CN117272685B - Optimal sanding decision simulation method based on train operation parameters - Google Patents
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Abstract
The invention relates to the technical field of mathematical modeling of an optimal value function, in particular to an optimal sanding decision simulation method based on train operation parameters, which comprises the following steps: step (1): constructing a hierarchical structure model for judging the sanding logic, and introducing an AHP algorithm to calculate a weight vector of a target so as to realize autonomous judgment of the sanding logic; step (2): taking the adhesion coefficient as an evaluation criterion of the sanding effect, and establishing a basic database of a plurality of train operation parameters and corresponding optimal sanding amount; step (3): and (3) establishing a sanding amount optimal value function by adopting multi-element fitting, calculating the optimal sanding amount under the comprehensive influence of train operation parameters, and outputting an optimal sanding decision to a lower computer by adjusting air pressure. The invention provides a practical simulation calculation method for autonomous judgment of sanding logic and decision-making of optimal sanding amount, and greatly improves the particle injection speed, sanding accuracy and tackifying efficiency.
Description
Technical Field
The invention relates to the technical field of mathematical modeling of optimal value functions, in particular to an optimal sanding decision simulation method based on train operation parameters.
Background
In order to solve the problem of low adhesion frequently occurring in railway systems, sand spraying and adhesion increasing modes are frequently adopted at home and abroad to improve the adhesion coefficient of wheel tracks. The principle is as follows: the sand spraying device is arranged in front of the locomotive traction wheel set, adhesion-promoting particles are sprayed on the wheel-rail interface, the shearing resistance of the wheel-rail interface is improved, and rail surface pollutants are removed, so that the adhesion-promoting purpose is achieved.
Currently, sanding presents 3 significant problems in current stage applications. The sanding logic judgment has strong subjective consciousness, and mainly judges whether sanding is performed according to the experience of a locomotive driver, so that the problems of sanding lag and the like are easily caused. Secondly, the constant flow sanding is adopted in the sanding process, the flow cannot be adjusted, the requirement of a locomotive on the adhesion coefficient cannot be met when the sanding amount is too small, and the damage of a wheel track and even a circuit insulation accident are further aggravated besides the waste of particles when the sanding amount is too large. Thirdly, the particle injection speed of the sanding device at the present stage is too low, and the particle quantity and the sanding quantity which actually enter the wheel track interface to generate the tackifying effect have great difference under the interference of external factors. Aiming at the problems, domestic and foreign specialists propose to adjust the sand spraying flow according to the running speed or the acceleration of the train. However, this method still has drawbacks: firstly, the flow is regulated in a grading way, and the control precision is low; secondly, the influence factors influencing the sanding amount are more, and the sanding decision is made only according to a single factor, so that the sanding decision has one-sided property.
The sand control system is used as an important component of the sand device, and how to provide an optimal sand decision simulation method based on train operation parameters, which is used for comprehensively analyzing sand influence factors, automatically judging sand logic and deciding optimal sand, greatly improving sand accuracy and viscosity increasing efficiency, is a problem to be solved by those skilled in the art.
Disclosure of Invention
The invention provides an optimal sanding decision simulation method based on train operation parameters, which can comprehensively analyze the influence of the train operation parameters on sanding amount, adopts an AHP (hierarchical structure analysis) algorithm and sanding amount optimal value function modeling, and provides a novel simulation method for autonomous judgment of sanding logic of a sanding device and optimal sanding amount decision. The simulation method is used for detecting feasibility and effectiveness on a high-speed sanding device, and the sanding accuracy and the tackifying efficiency of the particle injection speed are greatly improved.
The automatic sand spraying control method based on the train operation parameters comprises the following steps:
step (1): constructing a sanding logic judgment hierarchical structure model based on train operation parameters, and introducing an AHP algorithm to calculate a weight vector of a target so as to realize autonomous judgment of the sanding logic;
step (2): taking the adhesion coefficient as an evaluation criterion of the sanding effect, and establishing a basic database of a plurality of train operation parameters and corresponding optimal sanding amount;
step (3): and (3) establishing a sanding amount optimal value function by adopting multi-element fitting, calculating the optimal sanding amount under the comprehensive influence of train operation parameters, and outputting an optimal sanding decision to a lower computer by adjusting air pressure.
Preferably, in the step (1), a hierarchical structure model of sanding logic judgment is established, specifically:
taking two schemes for decision-making, namely sanding or not sanding, as scheme layers of the hierarchical structure model;
taking locomotive acceleration, train running speed, creep rate, braking grade and traction grade as criterion layers of a hierarchical structure model;
the weight vector representing the sanding logic is taken as a target layer of the hierarchical structure model.
