CN102023317A - Method for deploying strong wind monitoring points on rapid transit railway - Google Patents

Abstract

The invention discloses a method for laying out strong wind monitoring points on a high-speed railway. The steps of the method include: analyzing the distribution characteristics of strong winds based on data such as wind speed and wind direction from meteorological stations along the high-speed railway and its surroundings, and counting the frequency distribution of wind speeds in each wind direction. Basic monitoring points; according to the terrain and landform data, establish the correlation relationship between the wind field between any two points along the railway under complex terrain, and use the theory and method of computational fluid dynamics (CFD) to simulate the wind field along the high-speed railway, and arrange interpolation monitoring points; Simulate the wind field of special road sections, combine with wind tunnel experiments, and arrange special monitoring points. The present invention overcomes the disadvantages of high cost, too much subjectivity, and low precision caused by traditional methods based on on-the-spot investigation and qualitative analysis, and realizes the layout of high-speed railway high-wind monitoring points under various climate types and environmental conditions. The simulation accuracy is high, and the cost of point layout is low, which provides support for the effective monitoring of railway strong winds and the safe operation of high-speed railways.

Description

Layout method of strong wind monitoring points on high-speed railway

technical field

The invention belongs to the technical field of monitoring and control for safe driving of high-speed railways in windy weather, and relates to a method for laying out high-speed railway monitoring points for strong winds, in particular to a method for laying out hierarchical monitoring points based on quantitative calculation .

Background technique

Due to the characteristics of strong transportation capacity, fast speed, high punctuality rate, all-weather operation and high economic efficiency, high-speed railway plays an increasingly prominent role in the transportation system. In order to alleviate the tense situation of railway transportation and meet the needs of national economic and social development, my country is currently vigorously building high-speed railways. Due to the light body and fast speed of high-speed trains, the buoyancy force and pitching moment generated during operation are large, and the train is sensitive to the influence of crosswinds, especially in some special road sections such as super-large bridges, high embankments, hills and curves in the tuyere area. It is very easy to produce derailment and overturning accidents, which will cause heavy casualties and huge economic losses. The current strong wind monitoring method is to set up several monitoring points along the railway line, install wind speed and direction sensors and acquisition units, and collect wind speed and direction data in real time. Therefore, how to arrange strong wind monitoring points and ensure the validity and representativeness of monitoring data is an important part of establishing a monitoring and early warning system.

Since high-speed railway is a new thing that has only emerged in my country in recent years, and its development history in the world is not long, there are very few studies on the distribution method of high-speed railway wind monitoring at home and abroad. At present, domestic and foreign scholars focus on the layout of road meteorological monitoring points, the micro-site selection of wind farms, and the optimal distribution of atmospheric environment monitoring points. In the research on the layout of highway meteorological monitoring points, researchers mostly use qualitative analysis methods to narrow down the distribution range first, and select the location of monitoring points in combination with on-the-spot investigation and expert consultation. This method largely relies on the experience of researchers, is highly subjective, and does not involve scientific calculation methods for the number of monitoring points. It is difficult to judge whether the existing monitoring points can meet the wind speed monitoring requirements of the entire road. It is also difficult to directly apply the method of micro-site selection of wind farms to the monitoring and distribution of high-speed railways. In the site selection of wind farms, researchers usually choose locations with high average wind energy on a longer time scale, so they focus on analyzing the average wind speed and direction, average wind energy, and average wind power. Considering the stability of the equipment, avoid locations where the wind speed changes drastically. The distribution of monitoring points for high-speed railways needs to consider the magnitude of the extreme wind speed and the probability of strong winds, which is very different from the microscopic site selection of wind farms. The optimal layout of atmospheric environment monitoring is to arrange a large number of measured points, and then select the best representative point from the measured points through various methods such as correlation analysis, cluster analysis, neural network, and fuzzy mathematics. Due to the high cost of high-speed railway wind monitoring, it is difficult to arrange large-scale measured points before the optimal point is determined.

At the same time, several high-speed railways that have been built, are under construction or are to be built in my country have a long mileage and span multiple climatic zones. There are many special wind environments such as long bridges, viaducts, hills and mountainous tuyere along the line, directly copying foreign layout principles and values. The simulation method to analyze the wind field along the high-speed railway in my country may cause large errors. Therefore, it is of great significance to carry out comprehensive research on the distribution method of high-speed railway wind monitoring for ensuring driving safety.

Contents of the invention

The purpose of the present invention is to overcome the deficiencies in the existing methods, and provide a layered high-speed railway monitoring point layout method. This method is mainly based on fluid numerical simulation, combined with wind tunnel experiments, quantitatively analyzes the probability of strong winds along the high-speed railway, selects representative monitoring point locations, and effectively prevents the phenomenon of leakage and excess distribution.

