Simulation method for determining optimal sand spreading based on train operation parameters

Abstract

This invention relates to the technical field of mathematical modeling of optimal value functions, specifically to a simulation method for determining optimal sand application rates based on train operation parameters. The method includes the following steps: (1) constructing a hierarchical model of sand application logic judgment and introducing an AHP algorithm to calculate a target weight vector to realize autonomous sand application logic judgment; (2) constructing a basic database of many train operation parameters and their corresponding optimal sand application rates using adhesion coefficients as an evaluation criterion for sand application effectiveness; and (3) constructing an optimal value function of sand application rate using multivariate fitting, calculating the optimal sand application rate based on the comprehensive influence of the train operation parameters, and outputting the optimal sand application rate to the subordinate unit by adjusting the air pressure. The present invention provides a feasible simulation calculation method for autonomous sand application logic judgment and optimal sand application rate determination, significantly improving particle injection speed, sand application accuracy, and thickening efficiency.

Description

[Technical Field]

[0001] The present invention relates to the technical field of mathematical modeling of optimal value functions, and more particularly to a simulation method for determining optimal sand spreading based on train operation parameters. [Background technology]

[0002] To solve the common problem of low adhesion in railway systems, both at home and abroad, the method of scattering sand to increase the wheel-rail adhesion coefficient is often adopted. The principle is that a sand scattering device is installed in front of the pair of traction wheels of a locomotive to scatter viscosity-increasing particles at the wheel-rail interface, thereby increasing the shear resistance of the wheel-rail interface and removing contaminants from the rail surface, thereby achieving the purpose of increasing viscosity.

[0003] Currently, there are three significant problems with sand spreading in its current application. First, the logical judgment of sand spreading is highly subjective, mainly based on the locomotive driver's own experience when deciding whether to spread sand, which is prone to problems such as delayed sand spreading. Second, a fixed sand flow rate is used during the sand spreading process, and the flow rate cannot be adjusted. If the sand flow rate is too low, the locomotive's adhesion coefficient requirements cannot be met. If the sand flow rate is too high, it will not only waste particles but also worsen wheel-rail damage and even cause circuit insulation failures. Third, the particle injection speed of current sand spreading devices is too low. Under external interference, there is a large discrepancy between the amount of particles that actually enter the wheel-rail interface and create a thickening effect and the amount of sand spread. To address these issues, domestic and international experts have proposed adjusting the sand spreading rate based on the train's operating speed or acceleration. However, this method still has the following drawbacks: First, the flow rate is controlled in stages, resulting in low control accuracy. Second, there are many factors that affect the amount of sand spread, so determining how much sand to spread based on only one factor is one-sided.

[0004] As an important component of a sand spreading device, the sand spreading control system is an issue that must be resolved urgently by those skilled in the art. How to provide an optimal sand spreading determination simulation method based on train operation parameters that comprehensively analyzes the factors influencing the amount of sand spreading, automates the sand spreading logic judgment and the determination of the optimal amount of sand spreading, and greatly improves the accuracy of sand spreading and the thickening efficiency? Summary of the Invention [Problem to be solved by the invention]

[0005] This invention provides a simulation method for determining optimal sand spreading based on train operation parameters. It comprehensively analyzes the influence of train operation parameters on sand spreading amount, and uses the Analytic Hierarchy Process (AHP) algorithm and optimal value function modeling of sand spreading amount to provide a novel simulation method for sand spreading equipment's sand spreading logic autonomous judgment and optimal sand spreading amount determination. This simulation method performs feasibility and effectiveness detection for high-speed sand spreading equipment, greatly improving the sand spreading accuracy and thickening efficiency of particle injection speed. [Means for solving the problem]

[0006] The method for automatic sand spreading control based on train operation parameters according to the present invention includes: Step (1) constructs a hierarchical structure model of sand-spreading logic judgment based on train operation parameters, and introduces the AHP algorithm to calculate the target weight vector, thereby realizing sand-spreading logic autonomous judgment; Step (2) is to build a basic database of many train operation parameters and the corresponding optimal sand spreading amount, using the adhesion coefficient as a criterion for evaluating the effectiveness of sand spreading. The method includes a step (3) of constructing an optimal value function for the amount of sand spreading using multivariate fitting, calculating the optimal amount of sand spreading based on the overall influence of train operation parameters, and outputting the optimal sand spreading decision to the lower-level machine by adjusting the air pressure.