Preferably, in the step (1), the weight vector calculation process specifically includes:
respectively constructing comparison matrixes from a criterion layer to a target layer and from a scheme layer to the criterion layer according to a nine-scale method and a weight function;
consistency testing is carried out on the comparison matrix among all the layers by adopting a consistency ratio method;
when the comparison matrix meets consistency test, a geometric average method introduced into an AHP algorithm is used for solving a sanding weight vector, and the method specifically comprises the following steps:
；
wherein,W i is the first to influence decisions in the criterion layeriThe weight vector of the individual factors is used,a ij to compare the first in the matrixiThe corresponding scale of the personal factors is with the firstjThe individual factors correspond to the ratio between the scales,a kj to compare the first in the matrixkThe corresponding scale of the personal factors is with the firstjThe individual factors correspond to the ratio between the scales,nthe number of factors affecting the sanding amount.
Preferably, in the step (1), the weight vector of the sanding logic is calculated, specifically:
；
wherein,Was a weight vector for the sanding logic,W A as a weight vector from the criterion layer to the target layer,W B weight vectors from the scheme layer to the criterion layer; when the value representing sanding in the weight vector of the target layer is larger than 0.5, the sanding requirement is strong, and the sanding action is executed.
Preferably, in the step (2), a basic database of a plurality of train operation parameters and corresponding optimal sanding amounts is established, specifically:
based on a rolling simulation testing machine, selecting a recovery standard of an adhesion coefficient of a wheel rail after sanding under a low adhesion working condition, and determining a testing scheme of influence of train operation parameters on the optimal sanding amount;
designing each train operation parameter as a multi-level orthogonal test table, adjusting the sanding amount under each group of tests, taking the adhesion coefficient as an evaluation criterion of the sanding effect, and enabling the maximum adhesion coefficient to correspond to the optimal sanding amount;
and (3) confirming the optimal sanding amount under each group of tests, and establishing a basic database of a plurality of train operation parameters and the corresponding optimal sanding amount.
Preferably, in the step (3), a multi-element fitting is adopted to establish an optimal value function of the sanding amount, and the optimal sanding amount under the comprehensive influence of the train operation parameters is calculated, specifically:
based on a basic database of the optimal sanding amount, selecting an optimal value function type, and obtaining a fitting formula of train operation parameters and the optimal sanding amount through a least square method;
and calculating the optimal sanding amount under the current train operation parameters according to the optimal value function relation.
Preferably, in the step (3), after logic judgment and calculation of an optimal value, outputting an optimal sanding decision to a lower computer by adjusting air pressure, specifically:
performing linear regression analysis on the influence test of air pressure on sand scattering amount, and establishing a functional relation between the air pressure and the sand scattering amount;
calculating an air pressure value corresponding to the optimal sanding amount according to the air pressure and sanding amount functional relation;
and an optimal sanding decision is executed by adopting an electromagnetic valve and an air pressure controller APC, so that sanding logic judgment and sanding amount optimal value adjustment are realized.
Preferably, an optimal sanding decision simulation system based on train operation parameters is adopted, and the optimal sanding decision simulation system based on train operation parameters comprises a train operation parameter detection system, an optimal sanding decision model and a sanding device;
the optimal sanding decision simulation method is characterized in that a computer is written in through a program, an upper computer of the computer is a train operation parameter detection system, a lower computer of the computer is an air pressure controller, and data are shared among the computer, the train operation parameter detection system and the air pressure controller through a data line;
the sanding device comprises an air pressure generator, an air pressure controller, a sand box, a sanding valve, a sanding pipe, a high-speed spray gun and a plurality of air pipes; the air pressure generator is positioned at the front end of the sanding device, the air outlet of the air pressure generator is connected to the air pressure controller through an air pipe, and compressed air after being regulated flows through a flow divider to be respectively input into a sanding valve and a high-speed spray gun; the sand spreading valve is arranged at the bottom of the sand box and is used for sucking adhesion-promoting particles stored in the sand box; the sanding valve is connected with the high-speed spray gun through a sanding pipe.
Preferably, the high-speed sanding device adopts a two-stage accelerating sanding mode, the sanding valve accelerates the adhesion-promoting particles once by utilizing the entrainment effect, and the high-speed spray gun adopts a shrinkage nozzle to accelerate secondarily.
Preferably, the train operation parameter detection system is used for acquiring train operation parameters, and comprises an encoder and an acceleration sensor.
