• Wind and Structures
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• 제20권5호
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• pp.701-718
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• 2015

The joint distribution of wind speed and wind direction at a bridge site is vital to the estimation of the basic wind speed, and hence to the wind-induced vibration analysis of long-span bridges. Instead of the conventional way relying on the weather stations, this study proposed an alternate approach to obtain the original records of wind speed and the corresponding directions based on field measurement supported by the Structural Health Monitoring System (SHMS). Specifically, SHMS of Sutong Cable-stayed Bridge (SCB) is utilized to study the basic wind speed with directional information. Four anemometers are installed in the SHMS of SCB: upstream and downstream of the main deck center, top of the north and south tower respectively. Using the recorded wind data from SHMS, the joint distribution of wind speed and direction is investigated based on statistical methods, and then the basic wind speeds in 10-year and 100-year recurrence intervals at these four key positions are calculated. Analytical results verify the reliability of the recorded wind data from SHMS, and indicate that the joint probability model for the extreme wind speed at SCB site fits well with the Weibull model. It is shown that the calculated basic wind speed is reduced by considering the influence of wind direction. Compared to the design basic wind speed in the Specification of China, basic wind speed considering the influence of direction or not is much smaller, indicating a high safety coefficient in the design of SCB. The results obtained in this study can provide not only references for further wind-resistance research of SCB, but also improve the understanding of the safety coefficient for wind-resistance design of other engineering structures in the similar area.

• Smart Structures and Systems
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• 제21권5호
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.809-824
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• 2017

The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectation maximization (EM) algorithm-based angular-linear approach for probabilistic modeling of field-measured wind characteristics. The proposed method has been applied to model the wind speed and direction data during typhoons recorded by the structural health monitoring (SHM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. In the summer of 2015, three typhoons, i.e., Typhoon Chan-hom, Typhoon Soudelor and Typhoon Goni, made landfall in the east of China and then struck the Jiubao Bridge. By analyzing the wind monitoring data such as the wind speed and direction measured by three anemometers during typhoons, the wind characteristics during typhoons are derived, including the average wind speed and direction, turbulence intensity, gust factor, turbulence integral scale, and power spectral density (PSD). An EM algorithm-based angular-linear modeling approach is proposed for modeling the joint distribution of the wind speed and direction. For the marginal distribution of the wind speed, the finite mixture of two-parameter Weibull distribution is employed, and the finite mixture of von Mises distribution is used to represent the wind direction. The parameters of each distribution model are estimated by use of the EM algorithm, and the optimal model is determined by the values of $R^2$ statistic and the Akaike's information criterion (AIC). The results indicate that the stochastic properties of the wind field around the bridge site during typhoons are effectively characterized by the proposed EM algorithm-based angular-linear modeling approach. The formulated joint distribution of the wind speed and direction can serve as a solid foundation for the purpose of accurately evaluating the typhoon-induced fatigue damage of long-span bridges.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2003년도 추계학술발표회
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• pp.163-166
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• 2003

The seasonal mean wind direction and wind speed in a greater coasting area are investigated using the ECMWF(European Centre for Medium-Range Weather Forecasts) data for 11 years from 1985 to 1995. In winter, the main wind direction in Korea and vicinity, Taiwan and vicinity, and the North Pacific Ocean of middle latitudes is a northwesterly wind, northeasterly wind, and westerly wind respectively. The wind speed is strongest in the East China Sea, the South China Sea, and the North Pacific Ocean of low latitudes(Beaufort wind scale 5-6). A distribution pattern of wind direction in spring and fall is similar to that in winter. Seasonal mean wind speed is strongest in winter and the next is fall. The wind speed in summer is generally weak. However, that in the Indochina and vicinity is strong by the influence of Asian monsoon.

• 대한토목학회논문집
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• 제28권2A호
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• pp.197-205
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• 2008

본 연구에서는 광양-묘도 지역의 평균풍속을 추정하기 위하여, 일별 최대 풍속과 해당 방향에 대한 결합분포확률의 통계학적 해석에 극한값 확률분포 모델이 사용되었다. 이를 위하여, 교량 가설지점 인근의 기상관측소에 대한 일별 최대풍속 및 해당풍향의 데이터로부터 각각의 관측소에 대한 일별 최대기록의 빈도를 조사하였으며, 16방위 및 전방위에 대한 년 최대풍속의 표본을 추출하였다. 이러한 풍속기록은 Gumbel 및 Weibull 분포모델에 적용하였으며, 모멘트방법 및 최소제곱법 등을 통해 모수를 추정하였다. 또한, PPCC 검사를 통해 분포모델 및 모수의 적합 여부를 검사하였다. 적합 여부가 판단된 모수로부터, 해당 관측소별로 데이터의 표본 크기 및 교량 가설지점으로부터의 거리에 대한 요소를 고려하여 16방위 및 전방위에 대한 년 최대풍속을 추정하였다.

