Analysis Of The Spatiotemporal Distribution Characteristics Of Wind Speed Along The High-speed Rail Line Based On The WT Mode Roughness Model Optimization

In recent years,China's high-speed rail has made tremendous development,and the mileage of high-speed rail continues to hit new highs;while rapid development is also facing a lot of pressure,the safe operation of high-speed rail is particularly important.Strong winds are one of the main meteorological disasters that endanger the safe operation of high-speed railways.In particular,the frequency of extreme windy weather has increased in recent years.It is crucial to carry out research on the temporal and spatial distribution of wind speeds along high-speed railways.On the one hand,through the analysis of the characteristics of the spatial and temporal distribution of wind speed along the high-speed rail,accurate perception of strong wind-prone areas and spatial correlation along the high-speed rail can be achieved,and it has a guiding role in the setting and optimization of meteorological(strong wind)monitoring points along the high-speed rail;On the one hand,the analysis of the temporal and spatial distribution characteristics of wind speed along the high-speed rail is helpful to capture the characteristics of wind speed and direction changes in different geographical environments,and provides a theoretical basis for the prediction and early warning of gale along the high-speed rail.However,the existing research on wind speed along the high-speed rail has a relatively large time-space scale,and there is a lack of detailed research on small-scale areas in the complex environment prone to high winds along the high-speed rail.In view of this,with the support of the key projects of China Railway Group and the major projects of China Railway Shanghai Bureau Group,this article takes the Yangcheng Lake section along the Beijing-Shanghai high-speed railway as an example;for the specific underlying surface environment,the fluid mechanics based on the optimized roughness model is developed.The WT model research realizes the digital simulation of the temporal and spatial distribution characteristics of wind speed in the Yangcheng Lake area along the Beijing-Shanghai high-speed railway;on this basis,the research on the layout optimization method of the strong wind monitoring station along the BeijingShanghai high-speed railway is carried out,and the related research content is visualized Design and implementation.The specific content is as follows:(1)The original roughness model in the traditional WT model is based on the empirical assignment of the roughness of each area according to the type of surface cover,and there is only one value representing the average annual roughness,and the simulation accuracy cannot meet the requirements of small-scale areas.In order to obtain a more accurate roughness model of the study area,this paper divides the study area coverage into three types: towns,water bodies,and farmland,and uses Weiting Town,Yangcheng East Lake and Xibang Village as representative points,and is provided by the Jiangsu Meteorological Bureau.Based on the wind speed and direction data,the logarithmic wind profile is fitted by the least square method,and the roughness values of the three types of coverings in different seasons are obtained,and the original roughness model is optimized.Furthermore,using the three wind measurement stations along the Yangcheng Lake section of the Beijing-Shanghai high-speed railway as reference stations,the WT model before and after the roughness model optimization is used to simulate the observed wind speed at the reference station respectively,and the two simulated wind speeds are compared with the actual observed wind speed.Corresponding to the three evaluation indicators of RMSE,MAE and R,the comparison results verify the effectiveness and accuracy of the optimized roughness model.(2)In order to study the temporal and spatial distribution characteristics of wind speed in the Yangcheng Lake area along the Beijing-Shanghai high-speed railway,23 simulation stations were divided at 1 km intervals along the Yangcheng Lake section of the BeijingShanghai high-speed railway,and the WT model optimized by the roughness model was used to analyze the 23 stations in the study section.The wind speed is simulated and calculated,and the simulated data is analyzed by mathematical statistics and empirical orthogonal decomposition to obtain the characteristics of wind conditions and the temporal and spatial distribution of maximum wind speed along the Yangcheng Lake section of the Beijing-Shanghai high-speed railway;On this basis,combined with the characteristics of the time and space distribution of wind speed in the Yangcheng Lake section along the Beijing-Shanghai highspeed railway,the high wind frequency of 23 simulated stations and the correlation coefficient between the stations were analyzed,and a K-means clustering algorithm-based method for the layout of wind measurement stations along the line was proposed.The case analysis verifies the effectiveness of the method and provides technical support for the optimization of the layout of gale monitoring stations along the high-speed rail.(3)Finally,on the basis of the large-scale system developed by the subject,corresponding functional modules have been developed according to the research content of this article;mainly include the meteorological(wind speed)data statistical analysis function module,the meteorological(wind speed)data quality control function module,and the Beijing-Shanghai high-speed rail Visualization function modules for the temporal and spatial distribution characteristics of wind speed in Yangcheng Lake section.Through the development of corresponding functional modules,the graphical and intuitive display of the temporal and spatial distribution characteristics of wind speed along the high-speed rail can be realized,which is beneficial for related researchers and high-speed rail workers to quickly perceive the temporal and spatial distribution characteristics of wind speed along the high-speed rail.