The beneficial effects of the invention are as follows:
(1) According to the invention, an AHP algorithm is introduced to establish a sanding logic judgment hierarchical structure model based on train operation parameters, the hierarchical structure model comprises a scheme layer, a criterion layer and a target layer, the sanding logic judgment problem is converted into a sanding weight vector solving problem, and whether the current train needs sanding or not can be autonomously judged according to the vector size. The simulation method effectively avoids manual subjective intervention and improves accuracy of sand scattering logic judgment.
(2) The invention simulates the influence test of train operation parameters on the optimal sanding amount based on the rolling simulation testing machine, establishes a basic database of a plurality of operation parameters and the corresponding optimal sanding amount, and establishes the sanding amount optimal value function by adopting multi-element fitting. And (3) carrying out linear regression analysis on the influence test of air pressure on the sanding amount, establishing a linear relation between the air pressure and the sanding amount, and executing optimal sanding decision through electric elements such as an electromagnetic valve, an Air Pressure Controller (APC) and the like to realize sanding logic judgment and stepless regulation of the optimal sanding amount value. Compared with the traditional sanding amount adjusting modes such as manual experience, sanding amount grading adjustment and control and the like, the optimal sanding decision simulation method provided by the invention avoids subjectivity and hysteresis caused by manual experience intervention, and the control precision is as high as 0.1kg/min.
(3) The high-speed sanding device for detecting the optimal sanding simulation method comprises key parts such as a sanding valve, a high-speed spray gun and the like, compressed air can accelerate particles once through a entrainment effect generated by the sanding valve, and a contraction type nozzle at the high-speed spray gun accelerates the particles secondarily. The sand spraying mode avoids energy loss generated when particles pass through the sand spraying pipe, adopts a two-section accelerating sand spraying mode, improves the particle spraying speed to about 50m/s, and improves the accuracy and the tackifying efficiency of sand spraying.
(4) The sanding particles of the sanding automatic control device based on the train operation parameters adopt novel tackifying hard particles, and compared with common quartz sand particles, the novel tackifying hard particles can improve the tackifying effect by 30 percent and reduce the use amount of the particles by 87.5 percent.
Drawings
Fig. 1 is a schematic diagram of a sand spraying automatic control method based on train operation parameters in an embodiment.
FIG. 2 is a flowchart of a method for calculating a sanding logic weight vector in accordance with an embodiment.
Fig. 3 is a schematic diagram of a sanding logic judgment hierarchical structure model based on train operation parameters established by an AHP algorithm in an embodiment.
FIG. 4 is a graph showing the relationship between air pressure and sanding amount as a function of the air pressure after regression analysis according to the air pressure influence test in the example.
Fig. 5 is a schematic structural diagram of a high-speed sanding apparatus for detecting effectiveness of an optimal sanding decision simulation method in an embodiment.
FIG. 6 is a schematic diagram showing the assembly structure of the sanding valve and the flask in the embodiment.
FIG. 7 is a schematic view of a high-speed spray gun according to an embodiment.
FIG. 8 is a schematic diagram showing the results of the detection of the particle ejection speed of the sanding apparatus in the example.
The reference symbols in the drawings: the device comprises a 1-air pressure generator, a 2-air outlet, a 3-air pressure input interface, a 4-air pressure output interface, a 5-air pressure controller, a 6-diverter, a 7-sanding valve air pressure input air pipe, an 8-sanding valve, a 9-sand box, a 10-sanding pipe, an 11-high-speed spray gun, a 12-data wire, a 13-computer, a 14-train operation parameter detection system, a 15-high-speed spray gun air pressure input air pipe, a 16-sanding valve sand suction port, a 17-sanding valve air inlet, an 18-sanding valve throat, a 19-sanding valve sand outlet, a 20-high-speed spray gun sand inlet, a 21-high-speed spray gun nozzle, a 22-high-speed spray gun mixing chamber and a 23-high-speed spray gun outlet.
Description of the embodiments
For a further understanding of the present invention, the present invention will be described in detail with reference to the drawings and examples. It is to be understood that the examples are illustrative of the present invention and are not intended to be limiting.