The technical scheme of the present invention is as follows: a method for laying high-speed railway high-wind monitoring points, comprising the steps of:

(1) Statistically analyze the temperature, pressure and wind data of the conventional meteorological stations along the high-speed railway and around it, establish a two-parameter Weibull distribution wind speed probability model, and use historical data to calibrate the model parameters. Set up basic monitoring points on topographic units;

(2) Divide the geomorphic unit, study the correlation of wind field between any two points along the railway under complex terrain according to the topographic and geomorphic data in the same geomorphic unit, establish the correlation function of the probability of strong wind occurrence, and use the Navier-Stokes fluid equation to simulate the elevation The wind field along the high-speed railway under the joint action of factors such as surface roughness, obstacles, etc., and interpolation monitoring points are arranged;

(3) Carry out high-resolution numerical simulation and wind tunnel experiment simulation on the wind field of a special road section, and set up special monitoring points at positions where the frequency of strong winds in the two simulations of the numerical simulation and the wind tunnel experiment are both greater than the predetermined frequency. The special road section includes said special road section including bends, high embankments, tunnels, passes, and hills.

Compared with existing methods, the present invention has the following advantages:

Aiming at arranging as few monitoring points as possible in the best position, so as to obtain as much wind characteristics as possible along the high-speed railway, and to ensure the representativeness, reliability and accuracy of monitoring data, a method based on quantitative calculation is proposed. hierarchical layout method. The present invention makes full use of the advantages of various technical means such as statistical analysis, computational fluid dynamics theory and method, and wind tunnel experiments, and overcomes the shortcomings of high cost, too much subjectivity, and precision caused by traditional methods based on field surveys and qualitative analysis. , realizes the layout of high-speed railway wind monitoring points under various climate types and various environmental conditions, with high simulation accuracy and low cost of point layout, providing support for effective monitoring of high-speed railway wind and safe operation of high-speed railways.

Description of drawings

Fig. 1 is a schematic diagram of the layout method of high-speed railway high wind monitoring points;

Figure 2 is a rose diagram of wind direction along the high-speed railway;

Figure 3 is a histogram of frequency distribution of wind speed along the high-speed railway and a Weibull distribution curve;

Figure 4 is a graph of total error versus distance.

Detailed ways

The present invention will be further described below in conjunction with accompanying drawing.

Accompanying drawing 1 is the realization process of high-speed railway high wind monitoring point layout.

(1) Analyze the wind speed and wind direction data of conventional weather stations along the high-speed railway and around. Count the frequency of each wind direction and draw a wind rose diagram. Figure 2 is a rose diagram of wind direction at meteorological stations along the high-speed railway, showing the frequencies of 12 wind directions. At intervals of 1m/s, the frequency of wind speed in each interval in each direction is counted, a two-parameter Weibull distribution wind speed probability model is established, and the model parameters are calibrated by using the maximum likelihood method. The curve fitting results are shown in Figure 3, the horizontal axis represents the wind speed, and the vertical axis represents the frequency of the wind speed.

The formula for the two-parameter Weibull distribution is:

$\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; v</span>}}{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; [</span><span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; v</span>}}{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ]</span>$

In the formula, p is the probability that the wind speed is equal to vm/s, k is the model shape parameter, and A is the model scale parameter.

(2) Divide geomorphic units based on elevation and surface cover. The maximum influence radius of strong winds in the same geomorphic unit is 20 km. Within this radius, the correlation function of wind field at any two positions along the line is established. The formula for the correlation function is:

In the formula, q is the correlation coefficient of wind speed between two points along the railway line, and d is the distance between two points. Taking the location with complete meteorological data as the initial point, the Navier-Stokes fluid equation is used to simulate the wind field along the high-speed railway, and the model parameters such as elevation, surface roughness, and obstacle length, width, and height are substituted into the Navier-Stokes fluid equation to calculate different The probability of strong winds corresponding to the distance and the simulation error caused by the limitation of software computing capacity, and draw the simulation error-distance curve. The simulated error-distance function is multiplied by the correlation function, and the curve of the total error with distance is drawn, and the curve of the total error with distance is fitted by the least square method. In high-speed railway control, the upper limit of error is 20%. As shown in Figure 4, the distance corresponding to the 20% error upper limit on the curve is the interpolation monitoring point layout spacing.

