Point rail profile shape parameter dynamics optimization method considering vehicle profile shape evolution

Abstract

The invention discloses a point rail profile parameter dynamics optimization method considering vehicle profile evolution, which comprises the following steps of: establishing a point rail profile parameterization reconstruction module considering fixed frog point rail profile key characteristic parameters and a wheel tread profile set module considering wheel tread profile evolution at different operation stages of a train; combining the wheel tread profile and the point rail profile with a vehicle-fixed frog coupling dynamic model, selecting key control parameters of the point rail profile, setting a plurality of first preset conditions, taking dynamic parameters of the wheel-rail dynamic response as evaluation indexes, and comprehensively comparing the influence of the wheel tread profile and the point rail profile parameters on each wheel-rail dynamic response, so as to determine the wheel-rail dynamic response. And calculating and comparing dynamic response parameters of each scheme to obtain an optimal scheme of the point rail profile.

Description

Technical Field

[0001] The present invention relates to the field of track structures, and particularly to a dynamic optimization method for the profile parameters of a movable point frog considering the evolution of the wheel profile. Background Art

[0002] The fixed frog is the main type of turnout frog in heavy-haul and speed-up railways in China. The profile parameters of the rails in the frog area have a very significant impact on the wheel-rail matching performance, train operation characteristics, and the service life of the frog. Therefore, it is particularly important to adopt a reasonable parametric optimization design for the profile of the frog rails. At the same time, during the operation of the train, the wheel profile wears intensively with the service time, and the wheel profile also gradually evolves as the wear increases. The evolution of the wheel profile and the complex rail profile changes in the fixed frog area make the variation laws of the wheel-rail contact relationship and the wheel-rail dynamic interaction in the frog area more complex, seriously affecting the driving safety and stability, and the service life of the train wheels and the fixed frog.

[0003] At present, for the turnout area, considering the comprehensive changes in wheel profiles and the complex structural characteristics of turnouts, scholars at home and abroad have carried out a large number of related studies on the optimization of rail profiles in the turnout area. Among them, Casanueva et al. predicted the wear of freight train wheel profiles based on the Archard wear model. By analyzing the influence laws of geometric and stiffness non-uniformity of turnout structures on wheel wear when passing through turnouts, it was shown that the rail profile of the turnout had the greatest influence on the wear of wheel profiles. Lee J et al. classified 15,000 sets of wheel profiles with different wear degrees collected during the actual operation stage of Class I railways in the United States through probability statistics methods based on the wear degree of the wheel tread, and formed a five-level classification standard for wheel tread profiles based on the wear degree of the wheel tread, providing data support for various studies of the wheel-rail system. Based on this classification standard, the author further proposed three geometric parameters: the longitudinal slope of the switch rail, the height difference between the switch rail and the wing rail at the start and end sections of the wheel load transition, and set 30 sets of schemes for optimization design. Considering the occurrence probability of different wear degrees of wheel profiles, 400 sets of different wheel tread profiles were selected according to the five-level wear degree, and the wheel impact response was evaluated from the change in the position of the wheel-rail contact point in the wheel load transition section and the vertical movement trajectory of the wheel along the line, and then the optimal scheme was obtained. Xu Jingmang et al. optimized the frog profile based on the height difference required for the wheel tread to contact the wing rail and the switch rail simultaneously when the train passed through a fixed frog, effectively reducing the structural irregularity and wheel-rail impact. Zhang Pengfei et al. optimized the rail profile in the turnout area by taking the good wheel-rail contact in the turnout area as the objective function through on-site measurement of turnout profile data. Chang WH et al. optimized the profile of the switch rail by selecting the height difference between the typical section of the switch rail tip and the stock rail as the optimization parameter, with the optimization objectives of improving the stability of the train passing through the turnout in the straight and lateral directions and reducing the wheel-rail dynamic response. D Nicklisch comprehensively considered the change in the wheel tread profile and optimized the turnout gauge widening parameter, and compared and analyzed the rail wear and force characteristics before and after optimization. Wang P et al. optimized the rail profile in the switch area of high-speed railways, with the optimization objective of reducing the difference in rolling circle radius, and used the improved sequential quadratic programming method to optimize the rail profile of high-speed turnouts.

[0004] Most of the methods adopted in existing research are to separately carry out the evolution of wheel tread wear and the optimization of the fixed frog rail profile. However, there are few research methods that comprehensively consider the evolution of the wheel tread profile, parameterize the optimization of the rail profile by adjusting the key design parameters of the frog rail profile, and then improve the wheel-rail relationship in the frog area. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to propose a method that comprehensively considers the wheel tread wear and the optimization of the fixed frog rail profile, reconstructs the rail profile by adjusting the key design parameters of the frog rail profile, and realizes the optimal selection of the switch rail profile by studying the influence of the evolution of the wheel tread profile wear and the change of the switch rail profile parameters on the wheel-rail dynamic characteristics in the fixed frog area.

[0006] To achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0007] A dynamic optimization method for the parameters of the switch point profile considering the evolution of the vehicle profile includes:

[0008] Obtain the geometric characteristic parameters of the fixed frog rail profile, and construct a parametric reconstruction module for the switch point profile based on the geometric characteristic parameters of the fixed frog rail profile;

[0009] And obtain the wheel tread profile data of the train in different wear states when the operating mileage increases at equal intervals, and construct a wheel tread profile set module based on the wheel tread profile data;

[0010] Obtain a plurality of first preset conditions, and input the first preset conditions into the parametric reconstruction module of the switch point profile one by one to obtain first simulation data corresponding to each first preset condition. The first preset condition is configured as the change information of the switch point profile design parameters;

[0011] Organize the plurality of first simulation data into a first simulation data set;

[0012] And obtain a plurality of second preset conditions, and input the second preset conditions into the wheel tread profile set module one by one to obtain second simulation data corresponding to each second preset condition. The second preset condition is configured as the stage information of the preset mileage interval of the train within the preset cumulative operating mileage range;