Examples
As shown in fig. 1, the embodiment provides an optimal sanding decision simulation method based on train operation parameters, which includes the following steps:
step (1): constructing a sanding logic judgment hierarchical structure model based on train operation parameters, calculating a weight vector of the sanding logic through an AHP algorithm, realizing autonomous judgment of the sanding logic, and determining whether the train needs sanding currently according to the size of the weight vector, wherein the method specifically comprises the following steps of:
first, as shown in fig. 3, a sanding logic judgment hierarchical model based on train operation parameters is constructed, two schemes for decision-making, namely sanding or non-sanding, are used as scheme layers a of the hierarchical model, criteria or factors influencing the sanding amount decision-making, such as locomotive acceleration, train running speed, creep rate, braking grade, traction level and the like, are used as criterion layers B of the hierarchical model, and a sanding logic weight vector is used as a target layer C of the hierarchical model.
And secondly, respectively establishing a comparison matrix from a scheme layer to a criterion layer and from the criterion layer to a target layer according to a nine-scale method and a weight function.
(1) Scheme layer to criteria layer: because the influence factors such as acceleration rate, creep rate, speed, braking grade, traction grade and the like in the criterion layer are always changed in the locomotive movement process, a weight function of the influence of each factor on sanding can be established according to industry experts, and a comparison matrix from the scheme layer to the criterion layer is establishedThe following are provided:
；
wherein the method comprises the steps ofN=1, 2,3,4,5 represent 5 influencing factors of acceleration, creep rate, speed, braking level and traction level respectively,F(X N ) And a weight function for the influence of each influence factor in the criterion layer on sanding.
(2) Criterion layer to target layer: an industry expert of the railway system establishes a comparison matrix from each factor of the criterion layer to the target layer according to a nine-scale method, and specifically comprises the following steps: expert utilizationThe experience of the user scores the influence degree of each factor in the criterion layer affecting sanding, 1-9 respectively show that the factors are not important and are absolutely important for the target, and the scores of the factors from 1 to 9 show that the influence degree of the factors on the target is gradually important. Each influencing factor is compared in pairs, as shown in table 1, each parameter of the criterion layer is compared with the scale value, and a comparison matrix from the criterion layer to the target layer is establishedAThe criteria layer to target layer scaling table (table 1) and the comparison matrix are as follows:
table 1 criterion layer parameters contrast Scale
；
；
Then, before the hierarchical ordering, adopting a consistency ratio method to respectively carry out consistency test on the comparison matrix from the criterion layer to the target layer and the scheme layer to judge the consistency ratioCRWhether less than 0.1, calculated as follows:
；
；
wherein,CIandRIrespectively a consistency index and a random consistency index,λis the maximum eigenvalue of the matrix. The calculation finds that: although the matrix is comparedAAndBnot a consistent matrix, but meeting the consistency check of the matrix, indicates that the matrix construction is reasonable.
Finally, after consistency test is satisfied, hierarchical ordering calculation is needed to be carried out by adopting a geometric average method in an AHP algorithm, wherein the hierarchical ordering calculation comprises hierarchical single ordering calculation and hierarchical total ordering calculation. Hierarchical single-order computation, i.e. separately solving a matrix of criterion layers to target layersAAnd scheme layer to criterion layer matrixBWhile the hierarchical total ordering calculation solves for a sanding patrolThe edited weight vector is calculated as follows:
；
；
wherein,W i is the first to influence decisions in the criterion layeriThe weight vector of the individual factors is used,a ij to compare the first in the matrixiThe corresponding scale of the personal factors is with the firstjThe individual factors correspond to the ratio between the scales,a kj to compare the first in the matrixkThe corresponding scale of the personal factors is with the firstjThe individual factors correspond to the ratio between the scales,nthe number of factors affecting the sanding amount.WAs a weight vector for the sanding logic,W A as a weight vector from the criterion layer to the target layer,W B is a weight vector from the scheme layer to the criterion layer.
And the sanding logic judges, namely calculates a weight vector of the target layer, and when the weight vector value representing sanding is larger than 0.5, the sanding logic represents that the sanding requirement is strong. The optimal sanding decision simulation method controls the sanding amount by adjusting the air pressure,W>and 0.5, outputting an optimal sanding decision to a lower computer, namely the air pressure controller, and executing sanding action.