(3) Adjust the size of the calculation network, conduct high-resolution (50-meter resolution) simulations of wind fields on special road sections such as bends, high embankments, tunnels, passes, and hills, and calculate the frequency of strong winds along the railway. Use wind tunnel experiments to simulate the wind field of special road sections, and calculate the frequency of strong winds along the railway. Comparing the calculation results of the fluid simulation described in step (1) and step (2) and the wind tunnel experiment, in the two simulations, special monitoring points are arranged at positions where the occurrence frequency of strong winds is greater than the predetermined frequency.

Claims (8)

1. the method laid of a high-speed railway gale monitoring point, it is characterized in that, by the statistical study of high-speed railway weather station along the line data, the fluid numerical simulation of strong wind probability of occurrence, in conjunction with wind tunnel experiment, realize the laying of fundamental surveillance point, interpolation monitoring point, three different levels monitoring points of special monitoring point.

2. the method that high-speed railway gale monitoring point is laid is characterized in that described method comprises the steps:

(1) data such as wind speed along the line and conventional weather station on every side, wind direction are carried out statistical study to high-speed railway, utilize the strong wind frequency of occurrences on the boundary layer airflow modular estimate railway all kinds of topography and geomorphologies along the line unit, lay the fundamental surveillance point on greater than the geomorphic unit of preset frequency in the strong wind frequency of occurrences;

(2) divide geomorphic unit, in same geomorphic unit, study the railway correlationship of point-to-point transmission wind field arbitrarily along the line under the complex-terrain according to the landform relief data, set up the relevance function of point-to-point transmission strong wind probability of occurrence, simulation high-speed railway wind field along the line, calculate high-speed railway strong wind probability of occurrence and simulation error along the line, reach in total error and lay the interpolation monitoring point on the position of the error upper limit, wherein, described total error comprises that simulation error and distance become two parts of Model Calculation error that cause greatly;

(3) wind field to special road section carries out numerical Simulation of High Resolution and wind tunnel experiment simulation, all greater than laying the special monitoring point on the position of preset frequency, wherein said special road section comprises that described special road section comprises bend, high embankment, tunnel, bealock, local landform to the strong wind frequency of occurrences in numerical simulation and wind tunnel experiment.

3. the method that high-speed railway gale monitoring point as claimed in claim 2 is laid, it is characterized in that: step (1) is described utilizes on the boundary layer airflow modular estimate railway all kinds of topography and geomorphologies along the line unit strong wind frequency of occurrences preferably to set up Two-parameter Weibull Distribution wind speed probability model, utilize maximum-likelihood method to carry out the calibration of model parameter, the formula of Two-parameter Weibull Distribution is:

$p=\left(\frac{k}{A}\right){\left(\frac{v}{A}\right)}^{k-1}\exp [-{\left(\frac{v}{A}\right)}^k]$

In the formula, p is the probability that wind speed equals vm/s, and k is the mould shapes parameter, and v is a wind speed, and A is the model dimension parameter.

4. the method that high-speed railway gale monitoring point as claimed in claim 2 is laid, it is characterized in that: the foundation of the described division geomorphic unit of step (2) comprises elevation and increased surface covering.

5. the method for laying as claim 2 or 4 described high-speed railway gale monitoring points, it is characterized in that: step (2) is set up and further is included in maximum effect radius of determining strong wind in the same geomorphic unit in the relevance function of strong wind probability of occurrence, the correlativity of wind speed weakens gradually with the increase of distance between 2 along the line of the railway in this scope, and the formula of relevance function is:

In the formula, q is the related coefficient of wind speed between 2 along the line of the railway, R _MaxBe maximum effect radius of strong wind, d is the distance between 2.

6. the method that high-speed railway gale monitoring point as claimed in claim 2 is laid, it is characterized in that: step (2) comprises distinguishes the principal element that influences wind field, the flow field variation when comprising seasonal variety, the train high-speed cruising of vertical height, topography and geomorphology, barrier surrounding area, face of land vegetation, meteorological sensor position, electromagnetic compatibility etc.

7. the method that high-speed railway gale monitoring point as claimed in claim 2 is laid, it is characterized in that: the described simulation error of step (2) is the error that is caused by the computed in software capabilities limits, and total error comprises that simulation error and distance become two parts of Model Calculation error that cause greatly.

8. the method that high-speed railway gale monitoring as claimed in claim 5 is laid, it is characterized in that: step (2) is preferably utilized Navier-Stokes flow equation simulation high-speed railway wind field along the line, calculate high-speed railway strong wind probability of occurrence and simulation error along the line, draw simulation error-distance Curve, and in conjunction with the relevance function of described strong wind probability of occurrence, draw total error with the described railway change curve of the distance of point-to-point transmission arbitrarily along the line, utilize this curve of least square fitting, the cloth dot spacing of the pairing interpolation of estimation error upper limit monitoring point.

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