Basis for recommending an update of wind velocity pressures in Canadian design codes

2014 ◽  
Vol 41 (3) ◽  
pp. 206-221 ◽  
Author(s):  
H.P. Hong ◽  
T.G. Mara ◽  
R. Morris ◽  
S.H. Li ◽  
W. Ye
Keyword(s):  
Wind Speed ◽  
Wind Velocity ◽  
Return Period ◽  
Least Squares Method ◽  
Gumbel Distribution ◽  
Generalized Least Squares ◽  
Generalized Extreme Value Distribution ◽  
Design Codes ◽  
Distribution Fitting ◽  
Wind Speeds

Reference wind velocity pressures corresponding to specified return period wind speeds are provided in several Canadian design codes. A review of the two most recent editions of the National Building Code of Canada (NBCC) indicates that significant changes in some 50-year return period wind speeds, vAH-50, were introduced in the 2010 version of the NBCC-2010 compared to the previous NBCC-2005. The changes are due to analysis approaches, available wind records, and a re-examination of anemometer histories. To potentially improve the estimates of vAH-50, wind records in the Environment Canada HLY01 digital archive were processed. Two hundred and thirty-five meteorological stations are considered in the analysis, and height and exposure corrected annual maximum hourly-mean wind speed, VAH, are extracted. Statistical analysis and distribution fitting were carried out using the Gumbel distribution and generalized extreme value distribution and several fitting methods were employed. The results indicate that it is preferable to treat VAH as a Gumbel variate, and to carry out the fit using the generalized least-squares method. Wind speed contour maps for Canada are developed based on the estimated vAH-T for T equal to 50, 500, and 1000 years. A comparison of the maps of vAH-50 to those inferred from NBCC-2005 and NBCC-2010 shows that the developed map retains some of the smoothness of the wind speeds exhibited in NBCC-2010, while maintains the localized wind speed features presented in NBCC-2005. Results also show that the wind speed corresponding to the factored design wind load in NBCC-2010 is associated with a return period ranging from 200 to 5000 years, but for 90% of stations considered, the range narrows to 300 to 900 years.

scholarly journals A Nonstationary Extreme Value Analysis for the Assessment of Changes in Extreme Annual Wind Speed over the Gulf of St. Lawrence, Canada

2008 ◽  
Vol 47 (11) ◽  
pp. 2745-2759 ◽  
Author(s):  
Y. Hundecha ◽  
A. St-Hilaire ◽  
T. B. M. J. Ouarda ◽  
S. El Adlouni ◽  
P. Gachon
Keyword(s):  
Wind Speed ◽  
Gumbel Distribution ◽  
Reanalysis Data ◽  
Extreme Value ◽  
Significant Trend ◽  
Generalized Extreme Value Distribution ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Significance Level ◽  
Wind Speeds

Abstract Changes in the extreme annual wind speed in and around the Gulf of St. Lawrence (Canada) were investigated through a nonstationary extreme value analysis of the annual maximum 10-m wind speed obtained from the North American Regional Reanalysis (NARR) dataset as well as observed data from selected stations of Environment Canada. A generalized extreme value distribution with time-dependent location and scale parameters was used to estimate quantiles of interest as functions of time at locations where significant trend was detected. A Bayesian method, the generalized maximum likelihood approach, is implemented to estimate the parameters. The analysis yielded shape parameters very close to 0, suggesting that the distribution can be modeled using the Gumbel distribution. A similar analysis using a nonstationary Gumbel model yielded similar quantiles with narrower credibility intervals. Overall, little change was detected over the period 1979–2004. Only 7% of the investigated grids exhibited trends at the 5% significant level, and the analysis performed on the reanalysis data at locations of significant trend indicated a rise in the median extreme annual wind speed by up to 2 m s−1 per decade in the southern coastal areas with a corresponding increase in the 90% and 99% quantiles of the extreme annual wind speeds by up to 5 m s−1 per decade. Also in the northern part of the gulf and some offshore areas in the south, the 50%, 90%, and 99% quantile values of the extreme annual wind speeds are noted to drop by up to 1.5, 3, and 5 m s−1, respectively. While the directions of the changes in the annual extremes at the selected stations are similar to those of the reanalysis data at nearby grid cells, the magnitudes and significance levels of the changes are generally inconsistent. Change at the same significance level over the same period of the NARR dataset was noted only at 2 stations out of 13.

scholarly journals Design Wind Speed Estimation for Long Span Bridges in Korean Southern and Western Coasts

2020 ◽  
Vol 10 (2) ◽  
pp. 146-155
Author(s):  
Dooyong Cho
Keyword(s):  
Wind Speed ◽  
Least Squares ◽  
Return Period ◽  
Least Squares Method ◽  
Wind Load ◽  
Suspension Bridges ◽  
Method Of Moment ◽  
Long Span Bridges ◽  
Wind Speeds ◽  
Long Span