[0007] Preferably, in step (1), a hierarchical structure model of sand scattering logic judgment is constructed, specifically, A scheme that can be used to make two decisions, that is, whether to perform sand scattering or not, is defined as a scheme layer of the hierarchical structure model. The train operation parameters covering the locomotive acceleration, train running speed, creep rate, braking level and traction level are taken as the reference layer of the hierarchical structure model. The weight vector representing the sand sowing logic is set as the target layer of the hierarchical structure model.

[0008] Preferably, in step (1), the process of obtaining the weight vector specifically includes: According to the nine-scale method and weight function, the comparison matrix of the reference layer-target layer and the scheme layer-reference layer is constructed respectively; The consistency ratio method was used to verify the consistency of the comparison matrix between each hierarchy. If the comparison matrix satisfies the consistency verification, the geometric mean method in the AHP algorithm is introduced to obtain the sand scattering weight vector. Specifically, JPEG2025542051000002.jpg2842, where W i is the weight vector of the i-th element in the reference layer that influences the decision, and a ij is the ratio of the scale corresponding to the i-th element in the comparison matrix to the scale corresponding to the j-th element, and a kj is the ratio of the scale corresponding to the kth element in the comparison matrix to the scale corresponding to the jth element, and n is the number of influential elements of the sand amount.

[0009] Preferably, in step (1), calculating the weight vector of the sand sowing logic specifically includes: JPEG2025542051000003.jpg729, where W is the weight vector of the sand sowing logic, and W A is the weight vector of the reference layer-target layer, and W B is the weight vector of the scheme layer-reference layer, and if the value representing sand spreading in the weight vector of the target layer is greater than 0.5, it indicates that there is a strong demand for sand spreading, and a sand spreading operation is performed.

[0010] Preferably, in step (2), the construction of a basic database of many train operation parameters and corresponding optimal sand spreading amounts includes, specifically: Based on the rolling simulation test machine, the recovery criteria for wheel-rail adhesion coefficient after sand spreading under low adhesion working conditions were selected, and the test scheme for the influence of train operation parameters on the optimal amount of sand spreading was determined. The operating parameters of each train are designed into a multi-level orthogonal test table, and the sand amount for each set of tests is adjusted. The adhesion coefficient is used as the evaluation criterion for the sand-spreading effect, and the maximum adhesion coefficient corresponds to the optimal sand-spreading amount. The goal is to confirm the optimal sand spreading amount for each set of tests and to build a basic database of many train operation parameters and the corresponding optimal sand spreading amount.

[0011] Preferably, in step (3), constructing an optimal value function of the sand spreading amount using multivariate fitting and calculating the optimal sand spreading amount according to the comprehensive influence of the train operation parameters includes, specifically: Based on the basic database of the optimal sand spreading amount, the optimal value function type is selected, and the fitting formula between the train operation parameters and the optimal sand spreading amount is found using the least squares method. The optimal sand amount is calculated based on the optimal value function relationship under the current train operation parameters.

[0012] Preferably, in step (3), after performing logical judgment and optimal value calculation, outputting the optimal sand scattering decision to the subordinate machine by adjusting the air pressure, specifically, A linear regression analysis was performed to test the effect of atmospheric pressure on the amount of sand spread, and a functional relationship between the two was established. Calculate the atmospheric pressure value corresponding to the optimum sand amount based on the functional relationship between atmospheric pressure and sand amount; The optimum sand sprinkling amount is determined by using electrical elements such as a solenoid valve and a pressure controller, and the sand sprinkling logic decision and the optimum sand sprinkling amount can be adjusted.

[0013] Preferably, a simulation system for determining optimal sand spreading based on train operation parameters is used, and the simulation system for determining optimal sand spreading based on train operation parameters includes a train operation parameter detection system, an optimal sand spreading decision model, and a sand spreading device; The optimal sand spreading simulation method is written into a laptop computer as a program. The upper computer is a train operation parameter detection system, and the lower computer is a pressure controller. Both the upper and lower computers share data with the computer via data lines. The sand-spreading device includes an air pressure generator, an air pressure controller, a sand box, a sand-spreading valve, a sand-spreading pipe, a high-speed spray gun, and a number of air pipes. The air pressure generator is located at the tip of the sand-spreading device, and its exhaust port is connected to the air pressure controller via the air pipe. The compressed air is regulated and then input into the sand-spreading valve and the high-speed spray gun through a flow divider. The sand-spreading valve is attached to the bottom of the sand box and is used to wind up the thickened particles stored in the sand box. The sand-spreading valve and the high-speed spray gun are connected via the sand-spreading pipe.

[0014] Preferably, the high-speed sand spreading device uses a two-stage acceleration sand spreading mode, the sand spreading valve uses the winding effect to perform primary acceleration on the thickened particles, and the high-speed spray gun adopts a contracting nozzle to perform secondary acceleration.