Step (2): based on a rolling simulation testing machine, carrying out an influence test of train operation parameters on sanding amount, taking an adhesion coefficient as an evaluation criterion of sanding effect, and establishing a basic database of a plurality of train operation parameters and corresponding optimal sanding amount as a basis of an optimal sanding decision model, wherein the method specifically comprises the following steps:
firstly, a scheme of a sand scattering amount influence test by train operation parameters is formulated. The double-wheel rolling simulation testing machine based on independent laboratory research and development simulates the running speed of a train by setting the rotating speed of a sample, sets a rotating speed change curve function to simulate the acceleration of the train in the running process, respectively simulates the traction level of the train when in starting and the braking level when in braking by inputting different accelerations and decelerations, and calculates the creep rate by simulating the rotating speed difference of the upper sample and the lower sample of the wheel pair. The following are provided:
；
wherein,V 1 andV 2 the linear speeds of the wheel and the steel rail sample are respectively;ω 1 andω 2 the rotation speeds (r/min) of the wheel and rail samples are respectively;d 1 andd 2 wheel and rail sample diameters (mm), respectively;
and secondly, simulating various low adhesion conditions in the actual running of the train, selecting a recovery standard of the adhesion coefficient of the wheel rail after sanding under the low adhesion working condition, wherein the maximum available adhesion coefficient of the wheel rail interface is more than 0.2, and meeting the requirement of train traction braking. Designing each train operation parameter as a multi-level orthogonal test table, adjusting the sanding amount under each group of tests, taking the adhesion coefficient as an evaluation criterion of the sanding effect, and enabling the maximum adhesion coefficient to correspond to the optimal sanding amount value;
finally, confirming the optimal sanding amount corresponding to each group of train operation parameters, and establishing a basic database of a plurality of train operation parameters and the corresponding optimal sanding amount, wherein table 2 shows the optimal sanding amount value:
TABLE 2 schematic table of optimum value of sanding amount
；
Step (3): based on the basic database of the train operation parameters and the corresponding optimal sanding amount, a multi-element fitting is adopted to establish the optimal value function of the sanding amount, and the optimal sanding amount under the comprehensive influence of the train operation parameters is calculated. And carrying out regression analysis on the influence test of the air pressure on the sanding amount, establishing a functional relation between the air pressure and the sanding amount, calculating a corresponding air pressure value, and outputting an optimal sanding decision to a lower computer by adjusting the air pressure. The method comprises the following steps:
firstly, establishing a sand-spraying amount optimal value function by adopting multiple fitting, selecting an optimal value function type, obtaining a fitting formula of train operation parameters and the optimal sand-spraying amount by a least square method, and calculating the optimal sand-spraying amount according to a functional relation, wherein the principle is as follows:
；
wherein,φ k (x) Is a set of linearly independent functions relating to train operating parameters,a k is corresponding to undetermined coefficientk=1, 2,3,4, 5), the fitting criterion is to make the test resulty i (i=1,2,…,n) And (3) withf(x i ) The sum of the squares of the distances of (2) is minimal. Under the condition of conforming to the precision of engineering application, the method approaches the real functional relation between the train operation parameters and the sanding amount infinitely, and calculates the optimal sanding amount under the train operation parameters under a certain low-adhesion working condition by analyzing and observing the change rule.
And secondly, carrying out linear regression analysis on the influence test of the air pressure on the sanding amount, and establishing a functional relation between the air pressure and the sanding amount. The flow for establishing the functional relationship between the air pressure and the sand spraying amount is the same as that described above, and will not be described here again. The relationship between the air pressure and the sanding amount established after regression analysis according to the air pressure influence test is shown in FIG. 4. The results show that the air pressure and the sanding amount show a linear relation, and the method is specifically as follows:
；
wherein,Qthe unit is kg/min for sand spraying,Pthe unit is MPa for the pressure of compressed air. Wherein the method comprises the steps ofRThe closer to 1 the value of 0.88, the better the fitting degree of the linear regression curve to the observed value, the closer to the functional relationship between the true sanding amount and the air pressure.
And finally, calculating an air pressure value corresponding to the optimal sanding amount according to the air pressure and sanding amount functional relation, and executing optimal sanding decision by adopting electric elements such as an electromagnetic valve, an Air Pressure Controller (APC) and the like to realize sanding logic judgment and sanding amount optimal value adjustment. The air pressure controller can automatically output the compressed air (0-2.0 MPa) with the required accurate pressure according to the optimal sanding decision, and the simulation method does not need to manually adjust the pressure in the implementation process.
The embodiment adopts an optimal sanding decision simulation system based on train operation parameters, and comprises a train operation parameter detection system 14, an optimal sanding decision model and a sanding device (shown in figure 5);
the optimal sanding decision simulation method is characterized in that the optimal sanding decision simulation method is written into a computer 13 through a program, an upper computer of the computer 13 is a train operation parameter detection system 14, a lower computer of the computer 13 is an air pressure controller 5, and data are shared among the computer 13, the train operation parameter detection system 14 and the air pressure controller 5 through data lines; the optimal sanding decision simulation method is used for calculating the air pressure corresponding to the optimal sanding amount required by the train operation parameters under the low adhesion working condition. The concrete functions are as follows: firstly, inputting train operation parameters into a sanding logic judgment hierarchical structure model to judge whether sand is sprayed or not; and secondly, calculating the optimal sanding amount under the train operation parameters according to the sanding amount optimal value function, and outputting an optimal sanding decision.