[0013] Organize the plurality of second simulation data into a second simulation data set;

[0014] Match the first simulation data in the first simulation data set with the second simulation data in the second simulation data set to obtain a plurality of working condition simulation groups. Each working condition simulation group includes a first simulation data and a second simulation data;

[0015] Construct a vehicle-fixed frog coupling dynamics model;

[0016] Input the plurality of working condition simulation groups into the vehicle-fixed frog coupling dynamics model in sequence to obtain the initial dynamics simulation results corresponding to each working condition simulation group;

[0017] Compare the plurality of initial dynamics simulation results to obtain the optimal initial dynamics simulation result, and record it as the final dynamics simulation result;

[0018] Obtain the first simulation data associated with the working condition simulation group corresponding to the final dynamics simulation result, and record it as the first optimal simulation data;

[0019] Obtain the change information of the heart rail profile design parameters included in the first preset condition corresponding to the first optimal simulation data, and record it as the optimal heart rail profile design parameters.

[0020] In some embodiments, constructing a heart rail profile parametric reconstruction module based on the geometric feature parameters of the fixed frog rail profile includes:

[0021] Draw the heart rail profile parameter curve by using the geometric feature parameters of the fixed frog rail profile through the non-uniform rational B-spline curve theory;

[0022] The heart rail profile parameter curve includes constant feature parameters, linearly varying feature parameters, and a proportionality coefficient. The constant feature parameters include the heart rail top cross slope parameter and the heart rail head side longitudinal slope parameter. The linearly varying feature parameters include the heart rail head width and the heart rail top height. The proportionality coefficient is configured to correspond to the control points for adjusting the curvature of the composite arc section on the side of the heart rail head.

[0023] In some embodiments, the change information of the heart rail profile design parameters includes the change values of the heart rail top cross slope parameter and / or the proportionality coefficient. The first simulation data includes the heart rail profile influence information and the wheel-rail dynamic influence information.

[0024] In some embodiments, the first preset condition includes an alternative selection between a first parameter change condition and a second parameter change condition;

[0025] The first parameter change condition is configured such that the heart rail top cross slope parameter changes at a first preset gradient and the proportionality coefficient remains unchanged;

[0026] The second parameter change condition is configured such that the proportionality coefficient changes at a second preset gradient and the heart rail top cross slope parameter remains unchanged.

[0027] In some embodiments, the number of the first preset conditions is 8, the number of the first parameter change conditions used is 4, and the number of the second parameter change conditions used is 4.

[0028] In some embodiments, the preset cumulative operating mileage range is from 0 to 250,000 kilometers, the preset mileage interval is 50,000 kilometers, and each second preset condition corresponds to a stage information, and the stage information includes any one of the first stage information, the second stage information, the third stage information, the fourth stage information, the fifth stage information, and the sixth stage information;

[0029] The first stage information is configured as the mileage node corresponding to 0 kilometers;

[0030] The second stage information is configured as the mileage node corresponding to 50,000 kilometers;

[0031] The third stage information is configured as the mileage node corresponding to 100,000 kilometers;

[0032] The information of the fourth stage is configured as the mileage node corresponding to 150,000 kilometers;

[0033] The information of the fifth stage is configured as the mileage node corresponding to 200,000 kilometers;

[0034] The information of the sixth stage is configured as the mileage node corresponding to 250,000 kilometers.

[0035] In some embodiments, the second simulation data includes the evolution information and wear information of the wheel tread.

[0036] In some embodiments, the vehicle-fixed frog coupling dynamics model is configured for integration with multi-body dynamics analysis software;

[0037] The initial dynamics simulation results include the maximum value, the mean square value of the maximum value, and the mean square deviation of the dynamics parameters. The dynamics parameters include at least one of the wheel-rail interaction force, the derailment coefficient, the wheel load reduction rate, and the vehicle body vibration acceleration information.

[0038] Adopting the above technical solutions, compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] Through the technical path combining parametric reconstruction and dynamic coupling analysis, the present invention significantly improves the scientificity and engineering applicability of the design of the heart rail profile of the fixed frog. The heart rail profile parametric module constructed based on geometric characteristic parameters realizes flexible control of key parameters such as the rail top cross slope and the rail head width, breaks through the limitations of traditional fixed profile design, and supports the rapid generation and iterative optimization of diversified solutions. At the same time, the dynamic module integrating the wear data of the wheel tread throughout the life cycle establishes a tread evolution database covering different wear stages through preset mileage intervals, and accurately depicts the dynamic evolution law of the wheel-rail contact state with the operating mileage. By constructing a vehicle-fixed frog coupling dynamics model, multi-dimensional matching of the heart rail parameter scheme and the wheel wear state is carried out to form a dynamic working condition simulation group. Combining with the mean square value quantization evaluation index, multi-factor coupling analysis of the wheel-rail dynamic response is realized, and a closed-loop optimization mechanism for geometric parameter adjustment and dynamic performance feedback is established.

[0040] The dynamic optimization method for the switch rail profile parameters considering the evolution of the vehicle profile provided by the present invention uses a separate parameter adjustment strategy to accurately identify the independent effects of the rail top cross slope parameter and the proportionality coefficient on the wheel-rail contact characteristics, and effectively balance the stress distribution and geometric matching under different lateral displacements. The mechanism for extracting the optimal parameters in stages can not only adapt to the different requirements in the initial state and deep wear stage of the wheel, but also significantly reduce the peak fluctuation of the vertical and lateral dynamic responses of the wheel-rail, and improve the stability of the frog structure during long-term service. Compared with the traditional method, this technology has significant advantages in suppressing local stress concentration and delaying profile deterioration through dynamic coupling analysis and hierarchical optimization of parameter sensitivity, providing a scientific basis for extending the service life of the fixed frog and optimizing the operation and maintenance cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0042] Figure 1 It is a schematic flow chart of a dynamic optimization method for the switch rail profile parameters considering the evolution of the vehicle profile described in the specific implementation manner;

