• Wind and Structures
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• v.39 no.1
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• pp.15-30
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• 2024

In this paper, detailed wind field data of the full path of typhoon "Bailu" were obtained based on site measurements. Typhoon "Bailu" made first landfall southeast of the Taiwan Strait with a wind speed of approximately 30 m/s near the center of the typhoon eye and a second landfall in Dongshang County in Fujian Province. The moving process is classified into 3 regions for analysis and comparison. Detailed analyses of wind characteristics including wind profile, turbulence intensity, gust factor, turbulence integral scale and wind power spectral density function at the full process of the typhoon are conducted, and the findings are presented in this paper. Wind speed shows significant dependence on both the direction of the moving path and the distance between the typhoon center and measurement site. Wind characteristics significantly vary with the moving path of the typhoon center. The relationship between turbulence intensity and gust factor at different regions is investigated. The integral turbulence scales and wind speed are fitted by a Gaussian model. Such analysis and conclusions may provide guidance for future bridge wind-resistant design in engineering applications.

• Journal of Environmental Science International
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• v.27 no.3
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• pp.203-217
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• 2018

For this study, WRF numerical modeling was performed, using RDAPS information for input data on typhoons affecting the Korean peninsula to produce wind data of 700hPa. RAM numerical modeling was also used to calculate 3-second gusts as the extreme wind speed. After comparing wind speeds at an altitude of 10 m to evaluate the feasibility of WRF numerical modeling, modeled values were found to be similar with measured ones, reflecting change tendencies well. Therefore, the WRF numerical modeling results were verified. As a result of comparing and analyzing these wind speeds, as calculated through RAM numerical modeling, to evaluate applicability for disaster preparedness, change tendencies were observed to be similar between modeled and measured values. In particular, modeled values were slightly higher than measured ones, indicating applicability for the prevention of possible damage due to gales. Our analysis of 3-second gusts during the study period showed a high distribution of 3-second gusts in the southeast region of the Korean peninsula from 2002-2006. The frequency of 3-second gusts increased in the central north region of Korea as time progressed. Our analysis on the characteristics of 3-second gusts during years characterized by El $Ni{\tilde{n}}o$ or La Nina showed greater strength during hurricanes that affected the Korean peninsula in El $Ni{\tilde{n}}o$ years.

• Wind and Structures
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• v.15 no.2
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• pp.177-188
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• 2012

The meteorological events that cause most strong winds in Brazil are extra-tropical cyclones, downbursts and tornadoes. However, one hurricane formed off the coastline of southern Brazil in 2005, a tropical storm formed in 2010 and there are predictions that others may form again. Events such as those described in the paper and which have occurred before 1987, generate data for the wind map presented in the Brazilian wind loading code NBR-6123. This wind map presents the reference wind speeds based on 3-second gust wind speed at 10 m height in open terrain, with 50-year return period, varying from 30 m/s (north half of country) to 50 m/s (extreme south). There is not a separation of the type of climatological event which generated each registered velocity. Therefore, a thunderstorm (TS), an extra-tropical pressure system (EPS) or even a tropical cyclone (TC) are treated the same and its resulting velocities absorbed without differentiation. Since the flow fields generated by each type of meteorological event may be distinct, the indiscriminate combination of the highest wind velocities with aerodynamic coefficients from boundary layer wind tunnels may lead to erroneous loading in buildings.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.3
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• pp.349-359
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• 2016

In order to analytically evaluate buffeting responses, the analysis of wind characteristics such as turbulence intensity, turbulence length, gust, roughness coefficient, etc must be a priority. Static aerodynamic force coefficients, flutter coefficients, structural damping ratios, aerodynamic damping ratios and natural frequencies affect the analytical responses. The bridge interested in this paper has being been used for 32 years. As the time passes, current terrain conditions around the bridge are different markedly from the conditions it was built 32 years ago. Also, wind environments were considerably varied by the climate change. For this reason, it is necessary to evaluate the turbulence intensity, length, spectrum and roughness coefficient of the bridge site from full-scale measurements using the structural health monitoring system. The evaluation results indicate that wind characteristics of bridge site is analogous to that of open terrain although the bridge is located on the coastal area. To calculate buffeting responses, the analysis variables such as damping ratios, static aerodynamic force coefficients and natural frequency were evaluated from measured data. The analysis was performed with regard to 4 cases. The evaluated variables from measured data are applied to the first and second analysis cases. And the other analysis cases were performed based on Design Guidelines for Steel Cable Supported Bridges. The calculated responses of each analysis cases are compared with the buffeting response measured at less than 25m/s wind speed. It is verified that the responses by the numerical analysis applying the estimated variables based on full-scale measurements are well agreed with the measured actual buffeting responses under wind speed 25m/s. Also, the extreme wind speed corresponding to a recurrence interval 200 years is derived from Gumbel distribution. The derived wind speed for return period of 200 years is 45m/s. Therefore the buffeting responses at wind speed 45m/s is determined by the analysis applying the estimated variables.