[0015] Preferably, the train operation parameter detection system is used to collect train operation parameters and includes an encoder and an acceleration sensor. [Effects of the Invention]

[0016] The beneficial effects of the present invention are as follows:

[0017] (1) This invention uses the AHP algorithm to build a hierarchical model of sand-spreading logic judgment based on train operation parameters, including a scheme layer, a reference floor, and a target floor. The problem of sand-spreading logic judgment is transformed into a problem of obtaining a sand-spreading weight vector, and the magnitude of the vector can be used to autonomously determine whether the current train needs sand-spreading. This simulation method effectively avoids subjective human interference and improves the accuracy of sand-spreading logic judgment.

[0018] (2) This invention uses a rolling simulation tester to simulate the effects of train operating parameters on the optimal sand application rate. A basic database of many operating parameters and their corresponding optimal sand application rates is established, and an optimal sand application rate function is constructed using multivariate fitting. Linear regression analysis is performed on the test of the effect of air pressure on sand application rate, a linear relationship is established between the two, and optimal sand application rate is determined using electrical elements such as a solenoid valve and a pneumatic controller, achieving sand application logic judgment and stepless adjustment control of the optimal sand application rate. Compared to traditional sand application rate control methods such as artificial experience and step-by-step adjustment control of sand application rate, the optimal sand application rate determination simulation method proposed in this invention avoids the subjectivity and delay caused by the interference of artificial experience, and achieves high control accuracy, reaching 0.1 kg / min.

[0019] (3) In this invention, the high-speed sand-spreading device for finding the optimal sand-spreading simulation method is composed of major components such as a sand-spreading valve and a high-speed spray gun, and the compressed air primarily accelerates the particles through the winding effect of the sand-spreading valve, and the contracting nozzle of the high-speed spray gun secondary accelerates the particles. This sand-spreading method avoids the energy loss that occurs when the particles pass through the sand-spreading pipe, and adopts a two-stage acceleration sand-spreading mode, increasing the particle injection speed to about 50 m / s, improving the accuracy of sand-spreading and the thickening efficiency.

[0020] (4) The sand-spreading particles of the automatic sand-spreading control device based on train operation parameters are made of new thickening hard particles. Compared with ordinary quartz sand particles, the new thickening hard particles have a 30% higher thickening effect and can reduce the amount of particles used by 87.5%. [Brief explanation of the drawings]

[0021] [Figure 1] FIG. 1 is a schematic diagram of an automatic sand-spreading control method based on train operation parameters in an embodiment. [Figure 2] 1 is a flowchart of a method for calculating a sand sowing logical weight vector in an embodiment. [Figure 3] FIG. 1 is a schematic diagram of a hierarchical structure model of sand scattering logic judgment based on train operation parameters constructed by the AHP algorithm in an embodiment. [Figure 4] FIG. 1 is a schematic diagram of the functional relationship between atmospheric pressure and sand deposition rate constructed after performing linear regression analysis on the test of the effect of atmospheric pressure on sand deposition rate in the examples. [Figure 5] 1 is a structural schematic diagram of a high-speed sand spreading device for detecting the effectiveness of the optimal sand spreading determination simulation method in an embodiment. FIG. [Figure 6] 1 is a schematic diagram of the assembly structure of the sand-spreading valve and sand box in the embodiment. [Figure 7] 1 is a structural schematic diagram of a high-speed spray gun according to an embodiment. [Figure 8] FIG. 10 is a schematic diagram showing the detection results of particle spray velocity of the sand spreader in the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0022] In order to better understand the contents of the present invention, the present invention will be described in detail in conjunction with the drawings and examples, which should be understood as being merely for the purpose of illustrating the present invention, and not for the purpose of limiting it.

[0023] Example As shown in FIG. 1, the present invention provides a simulation method for determining optimal sand spreading based on train operation parameters, which includes the following steps:

[0024] Step (1): Build a hierarchical model of sand-spreading logic judgment based on train operation parameters, calculate the weight vector of the sand-spreading logic using the AHP algorithm, realize autonomous judgment of the sand-spreading logic, and determine whether the train currently needs to be sanded based on the magnitude of the weight vector, as shown in Figure 2. Specifically, the process is as follows:

[0025] First, as shown in Figure 3, a hierarchical model of sand-spreading logic decision-making based on train operation parameters is constructed. The scheme that can be used to make the two decisions of whether to spread sand or not is defined as scheme layer A of the hierarchical model. The criteria or factors that affect the sand-spreading decision, covering the locomotive acceleration, train running speed, creep rate, braking level, and traction level, are defined as criteria layer B of the hierarchical model. The weight vector representing the sand-spreading logic is defined as target layer C of the hierarchical model.