[0043] Figure 2 It is a schematic diagram of the distribution of the switch rail characteristic parameters and control points described in the specific implementation manner;

[0044] Figure 3 It is a schematic diagram of the wheel tread profile under different operating mileage described in the specific implementation manner;

[0045] Figure 4 It is a schematic diagram of the change law of the mean square value of the peak vertical and lateral wheel-rail forces under the first preset condition described in the specific implementation manner;

[0046] Figure 5 It is a schematic diagram of the change law of the mean square value of the peak vertical and lateral wheel-rail forces of the stage information described in the specific implementation manner;

[0047] Figure 6 It is a schematic diagram of the deviation distribution of the wheel-rail vertical force of each scheme and the root mean square of the first preset condition described in the specific implementation manner;

[0048] Figure 7 It is a schematic diagram of the deviation distribution of the wheel-rail lateral force of each scheme and the root mean square of the first preset condition described in the specific implementation manner;

[0049] Figure 8Schematic diagram of the change of the mean square value of the first preset condition of the derailment coefficient and the peak value of the wheel load reduction rate under the information of each stage described in the specific implementation manner;

[0050] Figure 9 Schematic diagram of the change of the mean square value of the stage information of the derailment coefficient and the peak value of the wheel load reduction rate under the information of each stage described in the specific implementation manner;

[0051] Figure 10 Schematic diagram of the change law of the peak value and the mean square value of the vertical and lateral vibration acceleration of the car body described in the specific implementation manner. Specific implementation manner

[0052] The present invention will be further described in detail below with reference to the drawings and embodiments. It should be specifically noted that the following embodiments are only used to illustrate the present invention, but do not limit the scope of the present invention. Similarly, the following embodiments are only partial embodiments of the present invention rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0053] Please refer to Figures 1 to 10 , this embodiment provides a dynamic optimization method for the switch point profile parameters considering the evolution of the vehicle profile, including:

[0054] Obtain the geometric characteristic parameters of the fixed frog rail profile, and construct a switch point profile parameterization reconstruction module based on the geometric characteristic parameters of the fixed frog rail profile;

[0055] And obtain the wheel tread profile data of the train in different wear states when the operating mileage increases at equal intervals, and construct a wheel tread profile set module based on the wheel tread profile data;

[0056] Obtain a plurality of first preset conditions, and input each first preset condition into the switch point profile parameterization reconstruction module one by one to obtain first simulation data corresponding to each first preset condition, and the first preset condition is configured as the change information of the switch point profile design parameters;

[0057] Organize the plurality of first simulation data into a first simulation data set;

[0058] And obtain a plurality of second preset conditions, and input each second preset condition into the wheel tread profile set module one by one to obtain second simulation data corresponding to each second preset condition, and the second preset condition is configured as the stage information of the preset mileage interval of the train within the preset cumulative operating mileage range;

[0059] Organize the plurality of second simulation data into a second simulation data set;

[0060] Match the first analog data in the first analog data set with the second analog data in the second analog data set to obtain multiple working condition simulation groups, and each working condition simulation group includes a first analog data and a second analog data;

[0061] Build a vehicle-fixed frog coupling dynamics model;

[0062] Input multiple working condition simulation groups into the vehicle-fixed frog coupling dynamics model in sequence to obtain the initial dynamics simulation results corresponding to each working condition simulation group;

[0063] Compare multiple initial dynamics simulation results to obtain the optimal initial dynamics simulation result, and record it as the final dynamics simulation result;

[0064] Obtain the first analog data associated with the working condition simulation group corresponding to the final dynamics simulation result, and record it as the first optimal analog data;

[0065] Obtain the change information of the frog profile design parameters included in the first preset condition corresponding to the first optimal analog data, and record it as the optimal frog profile design parameters.

[0066] In this embodiment, the change information of the frog profile design parameters refers to the change rule information for adjusting the frog profile parameters of the fixed frog. The stage information of the preset mileage interval of the train within the preset cumulative operating mileage refers to the evolution and wear conditions of the wheel tread when the cumulative operating mileage of the train is within the preset range and every preset mileage is accumulated. On the basis of fully considering factors such as the vehicle and fixed frog turnout structure type and vibration characteristics, the vehicle-fixed frog coupling dynamics model is used to calculate the dynamic evaluation index of the fixed frog wheel-rail system by reasonably simplifying the vehicle system and the fixed frog turnout system. According to the geometric characteristic parameters of the fixed frog, a frog profile parameterized reconstruction module considering the geometric characteristic parameters of the frog heart is established, and combined with the measured wheel tread profile set module, a vehicle-fixed frog coupling dynamics model considering the evolution of the wheel tread profile is further established, and an evaluation index for dynamic optimization is proposed. Based on this dynamic coupling model, by setting the frog profile parameters, the mean square values of the first preset condition and the second preset condition of the wheel-rail dynamic response based on the frog profile scheme and the wheel tread profile are used as evaluation indexes to analyze and compare the wheel-rail dynamic responses when different wear states of the wheel tread (i.e., the second analog data) match the frog profiles of each scheme (i.e., the first analog data) in different operating stages of the train, realizing the parameterized optimization of the frog profile parameters based on the wheel-rail dynamic response index.

[0067] In this embodiment, by establishing a parametric reconstruction module for the frog web profile, a parametric representation of the geometric characteristics of the frog web of the fixed frog is realized. It can flexibly adjust the design parameters of the frog web profile and generate diversified schemes, breaking through the limitations of traditional fixed profile analysis and providing a basic condition for multi-scheme comparison. Based on the measured wheel tread profile set module, the system incorporates the dynamic characteristics of the wheel wear evolution during train operation. By presetting mileage intervals to divide different wear stages, a tread profile database covering the entire life cycle state of the wheel is constructed, effectively improving the model's ability to represent the dynamic changes in the wheel-rail contact state. By constructing a vehicle-fixed frog coupled dynamics model, multi-dimensional matching is performed between the parametric schemes of the frog web and the evolving states of the wheel tread, forming a set of working condition simulations covering the dynamic changes in the wheel-rail geometric matching relationship, and realizing the multi-factor coupling analysis of the wheel-rail dynamic response. The method provided in this embodiment combines wheel-rail contact geometry optimization with dynamic performance evaluation, quantifies the dynamic response characteristics under different working conditions through the mean square value index, and establishes a closed-loop optimization mechanism from geometric parameter adjustment to dynamic performance feedback, significantly improving the scientificity and reliability of the frog web profile design. In addition, by extracting the optimal frog web profile design parameters in stages, the optimal matching scheme of the frog web profile in different wheel wear stages can be accurately identified, providing theoretical support for the long-term service performance optimization of the fixed frog structure and having engineering practical value.