Recently, many long-span cable supported bridges, including the cable stayed bridges and the suspension bridges, have already been constructed or are planned for construction. Because the meteorological values used to estimate the wind load for designing the long-span bridges were based on data from the 1960s through 1995 in Korea, it is necessary to reconsider the proper design wind load for long-span bridges. In this paper, the research area is confined to the southern and western coasts of Korea where many long-span bridges have been built. The method of moment and the least-squares method are used to estimate the expected wind speeds of a 100-year return period for girder bridges; Gumbel’s distribution is used to estimate the expected wind speeds of a 200-year return period for long-span bridges. As the return period wind speed on the land surface is revised because of recent high-speed velocity, the revised return period wind speed is increased by 17%. The compatibility of return period wind speed is also evaluated using the RMS (root mean square) error method. This paper concludes that the least-squares method is more compatible than the method of moment for the case of the southern and western coasts of Korea.

scholarly journals Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms

2019 ◽  
Vol 11 (3) ◽  
pp. 665 ◽  
Author(s):  
Lingzhi Wang ◽  
Jun Liu ◽  
Fucai Qian
Keyword(s):  
Wind Speed ◽  
Frequency Distribution ◽  
Least Squares Method ◽  
Wind Farms ◽  
Rayleigh Distribution ◽  
Distribution Model ◽  
Exponential Polynomial ◽  
Distribution Models ◽  
Wind Speed Data ◽  
Wind Speeds

This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an exponential polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The exponential polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model.

scholarly journals A Basic Wind Speed Map for Oman

2014 ◽  
Vol 11 (2) ◽  
pp. 64
Author(s):  
A.S. Alnuaimi ◽  
M.A. Mohsin ◽  
K.H. Al-Riyami
Keyword(s):  
Wind Speed ◽  
Return Period ◽  
Structural Design ◽  
Short Term ◽  
Wind Speed Data ◽  
Wind Speeds ◽  
Mean Wind Speed

The aim of this research was to develop the first basic wind speed map for Oman. Hourly mean wind speed records from 40 metrological stations were used in the calculation. The period of continuous records ranged from 4–37 years. The maximum monthly hourly mean and the maxima annual hourly mean wind speed data were analysed using the Gumbel and Gringorten methods. Both methods gave close results in determining basic wind speeds, with the Gumbel method giving slightly higher values. Due to a lack of long-term records in some regions of Oman, basic wind speeds were extrapolated for some stations with only short-term records, which were defined as those with only 4– 8 years of continuous records; in these cases, monthly maxima were used to predict the long-term basic wind speeds. Accordingly, a basic wind speed map was developed for a 50-year return period. This map was based on basic wind speeds calculated from actual annual maxima records of 29 stations with at least 9 continuous years of records as well as predicted annual maxima wind speeds for 11 short-term record stations. The basic wind speed values ranged from 16 meters/second (m/s) to 31 m/s. The basic wind speed map developed in this research is recommended for use as a guide for structural design in Oman. 

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.

Measuring Sub-second Wind Velocity Changes for Agricultural Drift One Meter Above the Ground

2019 ◽  
Vol 35 (5) ◽  
pp. 697-704
Author(s):  
Matthew W. Schramm ◽  
H Mark Hanna ◽  
Matt J. Darr ◽  
Steven J. Hoff ◽  
Brian L. Steward
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Wind Velocity ◽  
Wind Direction ◽  
Weather Conditions ◽  
Spray Drift ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind ◽  
Velocity Changes

Abstract. Agricultural spray drift is affected by many factors including current weather conditions, topography of the surrounding area, fluid properties at the nozzle, and the height at which the spray is released. During the late spring/summer spray seasons of 2014 and 2015, wind direction, speed, and solar radiation (2014 only) were measured at 10 Hz, 1 m above the ground to investigate conditions that are typically encountered by a droplet when released from a nozzle on an agricultural sprayer. Measurements of wind velocity as the wind passed from an upwind sensor to a downwind sensor were used to evaluate what conditions wind may be most likely to have a significant direction or speed change which affects droplet trajectory. For two individual datasets in which the average wind speed was 3.6 and 1.5 m/s (8.0 and 3.4 mi/h), there exists little linear correlation of wind speed or wind direction between an upwind and downwind anemometer separated by 30.5 m (100 ft). The highest observed correlation, resulting from a 12-s lag between the upwind and downwind datasets, was 0.29 when the average wind speed was 3.6 m/s (8.0 mi/h). Correlations greater than 0.1 were only found for wind speeds exceeding 3 m/s. Using this lag time, it was observed that the wind direction 30 s into the future had a 30% chance to be different by more than 20° from current conditions. A wind speed difference of more than 1 m/s (2.2 mi/h) from current conditions [mean wind speed was 3.6 m/s (8.0 mi/h)] was observed about 50% of the time. Analyzing 36 days of the 2014 and 2015 spray season wind velocity data showed that the most variability in wind direction occurred with wind speeds below 2 m/s (4.5 mi/h). Greater wind direction variability occurred in the mid-afternoon with higher solar radiation. Keywords: Sprayers, Spray drift, Spray droplets, Turbulence, Wind effects.

