analyzed by using Log-Normal and Gamma distributions, since these distributions fit better than Weibull, Normal, Exponential and Logistic distributions. Root Mean Squared Error (RMSE) and the coefficients of the goodness of fit (R²) were also determined by using statistical analysis. According to the results, extreme wind speed in the research area was 33 m/s at the Datça station. The effective wind load at this speed is 0.68 kN/㎡. The highest mean power densities for Datça, Fethiye, Marmaris and Köyceğiz were found to be 46.2, 1.6, 6.5 and 2.2 W/㎡, respectively. Also, although Log-normal distribution exhibited a good performance i.e., lower AD (Anderson - Darling statistic (AD) values) values, Gamma distribution was found more suitable in the estimation of wind speed and power of the region.

• Water for future
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• v.19 no.3
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• pp.239-248
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• 1986

Monthly wind stress, wind stress curl and volume transport stream functions are computed in the Eastern Sea(Japan Sea) based upon observed wind and atmospheric pressure data respectively. The presented two results show different distributios on locality and season but as common features the results reveal the northwesterly surface wind stress \ulcorner 새 the monsoon in winter, south to southwesterly wind stress \ulcorner 새 the southerly wind in summer and strond anticyclonic curl in the northern part on the Eastern Sea(Japan Sea) in winter. In the distributions obtained from the sea level atmospheric pressure data, the maximum value of the wind stress and of curls of small scales are shown off the southeast coast of Siberia and northeast coast of Korea. Volume transport distributions obtained from the Sverdrup relationship suggest that the strong northward boundary current can be formed along the northeast coast of Korea in winter and weak southward boundary current in summer.

• Journal of Environmental Science International
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• v.19 no.6
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• pp.689-701
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• 2010

The effects of high-resolution wind profiler (HWP) data on the wind distributions were evaluated in two different coastal areas during the study period (23-26 August, 2007), indicating weak-gradient flows. The analysis was performed using the Weather Research and Forecasting (WRF) model coupled with a three-dimensional variational (3DVAR) data assimilation system. For the comparison purpose, two coastal regions were selected as: a southwestern coastal (SWC) region characterized by a complex shoreline and a eastern coastal (EC) region surrounding a simple coastline and high mountains. The influence of data assimilation using the HWP data on the wind distributions in the SWC region was moderately higher than that of the EC region. In comparison between the wind speed and direction in the two coastal areas, the application of the HWP data contributed to improvement of the wind direction distribution in the SWC region and the wind strength in the EC region, respectively. This study suggests that the application of the HWP data exerts a large impact on the change in wind distributions over the sea and thus can contribute to the solution to lack of satellite and buoy data with their observational uncertainty.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.245-251
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• 2004

This paper discuss the conditionally sampled actual wind pressure distributions causing peak quasi-static wind loads in the large span roofs using the wind pressures at many locations on dome models measured simultaneously in a wind tunnel. The actual extreme pressure distributions are compared itk load-response-correlation (LRC) method and the quasi-steady pressure distributions. Based on the results, the reason for the discrepancy in the LRC pressure distribution and the actual extreme pressure distribution are discussed. Futhermore, a brief discussion is made of the equivalent static wind load estimation for the large span roofs.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.6
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• pp.27-35
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• 2008

This study aims to estimate the optimal probability distribution of daily electricity generation by wind power, in order to contribute in rural green-village planning. Wind power generation is now being recognized as one of the most popular sources for renewable resources over the country. Although it is also being adapted to rural area for may reasons, it is important to estimate the magnitudes of power outputs with reliable statistical methodologies while applying historical daily wind data, for correct feasibility analysis. In this study, one of the well-known statistical methodology is employed to define the appropriate statistical distributions for monthly power outputs for specific rural areas. The results imply that the assumption of normal distributions for many cases may lead to incorrect decision-making and therefore lead to the unreliable feasibility analysis. Subjective methodology for testing goodness of fit for normal distributions on all the cases in this study, provides possibilities to consider the other various types of statistical distributions for more precise feasibility analysis.

• Wind and Structures
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• v.3 no.4
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• pp.279-289
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• 2000

The main purpose of this paper is to demonstrate the necessity of considering wind load combinations even for low-rise buildings. It first discusses the overall quasi-static wind load effects and their combinations to be considered in structural design of low-rise buildings. It was found that the maximum torsional moment closely correlates with the maximum along-wind base shear. It was also found that the instantaneous pressure distribution causing the maximum along-wind base shear was quite similar to that causing the maximum torsional moment, and that this asymmetric pressure pattern simultaneously accompanies considerable across-wind and torsional components. Secondly, the actual wind pressure distributions causing maximum quasi-static internal forces in the structural frames are conditionally sampled and their typical pressure patterns are presented.

• Wind and Structures
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• v.21 no.2
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• pp.183-202
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• 2015

Experiments on two-dimensional rectangular prisms with various side ratios (B/D=2, 3, and 4, where B is the along-wind dimension, and D is the across-wind dimension) and rounded corners (R/D=0%, 5%, 10%, and 15%, where R is the corner radius) are reported in this study. The tests were conducted in low-turbulence uniform flow to measure the wind pressures on the surfaces of 12 models for Reynolds numbers ranging from $1.1{\times}10^5$ to $6.8{\times}10^5$. The aerodynamic force coefficients were obtained by integrating the wind pressure coefficients around the model surface. Experimental results of wind pressure distributions, aerodynamic force coefficients, and Strouhal numbers are presented for the 12 models. The mechanisms of the Reynolds number effects are revealed by analyzing the variations of wind pressure distributions. The sensitivity of aerodynamic behavior to the Reynolds number increases with increasing side ratio or rounded corner ratio for rectangular prisms. In addition, the variations of the mean pressure distributions and the pressure correlations on the side surfaces of rectangular prisms with the rounded corner ratio are analyzed at $Re=3.4{\times}10^5$.

• Wind and Structures
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• v.33 no.6
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• pp.423-435
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• 2021

Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.

• Journal of Bio-Environment Control
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• v.4 no.1
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• pp.1-8
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• 1995

One of the most destructive forces around greenhouses is wind. Wind loads can be obtained by multiplying velocity pressure by dimensionless wind force coefficient. Generally, wind force coefficients can be determined by wind tunnel experiments. The wind force coefficient distribution on a single - span arched greenhouse was estimated using experimental data and compared with reported values from various countries. The results obtained are as follows : 1. The coefficients obtained from this study agree with the values proposed by G. L. Nelson except about 0.5 of difference in the middle region of roof section. This discrepancy is mainly attributed to the dissimilarity of experimental conditions (or wind tunnel test such as Reynolds number, type of terrain, surface roughness of model, location of the lapping and measuring methods. 2. Considering that the wind force coefficients are varied along the height of a wall at wind direction perpendicular to wall, structural analysis using subdivided wind force coefficient distribution is more resonable for wall. 3. It is recommendable that wind force coefficient distribution on a roof should take more subdivision than the existing four equal divisions for more accurate structural design. 4. Structural design using wind forces close to real values is more advantageous in safety and expense.