[0068] In some embodiments, constructing a parametric reconstruction module for the frog web profile based on the geometric characteristic parameters of the fixed frog rail profile includes:

[0069] Drawing the parametric curve of the frog web profile with the geometric characteristic parameters of the fixed frog rail profile through the non-uniform rational B-spline curve theory;

[0070] The parametric curve of the frog web profile includes constant characteristic parameters, linearly varying characteristic parameters, and proportionality coefficients. The constant characteristic parameters include the cross slope parameter of the frog web top and the longitudinal slope parameter of the frog web head side. The linearly varying characteristic parameters include the width of the frog web head and the height of the frog web top. The proportionality coefficient is configured to correspond to the control point for adjusting the curvature of the compound arc section on the side of the frog web head.

[0071] Please refer to Figure 2 , in this embodiment, the inherent geometric characteristic parameters common to any frog web section of the fixed frog are deeply explored from the perspective of profile design, such as the cross slope parameter k1 of the frog web top, the longitudinal slope parameter k2 of the frog web head side, the width 2w of the frog web head, and the height h of the frog web top. Among them, k1 and k2 are constant parameters and do not change with the longitudinal position of the frog. 2w and h are piecewise linearly varying parameters and change with the longitudinal position of the frog. Based on the characteristic parameters, a small number of key control points {N i =(x i ,y i), (i = 1, 2, …, 6, 7), and combined with the NURBS curve to realize the parametric reconstruction of the profile of any cross-section. Taking the No. 12 fixed frog of 60 kg / m rail as an example, the cross slope parameter of the frog nose rail top and the longitudinal slope parameter of the side of the frog nose rail head are 1:20 and 1:5 respectively.

[0072] Among them, N1, N2, N3, N4, N5, and N6 are used to control the height of the frog nose rail top, the width of the frog nose rail head, and the gauge measurement points; a proportionality coefficient λ is set, and the position of point N7 is determined by this coefficient on the line connecting point P and point N4. A system of equations is established to solve the coordinates of each control point, and finally, the parametric fitting of the frog nose profile in the running direction of the frog is realized based on the NURBS curve.

[0073] In this embodiment, by extracting constant characteristic parameters (such as the cross slope parameter of the frog nose rail top and the longitudinal slope parameter of the side of the rail head) and linearly varying characteristic parameters (such as the width of the rail head and the height of the rail top), and combining the regulation of the curvature of the composite arc segment by the proportionality coefficient, the multi-dimensional parametric characterization of the geometric characteristics of the frog nose profile is realized. Based on the classification definition of constant parameters and piecewise linearly varying parameters, it not only retains the stability of the key geometric attributes of the frog nose but also takes into account the dynamic adjustment requirements caused by the longitudinal position change, significantly improving the flexibility and adaptability of profile reconstruction. By setting a small number of key control points and establishing a system of equations to solve the coordinates, combined with the NURBS curve fitting method, the geometric evolution law of the complex frog nose profile can be accurately described with low-dimensional parameters, reducing the dependence on a large amount of discrete data in traditional design and simplifying the profile generation process. The introduction of the proportionality coefficient further optimizes the curvature control of the composite arc segment on the side of the rail head and enhances the adjustment ability of the local characteristics of the profile. By deeply integrating geometric features and design logic through the parametric module, it not only supports the rapid reconstruction and iterative optimization of the frog nose profile but also ensures the geometric continuity between different cross-section profiles, providing high-precision geometric input for subsequent dynamic analysis.

[0074] In some embodiments, the information on the change of the frog nose profile design parameters includes the change values of the cross slope parameter of the frog nose rail top and / or the proportionality coefficient, and the first simulation data includes the information on the influence of the frog nose profile and the information on the influence of wheel-rail dynamics.

[0075] In this embodiment, when the wheel-rail lateral displacement is small, the wheel-rail contact in the frog area is mainly concentrated on the top surface of the frog nose, while when the wheel-rail lateral displacement is large, the wheel flange contacts the side of the rail head. Since the wear of the frog nose mainly occurs in the composite arc part of the top surface and the side of the frog nose rail head, and the cross slope parameter k1 of the frog nose rail top and the proportionality coefficient λ respectively affect the profile of the top surface of the rail head and the composite arc segment on the side of the rail head, therefore, combined with the characteristics of wheel-rail contact in the frog area, the cross slope parameter k1 of the frog nose rail top and the proportionality coefficient λ are selected as the main design parameters in the selection of the frog nose scheme.

[0076] In this embodiment, by selecting the cross slope parameter k1 of the switch rail top and the proportionality coefficient λ as the core design parameters, the adaptability of the wheel-rail contact characteristics in the frog area is effectively improved. Since k1 directly regulates the slope of the top surface profile of the rail head, it can optimize the stress distribution in the top surface contact area when the wheel-rail lateral displacement is small. And λ can improve the geometric matching of the wheel flange contact when the wheel-rail lateral displacement is large by controlling the curvature transition of the composite arc section on the side of the rail head. It specifically corresponds to the top bearing area with the most significant wear of the switch rail frog and the side composite arc wear area. Through the coordinated adjustment of the two parameters, not only is a reasonable slope of the top bearing surface of the rail maintained, but also a smooth transition of the side arc section is achieved. Compared with the traditional single-parameter design, this embodiment can more accurately balance the contact states under different lateral displacements, has significant advantages in suppressing local stress concentration and delaying profile deterioration, and at the same time avoids the problem of reduced optimization efficiency caused by parameter redundancy, providing an effective path for the refined improvement of the switch rail profile design.