The train operation parameter detection system 14 is used for acquiring train operation parameters, including encoders and acceleration sensors.
The sanding device comprises an air pressure generator 1, an air pressure controller 5, a sand box 9, a sanding valve 8, a sanding pipe 10, a high-speed spray gun 11 and a plurality of air pipes; the air pressure generator 1 is positioned at the front end of the sanding device, the air outlet 2 of the air pressure generator is connected to the air pressure controller 5 through an air pipe, the air pressure of the compressed air APC is automatically adjusted, and then the compressed air APC flows through the flow divider 6 to be respectively input into the sanding valve air pressure input air pipe 7 and the high-speed spray gun air pressure input air pipe 15 at the rear end of the sanding device. The sand box 9 is used for storing tackifying particles used for sanding and is fixed on a sand box bracket; the sanding valve 8 is arranged at the bottom of the sand box 9 and is used for sucking adhesion-promoting particles stored in the sand box 9; the sanding pipe 10 is respectively connected with a sanding valve 8 and a high-speed spray gun 11 and is used for transporting mixed jet flow of tackifying particles and compressed air; the high-speed spray gun 11 is used for spraying adhesion-promoting particles; the gas transmission pipe is connected with each unit and is used for transmitting compressed air and is connected by adopting a quick connector, and a raw material belt is wound around the thread at the other end of the quick connector. The installation, connection and detailed function and function of the key components of the sanding device are set forth below.
Air pressure generator (air compressor) 1: for providing clean and dry compressed air to the entire sanding device.
The air pressure controller 5: the device is used for automatically outputting the required air pressure and comprises an air pressure input interface 3, an air pressure output interface 4 and a control signal input interface. The air pressure input interface 3 is connected with an air compressor outlet through an air pipe, and the air pressure output interface 4 is connected with an air inlet of a sanding valve and an air inlet of a high-speed spray gun respectively through the air pipe after passing through the flow divider 6, and the air pipes are connected through quick connectors so as to ensure good air tightness. The control signal input interface is connected to the computer and software via data lines 12.
Sand box 9: the sand box is used for storing sand spreading and tackifying particles, comprises a sand box cover and a sand viewing port, and can be used for observing the positions of the particles in the sand box at any time and supplementing the particles in time.
Sand spreading valve 8: the compressed air is drawn up the adhesion promoting particles in the sand box when passing through the sanding valve, and as shown in fig. 6, the structural assembly diagram of the sanding valve and the sand box comprises: the sand spraying valve comprises a sand sucking port 16 of the sand spraying valve, a sand spraying valve air inlet 17 (connected with a sand spraying valve air pressure input air pipe 7), a sand spraying valve throat 18 and a sand spraying valve sand outlet 19. Compressed air enters the sanding valve through the air inlet, when the compressed air passes through the throat part of the sanding valve (the throat part is the position with the minimum diameter of a pipeline), the air flow speed increases, the pressure decreases, a certain negative pressure is formed at the throat part to generate entrainment flow, the air at the sand suction opening continuously sucks the tackifying particles in the sand box into the cavity in the sanding valve under the entrainment effect, and the tackifying particles are accelerated by the compressed air once and then enter the sanding pipe from the sand outlet.
Sand pipe 10: a mixed jet for delivering compressed air and tackifying particles. One end of the sanding pipe is connected with the sand outlet of the sanding valve, the other end of the sanding pipe is connected with the sand inlet of the high-speed spray gun, and the two ends of the sanding pipe are fastened by utilizing a stainless steel clamp pipe clamp so as to ensure good air tightness.
High-speed spray gun 11: for secondary acceleration of the particle jet to increase the particle jet velocity. Fig. 7 shows a schematic view of the structure of the high-speed spray gun, which comprises a sand inlet 20 of the high-speed spray gun, a nozzle 21 of the high-speed spray gun, a mixing chamber 22 of the high-speed spray gun and an outlet 23 of the high-speed spray gun. The particles and compressed air enter the mixing chamber from the sand inlet of the spray gun, and then enter the spray gun from the spray nozzle by another compressed air (input by the high-speed spray gun air pressure input air pipe 15) to accelerate the particles secondarily, and finally are sprayed out from the outlet of the spray gun. As shown in fig. 8, the detection result of the particle spraying speed of the sanding device is shown by the following speed test: the spraying speed of the tackifying particles with the particle diameters of 0.125-0.3 mm and 0.3-0.6 mm can reach about 50m/s under the air pressure of 0.5-0.7 MPa, and the design target of high-speed spraying is realized.