[0026] Then, the comparison matrix of scheme layer-reference layer and reference layer-target layer is constructed according to the nine-scale method and weight function, respectively.

[0027] (1) Scheme layer - Reference layer: The influence factors of the reference layer, such as acceleration, creep rate, speed, braking level, and traction level, constantly change during the locomotive's movement. Therefore, according to the weighting function of the influence of each factor on sand spreading, which was constructed by industry experts, the scheme layer - reference layer comparison matrix B N teeth,

[0028] JPEG2025542051000004.jpg24128,

[0029] where N=1, 2, 3, 4, 5 represent the five influencing factors: acceleration, creep rate, speed, braking level, and traction level, respectively, and F(X N ) is a weighting function of the influence of each influencing factor on sand spreading in the reference layer.

[0030] (2) Baseline-target layer: Railway system industry experts used the nine-scale method to construct a comparison matrix from each element of the base layer to the target layer. Specifically, the experts used their own experience to score the impact of each element of the base layer that affects sand spreading, with 1-9 representing whether the element is unimportant to the target or absolutely important, and scores from 1 to 9 representing increasing impact on the target. Each influencing element was further compared two at a time. Table 1 shows the comparative scale values ​​for each parameter of the base layer, and a base-target layer comparison matrix A was constructed. The base-target layer scale table (Table 1) and comparison matrix are as follows:

[0031] JPEG2025542051000005.jpg89147JPEG2025542051000006.jpg3970

[0032] Then, before hierarchical ranking, the comparison matrix of the base layer-target layer and the scheme layer-base layer is subjected to consistency verification using the consistency ratio method to determine whether the consistency ratio CR is less than 0.1. The calculation is as follows: JPEG2025542051000007.jpg1219JPEG2025542051000008.jpg1223JPEG2025542051000009.jpg1254, Here, CI and RI are the consistency index and random consistency index, respectively, and λ is the maximum feature value of the matrix. Calculations show that the comparison matrices A and B are not consistent matrices, but they satisfy the matrix consistency test, indicating that the matrix structure is reasonable.

[0033] Finally, after satisfying the consistency verification, the geometric mean method in the AHP algorithm is used to perform the hierarchical ordering calculation, which includes the hierarchical single ordering calculation and the hierarchical total ordering calculation. The hierarchical single ordering calculation is to obtain the weight vector of the base layer-target layer matrix A and the scheme layer-base layer matrix B, respectively. The hierarchical total ordering calculation is to obtain the weight vector of the sand sowing logic, which is calculated as follows: JPEG2025542051000010.jpg2842JPEG2025542051000011.jpg729, where W i is the weight vector of the i-th element in the reference layer that influences the decision, and a ij is the ratio of the scale corresponding to the i-th element in the comparison matrix to the scale corresponding to the j-th element, and a kj is the ratio of the scale corresponding to the kth element in the comparison matrix to the scale corresponding to the jth element, and n is the number of influential elements of the sand sowing amount. W is the weight vector of the sand sowing logic, and W A is the weight vector of the reference layer-target layer, and W B is the scheme layer-reference layer weight vector.

[0034] The sand-spreading logic decision is to calculate the weight vector of the target layer, and if the weight vector value representing sand-spreading is greater than 0.5, it indicates a strong demand for sand-spreading. The optimal sand-spreading decision simulation method controls the amount of sand-spreading by adjusting the air pressure, and if W>0.5, outputs the optimal sand-spreading decision to the lower-level air pressure controller, which then executes the sand-spreading operation.

[0035] Step (2): Using a rolling simulation test machine, test the influence of train operation parameters on the amount of sand spreading. Using the adhesion coefficient as the evaluation criterion for the effectiveness of sand spreading, build a basic database of many train operation parameters and the corresponding optimal sand spreading amounts, which will serve as the basis for a model for determining the optimal sand spreading. Specifically, this is as follows:

[0036] First, a test scheme was created to examine the effect of train operating parameters on the amount of sand scattered. Using a two-wheel pair rolling simulation test machine developed autonomously by the laboratory, the train's operating speed was simulated by setting the sample rotation speed, the rotation speed change curve function was set to simulate the acceleration during train operation, and various acceleration and deceleration values ​​were input to simulate the traction level when the train starts and the braking level during braking. The creep rate was calculated based on the difference in rotation speed between the upper and lower samples of the wheel pair, as follows:

[0037] JPEG2025542051000012.jpg1657

[0038] where V1 and V2 are the linear velocities of the wheel and rail samples, respectively, ω1 and ω2 are the rotational speeds (r / min) of the wheel and rail samples, respectively, and d1 and d2 are the diameters (mm) of the wheel and rail samples, respectively.

