Wind characteristics along a bridge catwalk in a deep-cutting gorge from field measurements
This paper investigates the wind characteristics along the bridge catwalk during construction in a deep-cutting gorge. Five sets of anemometers are installed in the catwalk to collect field measurements. The mean wind characteristics, including wind speed and wind direction, are first investigated....
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Published in | Journal of wind engineering and industrial aerodynamics Vol. 186; pp. 94 - 104 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.03.2019
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Subjects | |
Online Access | Get full text |
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Summary: | This paper investigates the wind characteristics along the bridge catwalk during construction in a deep-cutting gorge. Five sets of anemometers are installed in the catwalk to collect field measurements. The mean wind characteristics, including wind speed and wind direction, are first investigated. The influence of local topography on the mean wind characteristics is identified. Moreover, it is also found that the recorded wind data obtained from the deep-cutting gorge have strong nonstationary characteristics. Based on the traditional stationary wind speed model and a nonstationary wind speed model, turbulence wind characteristics, such as turbulence intensity, spectral analysis and turbulence length scale, are comprehensively analyzed and compared. The effect of local topography on the turbulence wind characteristics is also examined. Additionally, the change laws of turbulence intensity and turbulence length scale along the bridge catwalk are derived. The estimated auto-spectral density functions of all three turbulence components are in a good agreement with the Von Kármán forms.
•Five sets of anemometers are installed to collect field measurements.•Both mean and turbulence wind characteristics are analyzed.•Influence of the local topography on the wind characteristics is identified.•Wind data have strong nonstationary characteristics in such deep-cutting gorge. |
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ISSN: | 0167-6105 1872-8197 |
DOI: | 10.1016/j.jweia.2018.12.022 |