The embodiment discloses an optimal sanding decision simulation method based on train operation parameters. Firstly, establishing a sanding logic judgment hierarchical structure model based on train operation parameters through an AHP algorithm, wherein the sanding logic judgment hierarchical structure model comprises a scheme layer, a criterion layer and a target layer, and converting a sanding logic judgment problem into a sanding weight vector solving problem. After solving, whether the current train needs sanding or not can be autonomously judged according to the vector size, and the simulation method effectively avoids manual subjective intervention and improves the accuracy of sanding logic judgment. Secondly, based on a rolling simulation testing machine, simulating an influence test of train operation parameters on the optimal sanding amount, establishing a basic database of a plurality of operation parameters and the corresponding optimal sanding amount, and establishing a sanding amount optimal value function by adopting multi-element fitting. And (3) carrying out linear regression analysis on the influence test of air pressure on the sanding amount, establishing a linear relation between the air pressure and the sanding amount, and executing optimal sanding decision through electric elements such as an electromagnetic valve, an Air Pressure Controller (APC) and the like to realize sanding logic judgment and stepless regulation of the optimal sanding amount value. Compared with the traditional sanding amount adjusting modes such as manual experience, sanding amount grading adjustment and control and the like, the optimal sanding decision simulation method provided by the invention avoids subjectivity and hysteresis caused by manual experience intervention, and the control precision is as high as 0.1kg/min. Finally, the high-speed sanding device for detecting the optimal sanding simulation method has high-efficiency jetting performance, and consists of a sanding valve, a high-speed spray gun and other key parts, compressed air can accelerate particles once through a entrainment effect generated by the sanding valve, and a contraction type nozzle at the high-speed spray gun accelerates the particles secondarily. The sand spraying mode avoids energy loss generated when particles pass through the sand spraying pipe, adopts a two-section accelerating sand spraying mode, improves the particle spraying speed to about 50m/s, and improves the accuracy and the tackifying efficiency of sand spraying.
The invention and its embodiments have been described above by way of illustration and not limitation, and the invention is illustrated in the accompanying drawings and described in the drawings in which the actual structure is not limited thereto. Therefore, if one of ordinary skill in the art is informed by this disclosure, the structural mode and the embodiments similar to the technical scheme are not creatively designed without departing from the gist of the present invention.
Claims (8)
1. The optimal sanding decision simulation method based on train operation parameters is characterized by comprising the following steps of: the method comprises the following steps:
step (1): constructing a sanding logic judgment hierarchical structure model based on train operation parameters, and introducing an AHP algorithm to calculate a weight vector of a target so as to realize autonomous judgment of the sanding logic;
in the step (1), a hierarchical structure model of sanding logic judgment is established, specifically:
taking two schemes for decision-making, namely sanding or not sanding, as scheme layers of the hierarchical structure model;
taking locomotive acceleration, train running speed, creep rate, braking grade and traction grade as criterion layers of a hierarchical structure model;
taking a weight vector representing sanding logic as a target layer of the hierarchical structure model;
step (2): taking the adhesion coefficient as an evaluation criterion of the sanding effect, and establishing a basic database of a plurality of train operation parameters and corresponding optimal sanding amount;
in the step (2), a basic database of a plurality of train operation parameters and corresponding optimal sand spraying amount is established, and the method specifically comprises the following steps:
based on a rolling simulation testing machine, selecting a recovery standard of an adhesion coefficient of a wheel rail after sanding under a low adhesion working condition, and determining a testing scheme of influence of train operation parameters on the optimal sanding amount;
designing each train operation parameter as a multi-level orthogonal test table, adjusting the sanding amount under each group of tests, taking the adhesion coefficient as an evaluation criterion of the sanding effect, and enabling the maximum adhesion coefficient to correspond to the optimal sanding amount;
confirming the optimal sanding amount under each group of tests, and establishing a basic database of a plurality of train operation parameters and the corresponding optimal sanding amount;
step (3): and (3) establishing a sanding amount optimal value function by adopting multi-element fitting, calculating the optimal sanding amount under the comprehensive influence of train operation parameters, and outputting an optimal sanding decision to a lower computer by adjusting air pressure.