[0039] Next, various low adhesion situations during actual train operation were simulated, and the recovery criteria for wheel-rail adhesion coefficient after sand spreading under low adhesion working conditions were selected. The maximum available adhesion coefficient of the general wheel-rail interface is greater than 0.2, which can meet the demands of train traction braking. The operating parameters of each train were designed into a multi-level orthogonal test table, and the amount of sand spreading in each set of tests was adjusted. The adhesion coefficient was used as the evaluation criterion for sand spreading effectiveness, with the maximum adhesion coefficient corresponding to the optimal value of sand spreading. Finally, the optimal sand spreading amount corresponding to each set of train operation parameters was confirmed, and a basic database of many train operation parameters and the corresponding optimal sand spreading amount was established. Table 2 shows the optimal values ​​of sand spreading amount.

[0040] JPEG2025542051000013.jpg66147Step (3): Based on the basic database of train operation parameters and the corresponding optimal sand spreading amount, an optimal value function for sand spreading amount is constructed using multivariate fitting, and the optimal sand spreading amount based on the overall influence of train operation parameters is calculated. A linear regression analysis is performed on the test of the influence of air pressure on sand spreading amount to construct a functional relationship between air pressure and sand spreading amount, calculate the corresponding air pressure value, and adjust the air pressure to output the optimal sand spreading decision to the lower-level machine. Specifically, First, construct the optimal value function of sand spreading amount using multivariate fitting, select the optimal value function type, and use the least squares method to find the fitting formula between the train operation parameters and the optimal sand spreading amount, and calculate the optimal sand spreading amount based on the functional relationship. The principle is as follows: JPEG2025542051000014.jpg6128, where φ k (x) is a set of linearly independent functions related to train operation parameters, and a k is the corresponding undetermined coefficient (k=1, 2, 3, 4, 5), and the fitting criterion is the test result y i (i=1, 2, ..., n) and f(x i The goal is to minimize the sum of squares of the distance between the actual train operation parameters and the sand amount. By approximating the actual train operation parameters and the sand amount function as closely as possible with an accuracy suited to the construction application, and analyzing and observing the change rules, the optimal sand amount for the train operation parameters in a certain low-adhesion work situation can be calculated.

[0041] Next, a linear regression analysis is performed on the test of the effect of atmospheric pressure on sand deposition rate to establish a functional relationship between the two. The process of establishing the functional relationship between atmospheric pressure and sand deposition rate is the same as above, and will not be further described here. Figure 4 shows the functional relationship between atmospheric pressure and sand deposition rate established after performing a linear regression analysis on the test of the effect of atmospheric pressure on sand deposition rate. The results show that there is a linear relationship between atmospheric pressure and sand deposition rate, specifically as follows:

[0042] JPEG2025542051000015.jpg853Here, Q is the amount of sand sprinkled, and its unit is kg / min, and P is the pressure of the compressed air, and its unit is MPa. Here, R 2 The value of is 0.88, and the closer it is to 1, the better the fitting of the linear regression curve to the observed values, indicating that it approaches the actual functional relationship between the amount of sand sprinkled and atmospheric pressure.

[0043] Finally, the atmospheric pressure value corresponding to the optimal sanding amount is calculated based on the functional relationship between atmospheric pressure and sanding amount, and the optimal sanding decision is made using electrical elements such as a solenoid valve and an atmospheric pressure controller, thereby realizing sanding logic judgment and optimal sanding amount adjustment. Here, the atmospheric pressure controller can automatically output compressed air with the required accurate pressure (0-2.0 MPa) based on the optimal sanding decision, eliminating the need for manual pressure adjustment during the implementation of this simulation method.

[0044] This embodiment uses a simulation system for determining optimal sand spreading based on train operation parameters, which includes a train operation parameter detection system 14, an optimal sand spreading determination model, and a sand spreading device (as shown in FIG. 5 ); The optimal sand-spreading decision-making simulation method is written into a laptop computer 13 as a program. The host computer is a train operation parameter detection system 14, and the subordinate computer is an air pressure controller 5. Both the host and subordinate computers share data with the computer via data lines. The optimal sand-spreading decision-making simulation method is used to calculate the amount of air pressure corresponding to the optimal amount of sand needed for train operation parameters in low-adhesion operation situations. Its specific functions are to first input train operation parameters into a hierarchical structure model for sand-spreading logic decision-making to determine whether to sprinkle sand, then calculate the optimal amount of sand given the train operation parameters based on the optimal value function of the sand-spreading amount, and output the optimal sand-spreading decision.