[0077] In some embodiments, the first preset condition includes the alternative use of a first parameter change condition and a second parameter change condition;

[0078] The first parameter change condition is configured such that the cross slope parameter of the switch rail top changes in a first preset gradient while the proportionality coefficient remains unchanged;

[0079] The second parameter change condition is configured such that the proportionality coefficient changes in a second preset gradient while the cross slope parameter of the switch rail top remains unchanged.

[0080] In this embodiment, the preset gradient means that within the parameter value range that satisfies the switch rail design, the value of the cross slope parameter of the switch rail top is taken, and this value is random and does not have a complete linear relationship. By alternatively using the first parameter change condition and the second parameter change condition, the individual effects of the cross slope parameter k1 of the switch rail top and the proportionality coefficient λ on the switch rail profile change and the wheel-rail dynamic characteristics are analyzed.

[0081] In this embodiment, by setting an independent selection mechanism for the first parameter change condition and the second parameter change condition, the isolated influence analysis of the cross slope parameter k1 of the switch rail top and the proportionality coefficient λ is effectively realized. When the first condition is adopted, the proportionality coefficient λ is fixed and only k1 is adjusted, which can clearly reveal the dominant role of the cross slope parameter of the rail top on the switch rail top profile and the vertical wheel-rail contact characteristics. When the second condition is selected, k1 is fixed and λ is changed, which can accurately capture the independent contribution of the proportionality coefficient to the profile evolution of the composite arc section on the side of the rail head and the lateral wheel-rail dynamic response, avoiding the interference of the coupling effect caused by the synchronous change of the two parameters, and enabling the clear identification of the regulation rules of each parameter on the specific profile area and dynamic characteristics. By separating the action paths of the key variables, it not only improves the accuracy of parameter sensitivity evaluation, but also provides a directional basis for optimizing the slope of the switch rail top bearing surface and the transition shape of the side arc in stages, while reducing the complexity of multi-parameter collaborative optimization, and enhancing the pertinence and effectiveness of the adjustment of the switch rail design scheme.

[0082] In some embodiments, the number of the first preset conditions is 8, the number of the first parameter change conditions used is 4, and the number of the second parameter change conditions used is 4.

[0083] In this embodiment, the cross slope parameter k1 of the switch rail top and the proportionality coefficient λ are selected for parameter combination, and 8 switch rail profile schemes are designed, as shown in Table 1 below. For S1 to S4, only the parameter k1 is adjusted, and for S5 to S8, only the parameter λ is adjusted.

[0084] Table 1 Parametric schemes of switch rail profiles

[0085]

[0086] In this embodiment, by dividing the 8 switch rail profile schemes into two groups of independent parameter adjustment sequences, the separated influence evaluation of the cross slope parameter k1 of the switch rail top and the proportionality coefficient λ is realized. Among them, the S1-S4 group makes four-gradient adjustments to k1 under the condition of fixed λ, which can completely characterize the action law of the change of the cross slope parameter of the switch rail top on the profile curvature of the top surface and the vertical load distribution. The S5-S8 group makes four-gradient adjustments to λ under the condition of fixed k1, which accurately depicts the independent influence of the proportionality coefficient on the transition shape of the composite arc section on the side and the lateral contact characteristics. The grouped parametric design of this embodiment effectively eliminates the cross-coupling effect caused by the synchronous change of the two parameters by isolating the variable interference, so that the regulation mechanism of k1 on the top bearing area and the shape optimization path of λ on the side contact area can be clearly decoupled. Based on the typical parameter range covered by the four-gradient change, it can not only reveal the response characteristics of the continuous change of a single parameter to the profile evolution, but also provide basic data support for establishing a parameter sensitivity grading model, significantly improving the clarity of the parameter optimization direction and the efficiency of scheme comparison and selection.

[0087] In some embodiments, the preset cumulative operating mileage range is from 0 to 250,000 kilometers, the preset mileage interval is 50,000 kilometers, and each second preset condition corresponds to a stage information, where the stage information includes any one of the first stage information, the second stage information, the third stage information, the fourth stage information, the fifth stage information, and the sixth stage information;

[0088] The first stage information is configured as the mileage node corresponding to 0 kilometers;

[0089] The second stage information is configured as the mileage node corresponding to 50,000 kilometers;

[0090] The third stage information is configured as the mileage node corresponding to 100,000 kilometers;

[0091] The fourth stage information is configured as the mileage node corresponding to 150,000 kilometers;

[0092] The fifth stage information is configured as the mileage node corresponding to 200,000 kilometers;

[0093] The sixth stage information is configured as the mileage node corresponding to 250,000 kilometers.

[0094] Please refer to Figure 3 , in this embodiment, the first stage information is denoted as M1, the second stage information is denoted as M2, the third stage information is denoted as M3, the fourth stage information is denoted as M4, the fifth stage information is denoted as M5, and the sixth stage information is denoted as M6. When the preset cumulative operating mileage range is from 0 to 250,000 kilometers, the evolution of the wheel tread for every 50,000 kilometers of cumulative mileage is represented by 0, 5, 10, 15, 20, and 25 respectively as the operating mileage parameters. Among them, 0 represents the standard LMA-type wheel tread in the case of an operating mileage of 0 kilometers. It should be noted that the wheel tread profile adopts the LMA-type wheel tread of the train at different operating mileage from 0 to 25 kilometers, the fixed frog is a 12-number turnout frog, and the train turnout speed is 120 km / h.

