CN102023317A - Method for deploying strong wind monitoring points on rapid transit railway - Google Patents
Method for deploying strong wind monitoring points on rapid transit railway Download PDFInfo
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Abstract
The invention discloses a method for deploying strong wind monitoring points on a rapid transit railway, comprising the following steps: analyzing the gale distribution characteristics and carrying out statistics on the wind speed frequency distribution under each wind direction based on the information of wind speeds, wind directions and the like of weather stations along and around the rapid transit railway line, and deploying basic monitoring points; establishing the correlationship of winds fields between any two monitoring points along the railway line under complicated terrains based on the geographic and geomorphic information, simulating the wind fields along the rapid transit railway line by adopting the computational fluid dynamics (CFD) method theory and method, and deploying interpolated monitoring points; and simulating the wind fields of special road sections, and deploying special monitoring points by combining with wind tunnel tests. The method provided by the invention overcomes the defects of high cost, strong subjectivity, low accuracy and the like in the traditional method which gives priority to field survey and qualitative analysis, realizes deployment of strong wind monitoring points in rapid transit railways under various weather types and environmental conditions, has high simulation accuracy and low monitoring point deploying cost, and provides support for effective monitoring and safety running of rapid transit railways.
Description
Technical field
The invention belongs to the monitoring and the control technology field of high-speed railway safe driving under the strong wind weather, relate to a kind of method that high-speed railway gale monitoring point is laid that is used for, specifically relate to a kind of distribution method of monitoring point by different level based on quantitative Analysis.
Background technology
High-speed railway is characteristics such as movement capacity is strong, speed is fast, percent of punctuality is high, round-the-clock running, business efficiency height because of having, and role becomes increasingly conspicuous in the communications and transportation system.In order to alleviate the pressure of transportation by railroad, satisfy the needs of national economy and social development, China is building high-speed railway energetically at present.Because the car body of bullet train is light, speed is fast, rising buoyancy and facing upward the moment of bowing of producing during operation is big, train offside wind effect sensitivity, especially at some special road sections such as the especially big bridge in zone, air port, high embankment, hills and bends, very easily produce derailing, capsizing case, and then cause great casualties and tremendous economic loss.The high wind monitoring means that adopts is to set up several monitoring points along the railway at present, and wind speed wind direction sensor and collecting unit are installed, and gathers the wind speed and direction data in real time.Therefore, how to carry out gale monitoring point and lay, the validity that guarantees Monitoring Data is the important step of setting up monitor and early warning system with representative.
Because high-speed railway is the new things that China just rose in recent years, worldwide developing history is not long yet, and domestic and international research about high-speed railway gale monitoring points distributing method seldom.At present, the research of Chinese scholars is layouted based on the laying of highway weather monitoring point, the addressing of wind energy turbine set microcosmic and atmosphere environment supervision optimization.In the research that highway weather monitoring point is laid, the researcher adopts qualitative analysis methods to dwindle the cloth point range earlier more, chooses the position, monitoring point in conjunction with examine on the spot and expert consulting.This method depends on researcher's experience to a great extent, and subjectivity is strong, and does not relate to the science computing method of monitoring point quantity, is difficult to judge whether existing monitoring point can satisfy road air monitoring requirement completely.The method of wind energy turbine set microcosmic addressing also is difficult to directly apply to the high-speed railway monitoring and layouts.In the wind energy turbine set addressing, researcher's multiselect is got the big position of average wind energy under the long period yardstick, therefore the equal wind power of weight analysis mean wind speed wind direction, average wind energy peace.Consider stabilization of equipment performance, avoid wind speed to change violent position.And the probability that the high-speed railway monitoring is layouted and needed the size of consideration extreme value wind speed and high wind to occur has very big difference with the addressing of wind energy turbine set microcosmic.It then is to lay a large amount of eyeballs position that atmosphere environment supervision optimization is layouted, and chooses representative optimum position by the whole bag of tricks such as correlation analysis, cluster analysis, neural network, fuzzy mathematicses from the eyeball position again.Because high-speed railway gale monitoring cost height be difficult to lay the eyeball position on a large scale before having determined the optimum position, so the atmosphere environment supervision points distributing method is not suitable for the laying of high-speed railway gale monitoring point.
