|
|
Prediction of Short-term Strong Wind along the High-speed Railways |
Wang Yilin1, Li Zhenshan1,2, Zeng Qiulan2 |
1. Ministry of Education Key laboratory of Water and Sediment Sciences/Department of Environmental Engineering, Peking University, Beijing 100871, China; 2. School of Environment and Energy, Peking University, Shenzhen 518055, Guangdong, China |
|
|
Abstract Short-term strong wind prediction is essential for the secure operation of high-speed trains. The overturning accidents can be easily caused when the vibration frequency of the running trains meets the frequency of crosswind, then with which a simplified model was established on the condition of resonance frequency. The results show that the typical overturning time for high-speed trains is 10 s. To investigate the model responses of the vibration of incoming flow and the topographic factors in prediction, two standard models (railway behind the mountain and railway located among three mountains) were established, in which the waveform function is used to simulate the wind. The simulation results show that the Lattice-Boltzmann simulation method could reflect the changes in the flow field along the high-speed railway accurately and could also reflect the influence of topographic factors on the prediction. So the method might be an effective way to solve the forecast of strong wind along the high-speed railway in the future, and its worthy further investigation.
|
Received: 08 May 2013
Published: 20 May 2014
|
|
Corresponding Authors:
李振山(Email:lizhenshan@pku.edu.cn)
E-mail: lizhenshan@pku.edu.cn
|
[1] 丁明,张立军,吴义纯.基于时间序列分析的风电场风速预测模型[J].电力自动化设备,2005,25(8):32-34. [2] 马晓洁,张春来,张加琼,等.包兰铁路沙坡头段防护体系前沿栅栏沙丘形态与近地面流场[J].中国沙漠,2013,33(3):649-654. [3] 马国忠,张广兴,马玉芬.颠覆列车强风数值模式参数敏感性对比分析[J].中国沙漠,2010,30(6):1458-1463. [4] 马玉芬,赵玲,赵勇.一次强天气过程天山地形方案的敏感性试验研究[J].中国沙漠,2012,32(4):1127-1134. [5] 曾秋兰,李振山,卢傅安,等.高速公路透风型挡风墙不同位置防风特性的数值模拟研究[J].中国沙漠,2012,32(6):1542-1550. [6] 刘新婷,修春波,张欣,等.基于混沌不稳定周期方法的风速时间序列预测[J].东南大学学报(自然科学版),2012,42(S1):78-81. [7] Sfetsos A.A novel approach for the forecasting of mean hourly wind speed time series[J]. Renewable Energy,2002,27(2):163-174. [8] Moura M D,Zio E,Lins I D,et al.Failure and reliability prediction by support vector machines regression of time series data[J].Reliability Engineering & System Safety,2011,96(11):1527-1534. [9] 吕涛,唐巍,所丽.基于混沌相空间重构理论的风电场短期风速预测[J].电力系统保护与控制,2010,38(21):113-117. [10] Mohandes M A,Rehman S,Halawani T O.A neural networks approach for wind speed prediction[J]. Renewable Energy,1998,13(3):345-354. [11] Li G,Shi J.On comparing three artificial neural networks for wind speed forecasting[J]. Applied Energy,2010,87(7):2313-2320. [12] Cao Q,Ewing B T,Thompson M A.Forecasting wind speed with recurrent neural networks[J].European Journal of Operational Research,2012,221(1):148-154. [13] 潘迪夫,刘辉,李燕飞.基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型[J].电网技术,2008,32(7):82-86. [14] Louka P,Galanis G,Siebert N,et al.Improvements in wind speed forecasts for wind power prediction purposes using kalman filtering[J].Journal of Wind Engineering and Industrial Aerodynamics,2008,96(12):2348-2362. [15] Liu H,Tian H Q,Li Y F.Comparison of two new arima-ann and arima-kalman hybrid methods for wind speed prediction[J].Applied Energy,2012,98:415-424. [16] 周松林,茆美琴,苏建徽.基于小波分析与支持向量机的风速预测[J].太阳能学报,2012,33(3):452-456. [17] 刘辉.铁路沿线风信号智能预测算法研究[D].长沙:中南大学,2011. [18] 刘辉,潘迪夫,李燕飞.基于列车运行安全的青藏铁路大风预测优化模型与算法[J].武汉理工大学学报(交通科学与工程版),2008,32(6):986-989. [19] 常晓东,常晓磊.基于专家系统和神经网络的高铁风灾预警算法[J].实验技术与管理,2010,27(3):97-99. [20] 郝鲁波.客车模态计算与试验研究[D].大连:大连理工大学,2005. [21] 马静,张杰,杨志刚.横风下高速列车非定常空气动力特性研究[J].铁道学报,2008,30(6):109-114. [22] 王修琼,崔剑峰.Davenport谱中系数K的计算公式及其工程应用[J].同济大学学报(自然科学版),2002,30(7):849-852. [23] 关华,朱忠义,牟在根.利用Davenport谱求解荷载风振系数[C]//第九届全国现代结构工程学术研讨会论文集.济南,2009:593-597. [24] 李鹏飞.脉动风特性及其对桥梁主梁断面的抖振作用研究[D].上海:同济大学,2007. [25] 姚应峰.横风作用下200 km/h动车组安全性研究[D].成都:西南交通大学,2008. [26] Chen Y,Teng S L,Shukuwa T,et al.Lattice-Boltzmann simulation of two-phase fluid flows[J].International Journal of Modern Physics C:Computational Physics and Physical Computation,1998,9(8):1383-1391. [27] 王洋.基于格子玻尔兹曼方法的高速列车气动性能数值计算[D].北京:北京大学,2008. |
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|