[0095] This embodiment realizes a refined segmented analysis of the wheel tread evolution process by dividing the preset cumulative operating mileage range into 0 to 250,000 kilometers and setting six stage information at intervals of 50,000 kilometers. Each stage information clearly corresponds to the mileage node, forming a complete coverage from the initial state to the maximum operating mileage, and can systematically track the dynamic changes of the wheel tread in different operating cycles. Taking the 0-kilometer standard LMA wheel tread as the benchmark, combined with the parameter settings of the fixed fork type and the switch speed, the uniformity and traceability of the evolution process comparison are ensured. The use of this stage division method not only avoids the limitations of a single mileage node, but also reveals the progressive characteristics of wear accumulation through progressive analysis, providing a structured framework for evaluating the performance degradation trend of the wheel. At the same time, a clear mileage segmentation mechanism helps to establish a detection standard that matches the operation and maintenance cycle, and can formulate targeted maintenance strategies at different stages, taking into account both evaluation efficiency and result reliability, and providing theoretical support for extending the service life of components and optimizing operation and maintenance decisions.

[0096] In some embodiments, the second simulation data includes wheel tread evolution information and wear information.

[0097] This embodiment integrates the evolution information and wear information of the wheel tread to construct a full-dimensional analysis framework for wear dynamics. The evolution information can track the continuous change trend of the tread morphology, and the wear information quantifies the degree of loss at different stages. The combination of the two can accurately identify key wear nodes and damage accumulation rules, providing a basis for predicting performance degradation. Based on this, the maintenance cycle and maintenance strategy can be optimized in a targeted manner, while balancing economy and safety, effectively extending the service life of components and improving operational reliability.

[0098] In some embodiments, the vehicle-stationary frog coupling dynamics model is configured to be integrated with multi-body dynamics analysis software;

[0099] The initial dynamics simulation results include the maximum value, maximum mean square value and mean square value deviation of the dynamics parameters, and the dynamics parameters include at least one of the wheel-rail interaction force, derailment coefficient, wheel load reduction rate and vehicle body vibration acceleration information.

[0100] In this embodiment, by integrating the vehicle-fixed frog coupling dynamics model with multi-body dynamics analysis software, the simulation accuracy and reliability of the system's dynamic behavior are significantly improved. The multi-body dynamics analysis software includes at least one of SIMPACK, ADAMS, ANSYS, and ABAQUS. Adopting a multi-software compatibility mechanism, it can be flexibly adapted to different analysis requirements, enhancing the scalability and applicability of the model. By extracting the maximum value, the mean square value of the maximum value, and the mean square deviation of the dynamic parameters, it is possible to comprehensively evaluate the dynamic response characteristics of key indicators such as wheel-rail interaction forces and derailment coefficients under extreme conditions and normal operation. The maximum value reflects the impact effect of the instantaneous peak load on the system, the mean square value quantifies the intensity of parameter fluctuations, and the mean square deviation reveals the degree of dispersion of the dynamic response. The combination of the three can accurately identify the weak links in system stability. By using the multi-dimensional parameter analysis method, both transient abnormal risks are captured and the long-term dynamic evolution law is characterized, providing a quantitative basis for optimizing the vehicle-rail matching design, formulating safety thresholds and maintenance standards, and effectively supporting the refined evaluation of dynamic performance and safety decision-making.

[0101] Further, the following examples can be developed in combination with the above technical solutions:

[0102] Please refer to Figures 4 to 10 , in order to comprehensively analyze the overall change trend of the maximum value of the wheel-rail vertical and lateral forces under the first preset condition and stage information, the mean square values of the wheel-rail vertical and lateral forces under the first preset condition and stage information are analyzed respectively. Analyze the overall change law of the deviation values of each scheme when the operation mileage increases (the wheel tread wear intensifies), and then analyze the influence law of the frog profile parameters on the deviation values of the wheel-rail vertical and lateral forces in each scheme group, so as to select the optimization scheme. Figure 4 and Figure 5 are respectively the change laws of the mean square values of the peak values of the wheel-rail vertical and lateral forces under the first preset condition and stage information. Figure 6 and Figure 7 are respectively the distribution of the deviation between the maximum value of the wheel-rail vertical and lateral forces under each first preset condition and the mean square value under the corresponding first preset condition. Among them, a positive deviation indicates that the wheel-rail vertical and lateral force under a certain working condition is greater than the mean square value under the corresponding first preset condition, which is an unfavorable working condition; similarly, a negative deviation indicates that the wheel-rail vertical and lateral force under a certain working condition is less than the mean square value under the corresponding first preset condition, which is a favorable working condition.

[0103] The results show that, considering the worn wheel treads under different vehicle operating mileage, when the operating mileage is 100,000 km and 200,000 km, the vertical and lateral forces between the wheel and rail reach 161.06 KN and 40.11 KN respectively, which are increased by 143.61% and 3.29 times respectively compared with the standard tread, indicating that the change of the wheel tread profile has a significant impact on the change of the vertical and lateral forces between the wheel and rail; the mean square values of the peak stage information of the vertical and lateral forces between the wheel and rail change little from S1 to S7 and are the smallest in S8, being 78.13 KN and 9.42 KN respectively, which are reduced by 36.25% and 67.82% compared with S1, indicating that the influence weight of the switch rail profile parameters on the vertical and lateral forces between the wheel and rail is smaller than that of the wheel tread profile; the influence of the switch rail profile parameter λ4 on the peak value of the vertical and lateral forces between the wheel and rail is greater than that of k1.

[0104] Among Figure 6 and Figure 7 , from S1 to S4, the deviation value range of the maximum vertical and lateral forces between the wheel and rail in each operation stage is small, while the deviation value range from S5 to S8 is large; the most unfavorable conditions for the vertical force between the wheel and rail are shown as S5 and S7, and the most unfavorable conditions for the lateral force between the wheel and rail are shown as S5 and S6, while the deviation values of the vertical and lateral forces between the wheel and rail in S8 are all negative, indicating that in the case of S8, the maximum values of the vertical and lateral forces between the wheel and rail in each operation stage are all smaller than the mean square value of the first preset condition, indicating that S8 is the best among all the schemes; at the same time, it is further verified that the influence of the switch rail profile parameter λ4 on the vertical and lateral forces between the wheel and rail is greater than that of the switch rail top cross slope parameter k1.