Simultaneously, China builds, build or several high-speed railway mileages yet to be built long, cross over a plurality of climate zones, it is numerous to be positioned at special wind environments such as the bridge of growing up, overpass, hills and air port, mountain area along the line, and the high-speed railway wind field along the line of directly indiscriminately imitating external layout principle and method for numerical simulation analysis China may cause bigger error.Therefore, carry out the synthetic study of high-speed railway gale monitoring points distributing method, significant for guarantee driving safety.
Summary of the invention
The objective of the invention is to overcome the weak point that exists in the existing method, a kind of with different levels high-speed railway gale monitoring point distribution method is provided.This method is modeled as the master with fluid numerical value, and in conjunction with wind tunnel experiment, quantitative test high-speed railway strong wind probability along the line is chosen position, representational monitoring point, and the phenomenon of effectively anti-leak-stopping cloth, many cloth occurs.
Technical scheme of the present invention is as follows: the method that a kind of high-speed railway gale monitoring point is laid comprises the steps:
(1) temperature, pressure, wind data along the line to high-speed railway and conventional on every side weather station data are carried out statistical study, set up Two-parameter Weibull Distribution wind speed probability model, utilize historical summary to carry out the calibration of model parameter, on the high geomorphic unit of strong wind probability of occurrence, lay the fundamental surveillance point;
(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 strong wind probability of occurrence, and utilize factor actings in conjunction such as Navier-Stokes flow equation simulation elevation, roughness of ground surface, barrier high-speed railway wind field along the line down, laying interpolation monitoring point;
(3) wind field to special road section carries out numerical Simulation of High Resolution and wind tunnel experiment simulation, the strong wind frequency of occurrences is laid the special monitoring point on all greater than the position of preset frequency in numerical simulation and twice simulation of wind tunnel experiment, and wherein said special road section comprises that described special road section comprises bend, high embankment, tunnel, bealock, hills.
The present invention compares with existing method, has following advantage:
To lay the position of the least possible monitoring point in the best, make it obtain high-speed railway strong wind feature along the line as much as possible, guarantee that representativeness, reliability and the accuracy of Monitoring Data is purpose, a kind of distribution method by different level based on quantitative Analysis is proposed.The present invention makes full use of statistical study, Fluid Mechanics Computation is theoretical and the advantage of multiple technologies means such as method, wind tunnel experiment, overcome deficiencies such as classic method is too strong based on cost height, the subjectivity that examine on the spot, qualitative analysis were caused, precision, realized under the various climate types, the laying of high-speed railway gale monitoring point under the various environmental baseline, the simulation precision height, the cost of layouting is low, for effective monitoring of railway gale and the safe operation of high-speed railway provide support.
Description of drawings
Fig. 1 is a high-speed railway gale monitoring point distribution method synoptic diagram;
Fig. 2 is a high-speed railway wind rose map along the line;
Fig. 3 is high-speed railway wind speed frequency distribution histogram along the line and Weibull distribution curve;
Fig. 4 is the curve of total error with variable in distance.
Embodiment
The invention will be further described below in conjunction with accompanying drawing.
Accompanying drawing 1 is the implementation procedure that high-speed railway gale monitoring point is laid.
(1) wind speed along the line and conventional weather station on every side, wind direction data are analyzed to high-speed railway.Add up the frequency that each wind direction occurs, draw wind rose map.Fig. 2 is a high-speed railway weather station along the line wind rose map, represents the frequency that 12 wind directions occur.With 1m/s is at interval, and the frequency that each interval wind speed occurs on the statistics all directions is set up Two-parameter Weibull Distribution wind speed probability model, utilizes maximum-likelihood method to carry out the calibration of model parameter.Curve-fitting results as shown in Figure 3, transverse axis is represented the wind speed size, the longitudinal axis is represented the frequency that this wind speed occurs.
