摘要
以南广(南宁—广州)铁路西江大桥风环境监测子系统实测风场数据为研究对象,选择2次强良态风样本,运用经验模态分解法(EMD)和小波分析对强风样本进行非平稳分析,建立基于EMD分解和小波分析的非平稳风速模型,并将其与传统平稳风速模型计算结果对比分析。研究研究表明:西江大桥桥位处良态风非平稳特征明显;采用平稳风模型计算的紊流强度、阵风因子、积分尺度均比相应非平稳风模型计算的大;对于脉动风概率密度分布,非平稳风速模型比平稳模型更符合正态分布假定;2种风速模型计算的顺风向功率谱密度在低频段吻合较好,而在高频段非平稳风模型功率谱密度计算结果小于平稳风模型计算结果;Karman谱在高频段与2种计算模型功率谱密度结果较吻合,但在低频段存在较大差异。
Based on wind environment monitoring system of Nanning--Guangzhou railway of Xijiang bridge and taking its measured wind field data as the research object, two strong wind samples were chosen. The empirical mode decomposition (EMD) and wavelet analysis methods were applied to analyze nonstationary wind characteristics for these two samples. The nonstationary wind speed models were established by EMD and wavelet analysis methods. Moreover, comparisons were made between traditional wind speed model and nonstationary models. The results show that nonstationary characteristics at Xijiang bridge site are significant and the calculated values of turbulence intensify, gust factor, integral scales by using traditional wind speed model are larger than those by nonstationary models. With respect to probability density distribution of pulsating wind speed, nonstationary models fit well with the assumption of normal distribution in comparison to stationary model. The longitudinal power spectral density is in good agreement at low frequencies by using these two kinds of models, while the calculation values of nonstationary model is less than those of stationary model at high frequencies. Karman spectrum is in good agreement with the results of nonstationary and stationary models, but there exists bigger difference at low frequency.
出处
《中南大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2017年第5期1352-1359,共8页
Journal of Central South University:Science and Technology
基金
国家自然科学基金资助项目(51178471
51322808)
教育部新世纪优秀人才支持计划项目(NCET-12-0550)
长江学者和创新团队发展计划项目(IRT1296)
中国博士后科学基金资助项目(2014M562133)~~
关键词
高速铁路
钢箱提篮拱桥
非平稳风速模型
经验模态分解法
小波分析
风特性
high-speed railway
steel box X-style arch bridge
non-stationary wind speed model
empirical mode decomposition(EMD)
wavelet analysis
wind characteristic