[0105] Please refer to Figures 8 to 9 , considering the worn wheel treads under different vehicle operating mileage, the peak values of the derailment coefficient and the wheel load reduction rate are both less than the safety limit of 0.8. As the train operation mileage increases, the mean square value of the first preset condition of the peak value of the derailment coefficient gradually increases, and the increase rate of the mean square value of the first preset condition of the derailment coefficient after running 200,000 km is 90.0% compared with that at 0 km; the mean square value of the stage information of the derailment coefficient remains basically unchanged and is less than 0.19, and the mean square value of the stage information of the wheel load reduction rate is between 0.26 - 0.37, all far less than 0.8. It shows that the change of the wheel tread profile has a greater impact on the safety of the train passing through the turnout than the switch rail profile parameters, and the deepening of the wheel tread wear reduces the safety of the train passing through the turnout.

[0106] Please refer to Figure 10 , Figure 10 including Figure 10 (a), Figure 10 (b), Figure 10 (c) and Figure 10 (d), Figure 10 (a) reflects the mean square value of the first preset condition and the maximum value of the vertical acceleration; Figure 10 (b) reflects the mean square value of the stage information and the maximum value of the vertical acceleration; Figure 10 (c) reflects the mean square value of the first preset condition and the maximum value of the lateral acceleration;Figure 10 (d) Reflect the mean square value of the stage information and the maximum value of the lateral acceleration. By comprehensively comparing the maximum values of the vehicle body vibration acceleration under different train operation mileage and different switch rail profile design schemes, the mean square values of the maximum values of the vehicle body vibration acceleration in each scheme and each operation stage are used as reference values for analysis, and the variation laws of the peak and mean square values of the vertical and lateral vibration acceleration of the vehicle body are obtained. The results show that the wheel tread profile and the switch rail profile parameters have little influence on the vertical and lateral acceleration of the vehicle body. By considering the influence of the worn wheel tread and the switch rail profile parameters under different train operation mileage, and comprehensively analyzing the dynamic indexes such as the wheel-rail interaction force, derailment coefficient, load reduction rate and vehicle body vibration acceleration of each scheme, the switch rail profile scheme S8 (k1 = 1 / 20, λ4 = 3 / 4) is selected as the switch rail profile scheme suitable for the worn wheel tread.

[0107] In summary, the present invention provides a dynamic optimization method for switch rail profile parameters considering the evolution of vehicle profiles. Based on the geometric characteristic parameters of the fixed frog, a parametric reconstruction module for the switch rail profile considering the geometric characteristic parameters of the switch rail is established. Combined with the measured wheel tread profile set module, a vehicle-fixed frog coupling dynamic model considering the evolution of the wheel tread profile is further established, and an evaluation index for dynamic optimization is proposed. Based on this dynamic coupling model, by setting the switch rail profile parameter schemes, the mean square values of the first preset condition and the second preset condition of the wheel-rail dynamic response based on the switch rail profile scheme and the wheel tread profile are proposed as evaluation indexes, and the wheel-rail dynamic responses when the different worn states of the wheel tread (i.e., the second simulation data) match the switch rail profiles of each scheme (i.e., the first simulation data) under different train operation stages are analyzed and compared, realizing the parametric optimization of the switch rail profile parameters based on the wheel-rail dynamic response indexes. The results show that: when the different wheel tread profiles of the train pass through the frog in each operation stage, the influence of the switch rail profile parameter λ4 on the peak value of the vertical and lateral wheel-rail forces is greater than that of k1; the change of the wheel tread profile has a greater influence on the wheel-rail interaction force, derailment coefficient and wheel load reduction rate than the switch rail profile; the wheel tread profile and the switch rail profile parameters have little influence on the vertical and lateral acceleration of the vehicle body. Among them, the mean square values of the first stage information, the second stage information, the third stage information, the fourth stage information, the fifth stage information and the sixth stage information are the smallest in S8, and are reduced by 36.25% and 67.82% respectively compared with S1. The deviation between the maximum value of the vertical and lateral wheel-rail forces of each scheme and the mean square value of the corresponding first preset condition is negative in S8, and the mean square values of the other dynamic response indexes are also small. Therefore, the switch rail profile scheme S8 (k1 = 1 / 20, λ4 = 3 / 4) is selected as the switch rail profile scheme suitable for the worn wheel tread.

[0108] Adopting the above technical scheme, compared with the prior art, the present invention has the following beneficial effects:

[0109] Through the technical path combining parametric reconstruction and dynamic coupling analysis, the present invention significantly improves the scientific nature and engineering applicability of the design of the contour of the frog heart rail. The parametric module of the frog heart rail contour based on geometric feature parameters realizes the flexible adjustment of key parameters such as the cross slope of the rail top and the width of the rail head, breaks through the limitations of the traditional fixed contour design, and supports the rapid generation and iterative optimization of diversified schemes. At the same time, the dynamic module integrating the wear data of the wheel tread throughout its life cycle establishes a tread evolution database covering different wear stages through preset mileage intervals, and accurately depicts the dynamic evolution law of the wheel-rail contact state with the operation mileage. By constructing a vehicle-fixed frog coupling dynamics model, the heart rail parameter scheme and the wheel wear state are matched in multiple dimensions to form a dynamic working condition simulation group. Combining the root mean square value quantization evaluation index, the multi-factor coupling analysis of the wheel-rail dynamic response is realized, and a closed-loop optimization mechanism for the adjustment of geometric parameters and the feedback of dynamic performance is established.