Anemometry

2001 ◽  
Author(s):  
Fred V. Brock ◽  
Scott J. Richardson
Keyword(s):  
Wind Speed ◽  
Wind Velocity ◽  
Elevation Angle ◽  
Vertical Component ◽  
Wind Vector ◽  
Wind Speeds ◽  
North East ◽  
Orthogonal Components ◽  
The Mean ◽  
Design Type

The function of an anemometer (sometimes with a wind vane) is to measure some or all components of the wind velocity vector. It is common to express the wind as a two-dimensional horizontal vector since the vertical component of the wind speed is usually small near the earth’s surface. In some cases, the vertical component is important and then we think of the wind vector as being three-dimensional. The vector can be written as orthogonal components (u, v, and sometimes w] where each component is the wind speed component blowing in the North, East, or vertically up direction. Alternatively, the vector can be written as a speed and a direction. In the horizontal case, the wind direction is the direction from which the wind is blowing measured in degrees clockwise from North. The wind vector can be expressed in three dimensions as the speed, direction in the horizontal plane as above, and the elevation angle. Standard units for wind speed (a scalar component of the velocity) are m s-1 and knots (nautical miles per hour). Some conversion factors are shown in table 7-1. Wind velocity is turbulent; that is, it is subject to variations in speed, direction, and period. The wind vector can be described in terms of mean flow and gustiness or variation about the mean. The WMO standard defines the mean as the average over 10 minutes. The ideal wind-measuring instrument would respond to the slightest breeze yet be rugged enough to withstand hurricane-force winds, respond to rapidly changing turbulent fluctuations, have a linear output, and exhibit simple dynamic performance characteristics. It is difficult to build sensors that will continue to respond to wind speeds as they approach zero or will survive as wind speeds become very large. Thus a variety of wind sensor designs and, even within a design type, a spectrum of implementations have evolved to meet our needs.

scholarly journals Can Satellite Sampling of Offshore Wind Speeds Realistically Represent Wind Speed Distributions? Part II: Quantifying Uncertainties Associated with Distribution Fitting Methods

2004 ◽  
Vol 43 (5) ◽  
pp. 739-750 ◽  
Author(s):  
S. C. Pryor ◽  
M. Nielsen ◽  
R. J. Barthelmie ◽  
J. Mann
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Remotely Sensed ◽  
Offshore Wind ◽  
Data Series ◽  
Parameter Estimates ◽  
Distribution Fitting ◽  
Wind Speeds ◽  
Distribution Parameters ◽  
Independent Observations

Abstract Remote sensing tools represent an attractive proposition for measuring wind speeds over the oceans because, in principle, they also offer a mechanism for determining the spatial variability of flow. Presented here is the continuation of research focused on the uncertainties and biases currently present in these data and quantification of the number of independent observations (scenes) required to characterize various parameters of the probability distribution of wind speeds. Theoretical and empirical estimates are derived of the critical number of independent observations (wind speeds derived from analysis of remotely sensed scenes) required to obtain probability distribution parameters with an uncertainty of ±10% and a confidence level of 90% under the assumption of independent samples, and it is found that approximately 250 independent observations are required to fit the Weibull distribution parameters. Also presented is an evaluation of Weibull fitting methods and determination of the fitting method based on the first and third moments to exhibit the “best” performance for pure Weibull distributions. Further examined is the ability to generalize parameter uncertainty bounds presented previously by Barthelmie and Pryor for distribution parameter estimates from sparse datasets; these were found to be robust and hence generally applicable to remotely sensed wind speed data series.

Design of a Rooftop Wind Harnessing System With Smart Wind Deflecting Structure

2016 ◽  
Author(s):  
Majid Rashidi ◽  
J. R. Kadambi ◽  
Scott Suren
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Velocity ◽  
Wind Turbines ◽  
High Wind ◽  
Turbine Generator ◽  
Wind Speeds ◽  
Pass Through ◽  
Novel Design ◽  
Natural Wind

This work presents a novel design for a rooftop wind tower system; the system is primarily intended for areas of low natural wind speeds. The relatively low natural wind is increased by a factor of about 1.6 times via a wind deflecting structure to reach the cut-in speed of typical rooftop wind turbines which is approximately 3 m/s. The system additionally responds to relatively high wind velocity, above 20 m/s, in a way that eliminates its wind speed amplification attributes; this will protect the wind turbines against exposure to high wind speeds that could be harmful to the turbines and to the mast structure that supports the turbine/generator. In case of high natural wind speed, above 20 m/s, passively controlled trap-doors, that are parts of the wind deflecting structure, allow wind to pass through the wind deflecting structure, thereby eliminating wind speed amplification of the deflecting structure. The design disclosed in this work comprises of a half cylinder wind deflecting structure that includes a plurality of spring loaded trap-doors; when closed, they form the wind deflecting structure up to a prescribed maximum natural wind speed. As the natural wind speed increases beyond its prescribed maximum, the spring loaded trap doors open as the result of the pressure exerted upon them by the wind. The design presented in this work increases the range of cut-in and cut-out wind speeds for a typical rooftop wind turbine.

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