The sea-level pressure (SLP), 500 hPa height, zonal-mean 500 hPa height ([Z(500)]), stationary wave eddy component of the 500 hPa height (Z(500)*) and zonal-mean 500 hPa geostrophic wind [U-g] fields poleward of 20 de...The sea-level pressure (SLP), 500 hPa height, zonal-mean 500 hPa height ([Z(500)]), stationary wave eddy component of the 500 hPa height (Z(500)*) and zonal-mean 500 hPa geostrophic wind [U-g] fields poleward of 20 degreesN are examined for the period 1958-1997, with emphasis on the winter season. The relationships between the Arctic Oscillation (AO)index and algebraic difference of the zonal-mean wind in 55 degreesN and 35 degreesN (Ut) index were investigated, making use the Monte Carlo procedure, Singular Value Decomposition (SVD), Empirical orthogonal function (EOF) and regression method. The leading modes of empirical orthogonal function (EOF's) of SLP are more robust than the 500 hPa height EOF's. not only in the ratio of the two largest eigenvalues, but in more zonally symmetric. Comparing the meridional profiles of zonal-mean wind amplitude associated with the AO and Ut index, the profiles for the two indexes are very similar, both with respect to amplitude and the placement of the maximum and minimum. Comparing the station wave component of 500 hPa height field regressed upon the AO and Ut index. there is one-to-one correspondence between all the major centers of action in the two maps, especially in the North Atlantic and Eurasian continent. The pattern is unlike the prominent teleconnection patterns, they have hemispheric extent and cannot be interpreted in term of the individual wavetrains.展开更多
Daily observations of wind speed at 12 stations in the Greater Beijing Area during 1960–2008 were homogenized using the Multiple Analysis of Series for Homogenization method. The linear trends in the regional mean an...Daily observations of wind speed at 12 stations in the Greater Beijing Area during 1960–2008 were homogenized using the Multiple Analysis of Series for Homogenization method. The linear trends in the regional mean annual and seasonal （winter, spring, summer and autumn） wind speed series were-0.26,-0.39,-0.30,-0.12 and-0.22 m s-1 （10 yr）-1 , respectively. Winter showed the greatest magnitude in declining wind speed, followed by spring, autumn and summer. The annual and seasonal frequencies of wind speed extremes （days） also decreased, more prominently for winter than for the other seasons. The declining trends in wind speed and extremes were formed mainly by some rapid declines during the 1970s and 1980s. The maximum declining trend in wind speed occurred at Chaoyang （CY）, a station within the central business district （CBD） of Beijing with the highest level of urbanization. The declining trends were in general smaller in magnitude away from the city center, except for the winter case in which the maximum declining trend shifted northeastward to rural Miyun （MY）. The influence of urbanization on the annual wind speed was estimated to be about-0.05 m s-1 （10 yr）-1 during 1960–2008, accounting for around one fifth of the regional mean declining trend. The annual and seasonal geostrophic wind speeds around Beijing, based on daily mean sea level pressure （MSLP） from the ERA-40 reanalysis dataset, also exhibited decreasing trends, coincident with the results from site observations. A comparative analysis of the MSLP fields between 1966–1975 and 1992–2001 suggested that the influences of both the winter and summer monsoons on Beijing were weaker in the more recent of the two decades. It is suggested that the bulk of wind in Beijing is influenced considerably by urbanization, while changes in strong winds or wind speed extremes are prone to large-scale climate change in the region.展开更多