[0110] The method for dynamic optimization of the contour parameters of the frog heart rail considering the evolution of the vehicle contour provided by the present invention adopts a separated parameter adjustment strategy to accurately identify the independent influence of the cross slope parameter of the rail top and the proportional coefficient on the wheel-rail contact characteristics, and effectively balance the stress distribution and geometric matching under different lateral displacements. The mechanism of extracting the optimal parameters in stages can not only adapt to the different requirements in the initial state of the wheel and the deep wear stage, but also significantly reduce the peak fluctuation of the vertical and lateral dynamic response of the wheel-rail, and improve the stability of the frog structure during long-term service. Compared with the traditional method, this technology has significant advantages in suppressing local stress concentration and delaying contour deterioration through dynamic coupling analysis and hierarchical optimization of parameter sensitivity, providing a scientific basis for extending the service life of the fixed frog and optimizing the maintenance cycle.

[0111] The above are only some embodiments of the present invention, and thus do not limit the protection scope of the present invention. Any equivalent device or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A dynamic optimization method for the profile parameters of the switch point rail considering the evolution of the vehicle profile, characterized in that Including: Obtaining geometric characteristic parameters of the fixed frog rail profile, and constructing a parametric reconstruction module for the switch rail profile based on the geometric characteristic parameters of the fixed frog rail profile; And obtaining wheel tread profile data of the train in different wear states when the operating mileage increases at equal intervals, and constructing a wheel tread profile set module based on the wheel tread profile data; Obtaining a plurality of first preset conditions, and inputting each of the first preset conditions into the parametric reconstruction module for the switch rail profile one by one to obtain first simulation data corresponding to each of the first preset conditions, where the first preset conditions are configured as information on changes in switch rail profile design parameters; Sorting the plurality of first simulation data into a first simulation data set; And obtaining a plurality of second preset conditions, and inputting each of the second preset conditions into the wheel tread profile set module one by one to obtain second simulation data corresponding to each of the second preset conditions, where the second preset conditions are configured as stage information of the preset mileage interval of the train within the preset cumulative operating mileage range; Sorting the plurality of second simulation data into a second simulation data set; Matching the first simulation data in the first simulation data set with the second simulation data in the second simulation data set to obtain a plurality of working condition simulation groups, each of the working condition simulation groups including one first simulation data and one second simulation data; Constructing a vehicle-fixed frog coupling dynamics model; Inputting the plurality of working condition simulation groups into the vehicle-fixed frog coupling dynamics model in sequence to obtain initial dynamics simulation results corresponding to each of the working condition simulation groups; Comparing the plurality of initial dynamics simulation results to obtain the optimal initial dynamics simulation result, and denoting it as the final dynamics simulation result; Obtaining the first simulation data associated with the working condition simulation group corresponding to the final dynamics simulation result, and denoting it as the first optimal simulation data; Obtaining the information on changes in switch rail profile design parameters included in the first preset condition corresponding to the first optimal simulation data, and denoting it as the optimal switch rail profile design parameters.

2. The dynamic optimization method for the profile parameters of the switch point rail considering the evolution of the vehicle profile according to claim 1, characterized in that, Constructing the parametric reconstruction module for the switch rail profile based on the geometric characteristic parameters of the fixed frog rail profile includes: Drawing a switch rail profile parameter curve with the geometric characteristic parameters of the fixed frog rail profile through the non-uniform rational B-spline curve theory; The switch rail profile parameter curve includes constant characteristic parameters, linearly varying characteristic parameters, and a scale factor. The constant characteristic parameters include the switch rail top cross slope parameter and the switch rail head side longitudinal slope parameter. The linearly varying characteristic parameters include the switch rail head width and the switch rail top height. The scale factor is configured to correspond to the control points for adjusting the curvature of the switch rail head side composite arc segment.

3. The dynamic optimization method for the switch rail profile parameters considering the evolution of the vehicle profile according to claim 2, characterized in that The information on changes in switch rail profile design parameters includes the change value of the switch rail top cross slope parameter and / or the scale factor. The first simulation data includes switch rail profile influence information and wheel-rail dynamic influence information.

4. The dynamic optimization method for the switch rail profile parameters considering the evolution of the vehicle profile according to claim 3, characterized in that, The first preset conditions include an alternative selection between a first parameter change condition and a second parameter change condition; The first parameter change condition is configured such that the switch rail top cross slope parameter changes at a first preset gradient and the scale factor remains unchanged; The second parameter change condition is configured such that the proportionality coefficient changes in a second preset gradient, and the cross slope parameter of the switch rail top remains unchanged.

5. The dynamic optimization method for the switch point profile parameters considering the evolution of the vehicle profile according to claim 4, characterized in that, The number of the first preset conditions is 8, the number of the first parameter change conditions used is 4, and the number of the second parameter change conditions used is 4.

6. The dynamic optimization method for the switch point profile parameters considering the evolution of the vehicle profile according to claim 1, characterized in that The preset cumulative operating mileage range is from 0 to 250,000 km, the preset mileage interval is 50,000 km, each of the second preset conditions corresponds to one stage information, and the stage information includes any one of first stage information, second stage information, third stage information, fourth stage information, fifth stage information, and sixth stage information; The first stage information is configured as the mileage node corresponding to 0 km; The second stage information is configured as the mileage node corresponding to 50,000 km; The third stage information is configured as the mileage node corresponding to 100,000 km; The fourth stage information is configured as the mileage node corresponding to 150,000 km; The fifth stage information is configured as the mileage node corresponding to 200,000 km; The sixth stage information is configured as the mileage node corresponding to 250,000 km.

7. The dynamic optimization method for the frog profile parameters considering the evolution of the vehicle profile according to claim 6, characterized in that The second simulation data includes the evolution information and wear information of the wheel tread.

8. The dynamic optimization method for the switch point profile parameters considering the evolution of the vehicle profile according to claim 1, characterized in that The vehicle-fixed frog coupling dynamics model is configured as an integration of multi-body dynamics analysis software; The initial dynamics simulation results include the maximum value, the mean square value of the maximum value, and the mean square value deviation of the dynamics parameters, and the dynamics parameters include at least one of wheel-rail interaction force, derailment coefficient, wheel load reduction rate, and car body vibration acceleration information.

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