2. The optimal sanding decision simulation method based on train operation parameters of claim 1, wherein: in the step (1), the process of obtaining the weight vector specifically includes:
respectively constructing comparison matrixes from a criterion layer to a target layer and from a scheme layer to the criterion layer according to a nine-scale method and a weight function;
consistency testing is carried out on the comparison matrix among all the layers by adopting a consistency ratio method;
when the comparison matrix meets consistency test, a geometric average method introduced into an AHP algorithm is used for solving a sanding weight vector, and the method specifically comprises the following steps:
；
wherein,W i is the first to influence decisions in the criterion layeriThe weight vector of the individual factors is used,a ij to compare the first in the matrixiThe corresponding scale of the personal factors is with the firstjThe individual factors correspond to the ratio between the scales,a kj to compare the first in the matrixkThe corresponding scale of the personal factors is with the firstjThe individual factors correspond to the ratio between the scales,nthe number of factors affecting the sanding amount.
3. The optimal sanding decision simulation method based on train operation parameters of claim 2, wherein: in the step (1), the weight vector of the sanding logic is calculated, specifically:
；
wherein,Was a weight vector for the sanding logic,W A as a weight vector from the criterion layer to the target layer,W B weight vectors from the scheme layer to the criterion layer; and through calculation, if the value representing sanding in the target weight vector is larger than 0.5, the current train operation parameter needs to be sanded.
4. The optimal sanding decision simulation method based on train operation parameters of claim 3, wherein: step (3): adopting multi-element fitting to establish a sanding amount optimal value function, and calculating the optimal sanding amount under the comprehensive influence of train operation parameters, wherein the optimal sanding amount is specifically as follows:
based on a basic database of the optimal sanding amount, selecting an optimal value function type, and obtaining a fitting formula of train operation parameters and the optimal sanding amount through a least square method;
and calculating the optimal sanding amount under the current train operation parameters according to the optimal value function relation.
5. The optimal sanding decision simulation method based on train operation parameters of claim 4, wherein: in the step (3), after logic judgment and calculation of an optimal value, outputting an optimal sanding decision to a lower computer by adjusting air pressure, wherein the method specifically comprises the following steps:
performing linear regression analysis on the influence test of air pressure on sand scattering amount, and establishing a functional relation between the air pressure and the sand scattering amount;
calculating an air pressure value corresponding to the optimal sanding amount according to the air pressure and sanding amount functional relation;
and an optimal sanding decision is executed by adopting an electromagnetic valve and an air pressure controller APC, so that sanding logic judgment and sanding amount optimal value adjustment are realized.
6. The optimal sanding decision simulation method based on train operation parameters of claim 5, wherein: an optimal sanding decision simulation system based on train operation parameters is adopted, and comprises a train operation parameter detection system (14), an optimal sanding decision model and a sanding device;
the optimal sanding decision simulation method is characterized in that the optimal sanding decision simulation method is written into a computer (13) through a program, an upper computer of the computer (13) is a train operation parameter detection system (14), a lower computer of the computer (13) is an air pressure controller (5), and data are shared between the computer (13) and the train operation parameter detection system (14) and the air pressure controller (5) through data lines;
the sanding device comprises an air pressure generator (1), an air pressure controller (5), a sand box (9), a sanding valve (8), a sanding pipe (10), a high-speed spray gun (11) and a plurality of air pipes; the air pressure generator (1) is positioned at the front end of the sanding device, the air outlet (2) of the air pressure generator is connected to the air pressure controller (5) through an air pipe, and compressed air after being regulated flows through a flow divider (6) to be respectively input into a sanding valve (8) and a high-speed spray gun (11); the sand spreading valve (8) is arranged at the bottom of the sand box (9) and is used for sucking adhesion-promoting particles stored in the sand box (9); the sanding valve (8) and the high-speed spray gun (11) are connected through a sanding pipe (10).
7. The optimal sanding decision simulation method based on train operation parameters of claim 6, wherein: the high-speed sanding device adopts a two-stage accelerating sanding mode, the sanding valve (8) accelerates the tackifying particles once by utilizing the entrainment effect, and the high-speed spray gun (11) adopts a shrinkage nozzle to accelerate secondarily.
8. The optimal sanding decision simulation method based on train operation parameters of claim 6, wherein: the train operation parameter detection system (14) is used for collecting train operation parameters, and comprises an encoder and an acceleration sensor.
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