To analyze wind-induced response characteristics of a wind turbine tower more accurately, the blade-tower coupling effect was investigated. The mean wind velocity of the rotating blades and tower was simulated accordi...To analyze wind-induced response characteristics of a wind turbine tower more accurately, the blade-tower coupling effect was investigated. The mean wind velocity of the rotating blades and tower was simulated according to wind shear effects, and the fluctuating wind velocity time series of the wind turbine were simulated by a harmony superposition method. A dynamic finite element method (FEM) was used to calculate the wind-induced response of the blades and tower. Wind-induced responses of the tower were calculated in two cases (one included the blade-tower coupling effect, and the other only added the mass of blades and the hub at the top of the tower), and then the maximal displacements at the top of the tower of the tow cases were compared with each other. As a result of the influence of the blade-tower coupling effect and the total base shear of the blades, the maximal displacement of the first case increased nearly by 300% compared to the second case. To obtain more precise analysis, the blade-tower coupling effect and the total base shear of the blades should be considered simultaneously in the design of wind turbine towers.展开更多
In the present study, wind speed data of Jumla, Nepal have been statistically analyzed. For this purpose, the daily averaged wind speed data for 10 year period (2004-2014: 2012 excluded) provided by Department of Hydr...In the present study, wind speed data of Jumla, Nepal have been statistically analyzed. For this purpose, the daily averaged wind speed data for 10 year period (2004-2014: 2012 excluded) provided by Department of Hydrology and Meteorology (DHM) was analyzed to estimate wind power density. Wind speed as high as 18 m/s was recorded at height of 10 m. Annual mean wind speed was ascertained to be decreasing from 7.35 m/s in 2004 to 5.13 m/s in 2014 as a consequence of Global Climate Change. This is a subject of concern looking at government’s plan to harness wind energy. Monthly wind speed plot shows that the fastest wind speed is generally in month of June (Monsoon Season) and slowest in December/January (Winter Season). Results presented Weibull distribution to fit measured probability distribution better than the Rayleigh distribution for whole years in High altitude region of Nepal. Average value of wind power density based on mean and root mean cube seed approaches were 131.31 W/m<sup>2</sup>/year and 184.93 W/m<sup>2</sup>/year respectively indicating that Jumla stands in class III. Weibull distribution shows a good approximation for estimation of power density with maximum error of 3.68% when root mean cube speed is taken as reference.展开更多
This paper develops the modeling of wind speed by Weibull distribution in the intention to evaluate wind energy potential and help for designing small wind energy plant in Batouri in Cameroon. The Weibull distribution...This paper develops the modeling of wind speed by Weibull distribution in the intention to evaluate wind energy potential and help for designing small wind energy plant in Batouri in Cameroon. The Weibull distribution model was developed using wind speed data collected from a metrological station at the small Airport of Batouri. Four numerical methods (Moment method, Graphical method, Empirical method and Energy pattern factor method) were used to estimate weibull parameters K and C. The application of these four methods is effective using a sample wind speed data set. With some statistical analysis, a comparison of the accuracy of each method is also performed. The study helps to determine that Energy pattern factor method is the most effective (K = 3.8262 and C = 2.4659).展开更多
The characteristic wind curve （CWC） was com- monly used in the previous work to evaluate the operational safety of the high-speed trains exposed to crosswinds. How- ever, the CWC only provide the dividing line betwe...The characteristic wind curve （CWC） was com- monly used in the previous work to evaluate the operational safety of the high-speed trains exposed to crosswinds. How- ever, the CWC only provide the dividing line between safety state and failure state of high-speed trains, which can not evaluate the risk of derailment of high-speed trains when ex- posed to natural winds. In the present paper, a more realistic approach taking into account the stochastic characteristics of natural winds is proposed, which can give a reasonable and effective assessment of the operational safety of high-speed trains under stochastic winds. In this approach, the longitudi- nal and lateral components of stochastic winds are simulated based on the