[0045] The train operation parameter detection system 14 is used to collect train operation parameters and includes an encoder and an acceleration sensor.

[0046] The sand spreader includes an air pressure generator 1, an air pressure controller 5, a sand box 9, a sand spreader valve 8, a sand spreader pipe 10, a high-speed spray gun 11, and a number of air pipes. The air pressure generator 1 is located at the front end of the sand spreader, and its exhaust port 2 is connected to the air pressure controller 5 via an air pipe. The air pressure of the compressed air is automatically adjusted by the air pressure control device, and is input through a flow divider 6 to the air pressure input pipe 7 of the sand spreader valve at the rear end of the sand spreader and the air pressure input pipe 15 of the high-speed spray gun. The sand box 9 is used to store thickening particles used in sand spreading and is fixed to the sand box holder, the sand spreading valve 8 is attached to the bottom of the sand box 9 and is used to wind up the thickening particles stored in the sand box 9, the sand spreading pipe 10 is connected to the sand spreading valve 8 and the high-speed spray gun 11 respectively and is used to transport the mixed jet of thickening particles and compressed air, the high-speed spray gun 11 is used to spray the thickening particles, the air supply pipe is connected to each unit and is used to transport compressed air and is all connected with quick connectors, the other end of which has sealing tape wrapped around the threads. The installation, connection method and detailed roles and functions of the main components of the sand spreading device are explained below.

[0047] The air pressure generator (compressor) 1 is used to provide clean, dry compressed air to the entire sand spreading device.

[0048] The air pressure controller 5 is used to automatically output the required air pressure and includes 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 to the compressor via an air pipe, and the air pressure output interface 5 is connected to the sand dispensing valve and the air inlet of the high-speed spray gun via an air pipe after passing through a flow divider 6. The air pipe connections are all quick connectors to ensure good airtightness. The control signal input interface is connected to the computer and software via a data line 12.

[0049] The sand box 9 is used to store sand thickening particles, and includes a sand box cover and a sand observation port, so that the position of the particles inside the sand box can be observed at any time and replenished in a timely manner.

[0050] Regarding the sand spreader valve 8, as compressed air passes through the sand spreader valve, it winds up the thickened particles in the sand box. Figure 6 shows the structural assembly diagram of the sand spreader valve and sand box, including the sand suction port 16 of the sand spreader valve, the sand inlet port 17 of the sand spreader valve (connected to the sand spreader valve's air pressure input pipe 7), the sand spreader valve throat 18, and the sand discharge port 19 of the sand spreader valve. As the compressed air enters the sand spreader valve through the air inlet and passes through the sand spreader valve's throat (the throat is the point where the diameter of the pipe is smallest), the air velocity increases and the pressure decreases, forming a certain negative pressure at the throat and causing a winding flow. The air from the sand suction port constantly sucks up the thickened particles in the sand box into the cavity inside the sand spreader valve through the winding action, and after being accelerated by the compressed air, it enters the sand spreader pipe through the sand discharge port.

[0051] The sand-spreading pipe 10 is used to deliver a mixed jet of compressed air and thickened particles. One end of the sand-spreading pipe is connected to the sand outlet of the sand-spreading valve, and the other end is connected to the sand inlet of the high-speed spray gun. Both ends are fastened with stainless steel clamps to ensure good airtightness.

[0052] The high-speed spray gun 11 is used to perform secondary acceleration on the particle jet, increasing the particle injection velocity. Figure 7 shows a schematic diagram of the high-speed spray gun, including the sand inlet 20, the nozzle 21, the mixing chamber 22, and the outlet 23. Particles and compressed air enter the mixing chamber through the sand inlet, while the other compressed air (input through the air pressure input duct 15) enters the spray gun through the nozzle, subjecting the particles to secondary acceleration and ultimately ejecting them from the outlet. Figure 8 shows the particle injection velocity measurement results of the sand spreader. After velocity testing, it was found that under pressures of 0.5-0.7 MPa, the injection velocity of thickened particles with particle sizes of 0.125-0.3 mm and 0.3-0.6 mm both reached approximately 50 m / s, achieving the design goal of high-speed injection.