The formula of Two-parameter Weibull Distribution is:
In the formula, p is the probability that wind speed equals vm/s, and k is the mould shapes parameter, and A is the model dimension parameter.
(2) serve as main according to the division geomorphic unit with elevation and increased surface covering, maximum effect radius of the interior strong wind of same geomorphic unit is got 20km, sets up the relevance function of wind field on any two positions along the line in this radius.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, and d is the distance between 2.To possess the position of complete meteorological data as initial point, utilize Navier-Stokes flow equation simulation high-speed railway wind field along the line, model parameter substitution Navier-Stokes flow equations such as length and width height with elevation, roughness of ground surface, barrier, calculate pairing strong wind probability of occurrence of different distance and the simulation error that causes by the computed in software capabilities limits, draw simulation error-distance Curve.Simulation error-distance function and relevance function multiply each other, and draw the change curve of total error with distance, utilize the change curve of least square fitting total error with distance.In high-speed railway control, the error upper limit gets 20%.As shown in Figure 4,20% the pairing distance of the error upper limit is the cloth dot spacing of interpolation monitoring point on this curve.
(3) adjust the size of computational grid, the wind field of special road sections such as bend, high embankment, tunnel, bealock, hills is carried out the simulation of high resolving power (resolution is 50 meters), calculate the railway strong wind frequency of occurrences along the line.Utilize the wind field of wind tunnel experiment simulation special road section, calculate the railway strong wind frequency of occurrences along the line.The result of calculation of contrast step (1), the described fluid simulation of step (2) and wind tunnel experiment, the strong wind frequency of occurrences is all greater than laying the special monitoring point on the position of preset frequency in twice simulation.
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:
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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| CN103336860A (en) * | 2013-06-07 | 2013-10-02 | 国家电网公司 | Generation method for grid wind speed distribution map |
| CN103675355A (en) * | 2013-11-19 | 2014-03-26 | 中国大唐集团科学技术研究院有限公司 | Anemometer monitoring method and system |
| CN104015757A (en) * | 2014-06-09 | 2014-09-03 | 中南大学 | Railway train operation safety situation judgment method and device with multi-information integrated |
| CN105740990A (en) * | 2016-02-26 | 2016-07-06 | 中铁第四勘察设计院集团有限公司 | Method for selecting resident monitoring points in railway wind monitoring system |
| CN106897517A (en) * | 2017-02-22 | 2017-06-27 | 中铁二院工程集团有限责任公司 | Line of high-speed railway gale monitoring optimizes automatic search method of arranging net |
| CN108090285A (en) * | 2017-12-20 | 2018-05-29 | 中国科学院寒区旱区环境与工程研究所 | A kind of microclimate observation points distributing method suitable for the monitoring of complicated landform transmission line of electricity disaster caused by a windstorm |
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| CN109141808A (en) * | 2018-10-29 | 2019-01-04 | 广州地铁集团有限公司 | Wind speed space deduction method along the perception of subway overhead line road multiple spot wind speed |
| CN109765335A (en) * | 2018-12-25 | 2019-05-17 | 北京英视睿达科技有限公司 | Method, control device and the electronic equipment of monitoring point are set in monitoring region |
| CN111079808A (en) * | 2019-12-05 | 2020-04-28 | 国网湖南省电力有限公司 | Method and system for rapidly predicting gust based on weather typing |
| CN111239857A (en) * | 2020-02-18 | 2020-06-05 | 潘新民 | Strong wind forecasting method for special terrain |
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| CN104015757B (en) * | 2014-06-09 | 2015-05-13 | 中南大学 | Railway train operation safety situation judgment method and device with multi-information integration |
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| CN115936474B (en) * | 2022-10-17 | 2023-08-08 | 中南大学 | Method for setting strong wind monitoring points along high-speed railway |
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