Cooper theory and harmonic superposition. An algorithm is set up for calculating the unsteady aerody- namic forces （moments） of the high-speed trains exposed to stochastic winds. A multi-body dynamic model of the rail vehicle is established to compute the vehicle system dynamic response subjected to the unsteady aerodynamic forces （mo- ments） input. Then the statistical method is used to get the mean characteristic wind curve （MCWC） and spread range of the high-speed trains exposed to stochastic winds. It is found that the CWC provided by the previous analyticalmethod produces over-conservative limits. The methodol- ogy proposed in the present paper can provide more signif- icant reference for the safety operation of high-speed trains exposed to stochastic winds.展开更多
针对K-means算法进行大跨屋盖结构表面风荷载分区中存在的分类数k值需凭经验事先给定以及所有初始聚类中心均需随机选取带来的分类情况数过多、从中寻找最优分类结果工作量大且效率低的问题,提出基于改进K-means算法的大跨屋盖结构表面...针对K-means算法进行大跨屋盖结构表面风荷载分区中存在的分类数k值需凭经验事先给定以及所有初始聚类中心均需随机选取带来的分类情况数过多、从中寻找最优分类结果工作量大且效率低的问题,提出基于改进K-means算法的大跨屋盖结构表面风荷载分区方法。首先,建立分类数k与其相应测点风荷载的误差平方和(Sum of the Squared Errors:SSE)关系曲线,引入手肘法基本思想,实现最优分类数kst值的精准识别;其次,在首个初始聚类中心随机选取基础上,引入轮盘法基本思想,完成对剩余初始聚类中心的高效选取;然后,根据类内紧凑、类间分散的原则,通过类内紧凑性判定指标S(k)和类间分散性判定指标D(k),构造并借助SD(k)值有效性检验,得到最优的风荷载分区结果;最后,以北京奥林匹克网球中心大跨悬挑屋盖结构为例,针对风洞试验所得风荷载测试结果,采用所提方法对其表面最不利风压系数进行分区计算,并与传统K-means算法进行对比,结果表明,所提方法能够高效实现大跨屋盖结构表面风压分区计算,具有较好的工程应用价值。展开更多
The wind-rain induced vibration phenomena in the Dongting Lake Bridge (DLB) can be observed every year, and the field measurements of wind speed data of the bridge are usually nonstationary. Nonstationary wind speed c...The wind-rain induced vibration phenomena in the Dongting Lake Bridge (DLB) can be observed every year, and the field measurements of wind speed data of the bridge are usually nonstationary. Nonstationary wind speed can be decomposed into a deterministic time-varying mean wind speed and a zero-mean stationary fluctuating wind speed component. By using wavelet transform (WT), the time-varying mean wind speed is extracted and a nonstationary wind speed model is proposed in this paper. The wind characteristics of turbulence intensity, integral scale and probability distribution of the bridge are calculated from the typical wind samples recorded by the two anemometers installed on the DLB using the proposed nonstationary wind speed model based on WT. The calculated results are compared with those calculated by the empirical mode decomposition (EMD) and traditional approaches. The compared results indicate that the wavelet-based nonstationary wind speed model is more reasonable and appropriate than the EMD-based nonstationary and traditional stationary models for characterizing wind speed in analysis of wind-rain-induced vibration of cables.展开更多
基金This work was supported by LASG, Institute of Atmospheric Physics, Chinese Academy of Sciencesin 2000 and the National Science
文摘The sea-level pressure (SLP), 500 hPa height, zonal-mean 500 hPa height ([Z(500)]), stationary wave eddy component of the 500 hPa height (Z(500)*) and zonal-mean 500 hPa geostrophic wind [U-g] fields poleward of 20 degreesN are examined for the period 1958-1997, with emphasis on the winter season. The relationships between the Arctic Oscillation (AO)index and algebraic difference of the zonal-mean wind in 55 degreesN and 35 degreesN (Ut) index were investigated, making use the Monte Carlo procedure, Singular Value Decomposition (SVD), Empirical orthogonal function (EOF) and regression method. The leading modes of empirical orthogonal function (EOF's) of SLP are more robust than the 500 hPa height EOF's. not only in the ratio of the two largest eigenvalues, but in more zonally symmetric. Comparing the meridional profiles of zonal-mean wind amplitude associated with the AO and Ut index, the profiles for the two indexes are very similar, both with respect to amplitude and the placement of the maximum and minimum. Comparing the station wave component of 500 hPa height field regressed upon the AO and Ut index. there is one-to-one correspondence between all the major centers of action in the two maps, especially in the North Atlantic and Eurasian continent. The pattern is unlike the prominent teleconnection patterns, they have hemispheric extent and cannot be interpreted in term of the individual wavetrains.