[0053] This embodiment discloses a simulation method for determining optimal sanding based on train operation parameters. First, a hierarchical sanding logic decision model based on train operation parameters is constructed using the AHP algorithm, including a scheme layer, a reference floor, and a target floor. The sanding logic decision problem is transformed into a problem of determining a sanding weight vector. After the vector is determined, an autonomous decision is made based on the magnitude of the vector to determine whether the current train needs sanding. This simulation method effectively avoids subjective human interference and improves the accuracy of sanding logic decisions. Next, a rolling simulation tester is used to simulate the effects of train operation parameters on the optimal sanding amount. A basic database of many operation parameters and their corresponding optimal sanding amounts is built, and an optimal sanding amount function is constructed using multivariate fitting. A linear regression analysis is performed on the effects of air pressure on the sanding amount, and a linear relationship is established between the two. The optimal sanding decision is then made using electrical elements such as a solenoid valve and a pneumatic controller, achieving stepless adjustment of the sanding logic decision and the optimal sanding amount. Compared with traditional sand dosage control methods, such as artificial experience and step-by-step sand dosage adjustment control, the optimal sand dosage determination simulation method proposed in this invention avoids the subjectivity and delay caused by the interference of artificial experience, and has a high control accuracy of 0.1 kg / min. Finally, the high-speed sand-spreading device used to determine the optimal sand-spreading simulation method in this invention has efficient spraying performance and is composed of major components such as a sand-spreading valve and a high-speed spray gun. Compressed air primarily accelerates the particles through the winding effect of the sand-spreading valve, and the contracting nozzle of the high-speed spray gun secondarily accelerates the particles. This sand-spreading method avoids energy loss caused by particles passing through the sand-spreading pipe and adopts a two-stage acceleration sand-spreading mode, increasing the particle spray speed to approximately 50 m / s, improving the accuracy of sand-spreading and thickening efficiency.

[0054] The above description of the present invention and its embodiments is illustrative, but the description is not limitative, and the drawings are merely examples of the present invention, and the actual structure is not limited thereto. Therefore, any structural forms and embodiments similar to the technical solutions designed by a person skilled in the art based on the teachings of the present invention without departing from the creative spirit of the present invention and without any creative effort should fall within the scope of protection of the present invention. [Explanation of symbols]

[0055] 1 - atmospheric pressure generator, 2 - exhaust port, 3 - atmospheric pressure input interface, 4 - atmospheric pressure output interface, 5 - atmospheric pressure controller, 6 - flow diverter, 7 - atmospheric pressure input pipe of sand spreader valve, 8 - sand spreader valve, 9 - sand box, 10 - sand spreader pipe, 11 - high-speed spray gun, 12 - data line, 13 - laptop, 14 - train operation parameter detection system, 15 - atmospheric pressure input pipe of high-speed spray gun, 16 - sand suction port of sand spreader valve, 17 - air inlet of sand spreader valve, 18 - throat part of sand spreader valve, 19 - sand discharge port of sand spreader valve, 20 - sand inlet of high-speed spray gun, 21 - nozzle of high-speed spray gun, 22 - mixing chamber of high-speed spray gun, 23 - outlet of high-speed spray gun.

Claims

1. A simulation method for determining optimal sand spreading based on train operation parameters, comprising: Step (1) of constructing a hierarchical structure model of sand scattering logic judgment based on train operation parameters, and introducing an AHP algorithm to calculate a target weight vector, thereby realizing sand scattering logic autonomous judgment; Step (2) of constructing a basic database of many train operation parameters and the corresponding optimal sand spreading amount, using the adhesion coefficient as an evaluation criterion for the sand spreading effect; A simulation method for determining optimal sand spreading based on train operation parameters, characterized by including a step (3) of constructing an optimal value function for the amount of sand spreading using multivariate fitting, calculating the optimal amount of sand spreading based on the overall influence of train operation parameters, and outputting the optimal sand spreading decision to the lower-level machine by adjusting the air pressure.

2. In step (1), a hierarchical structure model of sand-spreading logic judgment is constructed. Specifically, The two decision schemes, whether to perform sand scattering or not, are set as the scheme layer of the hierarchical structure model. The locomotive acceleration, train running speed, creep rate, braking level and traction level are set as the reference layer of the hierarchical structure model. Step (1) is to set a weight vector representing the sand sowing logic as a target layer of a hierarchical structure model; A simulation method for determining optimal sand spreading based on train operation parameters according to claim 1.

3. In step (1), the process of obtaining the weight vector is specifically as follows: According to the nine-scale method and weight function, construct the comparison matrix of the reference layer-target layer and the scheme layer-reference layer respectively; The consistency ratio method was used to verify the consistency of the comparison matrix between each hierarchy. If the comparison matrix satisfies the consistency verification, the geometric mean method in the AHP algorithm is introduced to obtain the sand scattering weight vector, specifically: and Here, W ｉ is the weight vector of the i-th element influencing the decision in the reference layer, and a ｉｊ is the ratio of the scale corresponding to the i-th element in the comparison matrix to the scale corresponding to the j-th element, and a ｋｊ is the ratio of the scale corresponding to the kth element in the comparison matrix to the scale corresponding to the jth element, and n is the number of influential elements of the sand amount. A simulation method for determining optimal sand spreading based on train operation parameters according to claim 2.