• 대기
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• 제18권3호
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• pp.171-182
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• 2008

In this study, we analyzed the spatial and temporal distribution of wind resources over Korea based on hourly observational data recorded over a period of 5 years from 457 stations belonging to Korea Meteorological Administration (KMA). The surface and 850 hPa wind data obtained from the Korea Local Analysis and Prediction System (KLAPS) and the Regional Data Assimilation and Prediction System (RDAPS) over a period of 1 year are used as supplementary data sources. Wind speed is generally high over seashores, mountains, and islands. In 62 (13.5%) stations, mean wind speeds for 5 years are greater than $3ms^{-1}$. The effects of seasonal wind, land-sea breeze, and mountain-valley winds on wind resources over Korea are evaluated as follows: First, wind is weak during summer, particularly over the Sobaek Mountains. However, over the coastal region of the Gyeongnam-province, strong southwesterly winds are observed during summer owing to monsoon currents. Second, the wind speed decreases during night-time, particularly over the west coast, where the direction of the land breeze is opposite to that of the large-scale westerlies. Third, winds are not always strong over seashores and highly elevated areas. The wind speed is weaker over the seashore of the Gyeonggi-province than over the other seashores. High wind speed has been observed only at 5 stations out of the 22 high-altitude stations. Detailed information on the wind resources conditions at the 21 stations (15 inland stations and 6 island stations) with high wind speed in Korea, such as the mean wind speed, frequency of wind speed available (WSA) for electricity generation, shape and scale parameters of Weibull distribution, constancy of wind direction, and wind power density (WPD), have also been provided. Among total stations in Korea, the best possible wind resources for electricity generation are available at Gosan in Jeju Island (mean wind speed: $7.77ms^{-1}$, WSA: 92.6%, WPD: $683.9Wm^{-2}$) and at Mt. Gudeok in Busan (mean wind speed: $5.66ms^{-1}$, WSA: 91.0%, WPD: $215.7Wm^{-2}$).

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• 해양환경안전학회지
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• 제12권3호
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• pp.185-192
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• 2006

풍향 풍속 분포는 해파의 형성 및 발달과 밀접히 관련되어 있어 선박의 안전 운항에 있어서 매우 중요하다. 이 연구에서는 11년간(1985-1995년)의 ECMWF(유럽중기예보센터) 객관해석 자료를 이용하여 항행구역상 근해구역에서의 기후학적인 바람분포 특성을 월별로 조사, 분석하였다. 한후기인 10월에서 3월까지의 풍향분포는 거의 비슷하며, 1월은 풍속이 가장 강하다 북위 30도 이북의 북서 내지 서북서풍과 대만해협 및 남중국해의 북동풍은 지속적이고 매우 강한 특성을 보인다. 6-8월의 풍향분포는 거의 유사하며 남중국해에서의 남서 내지 남남서풍은 강하고, 남반구에서는 강한 남동무역풍이 존재한다 4월, 5월 및 9월은 전반적으로 약한 풍속분포를 보인다

• 동아시아경상학회지
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• 제3권3호
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• pp.14-26
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• 2015

A wind speed forecast is a crucial and sophisticated task in a wind farm for planning turbines and corresponds to an estimate of the expected production of one or more wind turbines in the near future. By production is often meant available power for wind farm considered (with units KW or MW depending on both the wind speed and direction. Such forecasts can also be expressed in terms of energy, by integrating power production over each time interval. In this study, we technically focused on mathematical modeling of wind speed and direction forecast based on locally data set gathered from Aghdasiyeh station in Tehran. The methodology is set on using most common techniques derived from literature review. Hence we applied the most sophisticated forecasting methods to embed seasonality, trend, and irregular pattern for wind speed as an angular variables. Through this research, we carried out the most common techniques such as the Box and Jenkins family, VARMA, the component method, the Weibull function and the Fourier series. Finally, the best fit for each forecasting method validated statistically based on white noise properties and the final comparisons using residual standard errors and mean absolute deviation from real data.

• Wind and Structures
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• 제38권4호
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• pp.277-293
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• 2024

The recorded thunderstorm winds at a point contain tri-directional components. The probabilistic characteristics of such recorded winds in terms of instantaneous mean wind speed and direction, and the probability distribution and the time-frequency dependent crossed and non-crossed power spectral density functions for the high-frequency fluctuating wind components are unclear. In the present study, we analyze the recorded tri-directional thunderstorm wind components by separating the recorded winds in terms of low-frequency time-varying mean wind speed and high-frequency fluctuating wind components in the alongwind direction and two orthogonal crosswind directions. We determine the time-varying mean wind speed and direction defined by azimuth and elevation angles, and analyze the spectra of high-frequency wind components in three orthogonal directions using continuous wavelet transforms. Additionally, we evaluate the coherence between each pair of fluctuating winds. Based on the analysis results, we develop empirical spectral models and lagged coherence models for the tri-directional fluctuating wind components, and we indicate that the fluctuating wind components can be treated as Gaussian. We show how they can be used to generate time histories of the tri-directional thunderstorm winds.