基金supported by grants from the MOST NBRPC(2009CB421401)CNNSF(41075063) and the CMA Institute of Urban Meteorology
文摘Daily observations of wind speed at 12 stations in the Greater Beijing Area during 1960–2008 were homogenized using the Multiple Analysis of Series for Homogenization method. The linear trends in the regional mean annual and seasonal （winter, spring, summer and autumn） wind speed series were-0.26,-0.39,-0.30,-0.12 and-0.22 m s-1 （10 yr）-1 , respectively. Winter showed the greatest magnitude in declining wind speed, followed by spring, autumn and summer. The annual and seasonal frequencies of wind speed extremes （days） also decreased, more prominently for winter than for the other seasons. The declining trends in wind speed and extremes were formed mainly by some rapid declines during the 1970s and 1980s. The maximum declining trend in wind speed occurred at Chaoyang （CY）, a station within the central business district （CBD） of Beijing with the highest level of urbanization. The declining trends were in general smaller in magnitude away from the city center, except for the winter case in which the maximum declining trend shifted northeastward to rural Miyun （MY）. The influence of urbanization on the annual wind speed was estimated to be about-0.05 m s-1 （10 yr）-1 during 1960–2008, accounting for around one fifth of the regional mean declining trend. The annual and seasonal geostrophic wind speeds around Beijing, based on daily mean sea level pressure （MSLP） from the ERA-40 reanalysis dataset, also exhibited decreasing trends, coincident with the results from site observations. A comparative analysis of the MSLP fields between 1966–1975 and 1992–2001 suggested that the influences of both the winter and summer monsoons on Beijing were weaker in the more recent of the two decades. It is suggested that the bulk of wind in Beijing is influenced considerably by urbanization, while changes in strong winds or wind speed extremes are prone to large-scale climate change in the region.
基金supported by the National Natural Science Foundation of China (No. 50708015)the Program for New Century Excellent Talents in University (No. NCET-06-0270), China
文摘To analyze wind-induced response characteristics of a wind turbine tower more accurately, the blade-tower coupling effect was investigated. The mean wind velocity of the rotating blades and tower was simulated according to wind shear effects, and the fluctuating wind velocity time series of the wind turbine were simulated by a harmony superposition method. A dynamic finite element method (FEM) was used to calculate the wind-induced response of the blades and tower. Wind-induced responses of the tower were calculated in two cases (one included the blade-tower coupling effect, and the other only added the mass of blades and the hub at the top of the tower), and then the maximal displacements at the top of the tower of the tow cases were compared with each other. As a result of the influence of the blade-tower coupling effect and the total base shear of the blades, the maximal displacement of the first case increased nearly by 300% compared to the second case. To obtain more precise analysis, the blade-tower coupling effect and the total base shear of the blades should be considered simultaneously in the design of wind turbine towers.
文摘In the present study, wind speed data of Jumla, Nepal have been statistically analyzed. For this purpose, the daily averaged wind speed data for 10 year period (2004-2014: 2012 excluded) provided by Department of Hydrology and Meteorology (DHM) was analyzed to estimate wind power density. Wind speed as high as 18 m/s was recorded at height of 10 m. Annual mean wind speed was ascertained to be decreasing from 7.35 m/s in 2004 to 5.13 m/s in 2014 as a consequence of Global Climate Change. This is a subject of concern looking at government’s plan to harness wind energy. Monthly wind speed plot shows that the fastest wind speed is generally in month of June (Monsoon Season) and slowest in December/January (Winter Season). Results presented Weibull distribution to fit measured probability distribution better than the Rayleigh distribution for whole years in High altitude region of Nepal. Average value of wind power density based on mean and root mean cube seed approaches were 131.31 W/m<sup>2</sup>/year and 184.93 W/m<sup>2</sup>/year respectively indicating that Jumla stands in class III. Weibull distribution shows a good approximation for estimation of power density with maximum error of 3.68% when root mean cube speed is taken as reference.