4. In step (1), calculating the weight vector of the sand sowing logic is specifically and where W is the weight vector of the sand sowing logic, and W Ａ is the weight vector of the reference layer-target layer, and W Ｂ is a weight vector of the scheme layer - the reference layer, and according to calculation, if the value representing sand spreading in the target weight vector is greater than 0.5, it indicates that sand spreading is required under the current train operation parameters. A simulation method for determining optimal sand spreading based on train operation parameters according to claim 3.

5. In step (2), a basic database of many train operation parameters and the corresponding optimal sand spreading amount is constructed. Specifically, Based on the rolling simulation test machine, the recovery criteria for wheel-rail adhesion coefficient after sand spreading under low adhesion working conditions were selected, and the test scheme for the influence of train operation parameters on the optimal amount of sand spreading was determined. The operating parameters of each train are designed into a multi-level orthogonal test table, and the sand amount for each set of tests is adjusted. The adhesion coefficient is used as the evaluation criterion for the sand-spreading effect, and the maximum adhesion coefficient corresponds to the optimal sand-spreading amount. Step (2) is to confirm the optimal sand application rate for each set of tests and build a basic database of many train operation parameters and the corresponding optimal sand application rates; A simulation method for determining optimal sand spreading based on train operation parameters according to claim 4.

6. Specifically, step (3) of constructing an optimal value function of the sand spreading amount using multivariate fitting and calculating the optimal sand spreading amount based on the overall influence of train operation parameters includes: Based on the basic database of the optimal sand spreading amount, the optimal value function type is selected, and the fitting formula between the train operation parameters and the optimal sand spreading amount is found using the least squares method. The optimum amount of sand to be spread is calculated based on the optimum value function relationship under the current train operation parameters. A simulation method for determining optimal sand spreading based on train operation parameters according to claim 5.

7. In step (3), after performing logical judgment and optimal value calculation, the optimal sand scattering decision is output to the subordinate machine by adjusting the air pressure. Specifically, A linear regression analysis was performed to test the effect of atmospheric pressure on the amount of sand spread, and a functional relationship between the two was established. Calculate the atmospheric pressure value corresponding to the optimum sand amount based on the functional relationship between atmospheric pressure and sand amount; The optimum sand sprinkling decision is made using a solenoid valve and an air pressure controller APC, and the sand sprinkling logic decision and the optimum sand sprinkling amount are realized. A simulation method for determining optimal sand spreading based on train operation parameters according to claim 6.

8. A simulation system for determining optimal sand spreading based on train operation parameters is used, and the simulation system for determining optimal sand spreading based on train operation parameters includes a train operation parameter detection system (14), an optimal sand spreading decision model, and a sand spreading device; The optimum sand spreading determination simulation method is written into a computer (13) by a program, the host computer of the computer (13) is a train operation parameter detection system (14), the subordinate computer of the computer (13) is an air pressure controller (5), and data is shared between the computer (13), the train operation parameter detection system (14), and the air pressure controller (5) via a data line. The sand-spreading device includes an air pressure generator (1), an air pressure controller (5), a sand box (9), a sand-spreading valve (8), a sand-spreading pipe (10), a high-speed spray gun (11), and a plurality of air pipes. The air pressure generator (1) is located at the tip of the sand-spreading device, and its exhaust port (2) is connected to the air pressure controller (5) via the air pipe. The compressed air is adjusted and then input to the sand-spreading valve (8) and the high-speed spray gun (11) through a flow divider (6). The sand-spreading valve (8) is attached to the bottom of the sand box (9) and is used to wind up thickened particles stored in the sand box (9). The sand-spreading valve (8) and the high-speed spray gun (11) are connected via the sand-spreading pipe (10). A simulation method for determining optimal sand spreading based on train operation parameters according to claim 7.

9. The high-speed sand spreading device uses a two-stage acceleration sand spreading mode, in which the sand spreading valve (8) uses a winding effect to perform primary acceleration of the thickened particles, and the high-speed spray gun (11) uses a contracting nozzle to perform secondary acceleration. A simulation method for determining optimal sand spreading based on train operation parameters according to claim 8.

10. The train operation parameter detection system (14) is used to collect train operation parameters and is characterized by including an encoder and an acceleration sensor. A simulation method for determining optimal sand spreading based on train operation parameters according to claim 8.

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