文摘This paper develops the modeling of wind speed by Weibull distribution in the intention to evaluate wind energy potential and help for designing small wind energy plant in Batouri in Cameroon. The Weibull distribution model was developed using wind speed data collected from a metrological station at the small Airport of Batouri. Four numerical methods (Moment method, Graphical method, Empirical method and Energy pattern factor method) were used to estimate weibull parameters K and C. The application of these four methods is effective using a sample wind speed data set. With some statistical analysis, a comparison of the accuracy of each method is also performed. The study helps to determine that Energy pattern factor method is the most effective (K = 3.8262 and C = 2.4659).
基金supported by the 2013 Doctoral Innovation Funds of Southwest Jiaotong University and the Fundamental Research Funds for the Central Universitiesthe High-speed Railway Basic Research Fund Key Project of China(U1234208)the National Natural Science Foundation of China(50823004)
文摘The characteristic wind curve （CWC） was com- monly used in the previous work to evaluate the operational safety of the high-speed trains exposed to crosswinds. How- ever, the CWC only provide the dividing line between safety state and failure state of high-speed trains, which can not evaluate the risk of derailment of high-speed trains when ex- posed to natural winds. In the present paper, a more realistic approach taking into account the stochastic characteristics of natural winds is proposed, which can give a reasonable and effective assessment of the operational safety of high-speed trains under stochastic winds. In this approach, the longitudi- nal and lateral components of stochastic winds are simulated based on the Cooper theory and harmonic superposition. An algorithm is set up for calculating the unsteady aerody- namic forces （moments） of the high-speed trains exposed to stochastic winds. A multi-body dynamic model of the rail vehicle is established to compute the vehicle system dynamic response subjected to the unsteady aerodynamic forces （mo- ments） input. Then the statistical method is used to get the mean characteristic wind curve （MCWC） and spread range of the high-speed trains exposed to stochastic winds. It is found that the CWC provided by the previous analyticalmethod produces over-conservative limits. The methodol- ogy proposed in the present paper can provide more signif- icant reference for the safety operation of high-speed trains exposed to stochastic winds.
文摘针对K-means算法进行大跨屋盖结构表面风荷载分区中存在的分类数k值需凭经验事先给定以及所有初始聚类中心均需随机选取带来的分类情况数过多、从中寻找最优分类结果工作量大且效率低的问题,提出基于改进K-means算法的大跨屋盖结构表面风荷载分区方法。首先,建立分类数k与其相应测点风荷载的误差平方和(Sum of the Squared Errors:SSE)关系曲线,引入手肘法基本思想,实现最优分类数kst值的精准识别;其次,在首个初始聚类中心随机选取基础上,引入轮盘法基本思想,完成对剩余初始聚类中心的高效选取;然后,根据类内紧凑、类间分散的原则,通过类内紧凑性判定指标S(k)和类间分散性判定指标D(k),构造并借助SD(k)值有效性检验,得到最优的风荷载分区结果;最后,以北京奥林匹克网球中心大跨悬挑屋盖结构为例,针对风洞试验所得风荷载测试结果,采用所提方法对其表面最不利风压系数进行分区计算,并与传统K-means算法进行对比,结果表明,所提方法能够高效实现大跨屋盖结构表面风压分区计算,具有较好的工程应用价值。
文摘The wind-rain induced vibration phenomena in the Dongting Lake Bridge (DLB) can be observed every year, and the field measurements of wind speed data of the bridge are usually nonstationary. Nonstationary wind speed can be decomposed into a deterministic time-varying mean wind speed and a zero-mean stationary fluctuating wind speed component. By using wavelet transform (WT), the time-varying mean wind speed is extracted and a nonstationary wind speed model is proposed in this paper. The wind characteristics of turbulence intensity, integral scale and probability distribution of the bridge are calculated from the typical wind samples recorded by the two anemometers installed on the DLB using the proposed nonstationary wind speed model based on WT. The calculated results are compared with those calculated by the empirical mode decomposition (EMD) and traditional approaches. The compared results indicate that the wavelet-based nonstationary wind speed model is more reasonable and appropriate than the EMD-based nonstationary and traditional stationary models for characterizing wind speed in analysis of wind-rain-induced vibration of cables.
