Wheel profile optimization method, device and equipment based on composite equivalent taper

Abstract

The present application belongs to the field of wheel profile design, and provides a wheel profile optimization method, device and equipment based on composite equivalent taper. In order to solve the problem of low wheel profile optimization efficiency, the wheel profile optimization method based on composite equivalent taper comprises the following steps: obtaining the relationship between the composite equivalent taper and the vehicle lateral stability and the wheel wear performance based on the vehicle dynamics model and the vehicle and track working conditions; based on the relationship between the composite equivalent taper and the vehicle lateral stability and the wheel wear performance, taking the minimization of the composite equivalent taper as the objective function, and taking the value range of the circular arc parameter describing the wheel profile as the constraint function, obtaining the optimized value of the circular arc parameter describing the wheel profile, that is, obtaining the optimized wheel profile. The present application improves the wheel profile optimization efficiency while describing the wheel profile with a small number of parameters.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wheel profile design, and particularly relates to a wheel profile optimization method, device and equipment based on composite equivalent taper. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] With the increase of the running mileage of high-speed railways, the problem of wheel wear is becoming more and more prominent. In the actual operation of the vehicle, the wheel wear will change the geometric state of the wheel and rail contact, thereby affecting the vehicle dynamics performance. In addition, the concave wear of the wheel profile, the wheel polygon wear, the wheel tread peeling, the low taper car shaking, the rail corrugation, the poor rail surface light band, the broken spring strip and other problems caused by the wheel wear may all cause safety problems in the operation of the vehicle. Therefore, attention should be paid to the optimization design of the wheel profile in the design stage of the vehicle, so as to prolong the service cycle of the wheel and improve the vehicle dynamics performance.

[0004] At present, the research on the optimization of the wheel profile can be divided into the optimization with the target of reducing the wheel wear and the multi-objective optimization with the additional consideration of the vehicle dynamics performance from the perspective of optimization target. In the single-objective optimization, the NURBS cubic curve is used to describe the wheel profile, the intelligent optimization algorithm is used to optimize the design of the wheel profile with the target of reducing the wheel wear, and the dynamics performance is verified. In the multi-objective optimization, the NURBS cubic curve or the circular arc parameter is used to describe the wheel profile, the vehicle running stability, the stability of the snake movement and the wheel wear characteristics are considered as the optimization target, and the multi-objective optimization design of the wheel profile for solving the low taper car shaking is carried out. The premise of the existing wheel profile optimization scheme is to use multiple variables to describe the geometric shape of the wheel profile, and the increase of the description variables leads to the increase of the complexity and uncertain factors of the research, and the difficulty of the optimization design of the wheel profile also increases. However, the use of a small number of parameters to describe the wheel profile and the evaluation of the wheel profile through a single nonlinear parameter can improve the calculation efficiency of the optimization design of the wheel profile, but the disadvantage is that it is difficult to determine the nonlinear parameters for describing the wheel-rail contact geometry and the vehicle dynamics performance.

[0005] In summary, the inventors found that the existing wheel profile optimization scheme has the problems of multiple description variables leading to great design difficulty, and the nonlinear parameters in the scheme of a small number of parameters describing the wheel profile being difficult to determine, which finally leads to the problems of low optimization efficiency of the wheel profile and poor optimization effect. SUMMARY

[0006] In order to solve the technical problems in the background art, the application provides a wheel profile optimization method, device and equipment based on composite equivalent conicity, which can improve the wheel profile optimization efficiency while describing the wheel profile with a small number of parameters.

[0007] In order to achieve the above-mentioned purpose, the application adopts the following technical solutions:

[0008] The first aspect of the application provides a wheel profile optimization method based on composite equivalent conicity, which comprises:

[0009] Based on the vehicle dynamics model and the vehicle and track working conditions, the relationship between the composite equivalent conicity and the vehicle lateral stability and the wheel wear performance is obtained.

[0010] Based on the relationship between the composite equivalent conicity and the vehicle lateral stability and the wheel wear performance, the optimization value of the circular arc parameter describing the wheel profile is obtained as the objective function with the minimum composite equivalent conicity and the constraint function with the value range of the circular arc parameter describing the wheel profile, that is, the optimized wheel profile is obtained.

[0011] As an implementation mode, the composite equivalent conicity is obtained by weighting and summing the linear equivalent conicities under different lateral displacements of the wheel set according to the proportion of the wheel-rail contact width in the total wheel-rail contact width.

[0012] The above-mentioned scheme has the advantages that the influence of the change of the entire wheel-rail contact area on the wheel-rail contact geometry is considered, the linear equivalent conicities under different lateral displacements of the wheel set are weighted and processed, and the accurate description of the wheel-rail contact geometry state is ensured.

[0013] As an implementation mode, the circular arc parameters describing the wheel profile include the transition circular arc, the middle part circular arc of the profile, the horizontal coordinate of the center of the transition circular arc and the horizontal coordinate of the center of the middle part circular arc of the profile.

[0014] It should be noted here that the circular arc parameters describing the wheel profile can include the rim root circular arc and the outer side reverse circular arc in addition to the above-mentioned parameters, and the actual situation can be selected by those skilled in the art.

[0015] As an implementation mode, a radial basis function neural network model is adopted to construct an approximate model of the composite equivalent conicity.

[0016] The radial basis function neural network model (RBF model) can approximate any nonlinear function with arbitrary precision and has global approximation capability, which fundamentally avoids local optimization problems, and has compact topology structure, structure parameters can realize separated learning, and fast convergence speed.

[0017] As an implementation form, the optimal Latin hypercube design is used to train the training set and the test set, and the accuracy of the approximate model is verified; wherein the samples in the training set and the test set are composed of input and output, the input is the circular arc parameter of the wheel profile, and the output is the vehicle lateral stability and the wheel wear performance.

[0018] As an implementation form, the particle swarm algorithm is used to optimize the circular arc parameter of the wheel profile, and the optimized wheel profile is obtained.

[0019] The particle swarm algorithm is simple in operation, fast in convergence, and easy to adjust the algorithm according to different actual needs, so the optimization algorithm selects the particle swarm algorithm.

[0020] The second aspect of the application provides a wheel profile optimization device based on a composite equivalent cone, which comprises:

[0021] A relationship determining module is configured to obtain the relationship between the composite equivalent cone and the vehicle lateral stability and the wheel wear performance based on the vehicle dynamics model and the vehicle and track working conditions.

[0022] A parameter optimization module is configured to obtain the optimized value of the circular arc parameter of the wheel profile as the optimized wheel profile based on the relationship between the composite equivalent cone and the vehicle lateral stability and the wheel wear performance, taking the minimum composite equivalent cone as the objective function, and taking the value range of the circular arc parameter of the wheel profile as the constraint function.

[0023] As an implementation form, the composite equivalent cone is obtained by weighting and summing the linear equivalent cones under different lateral displacements of the wheel set according to the proportion of the wheel-rail contact width in the total wheel-rail contact width.

[0024] As an implementation form, the circular arc parameter of the wheel profile comprises a transition circular arc, a profile middle circular arc, a horizontal coordinate of the center of the transition circular arc, and a horizontal coordinate of the center of the profile middle circular arc.

[0025] As an implementation form, a radial basis neural network model is used to construct the approximate model of the composite equivalent cone.

[0026] As an implementation form, the optimal Latin hypercube design is used to train the training set and the test set, and the accuracy of the approximate model is verified; wherein the samples in the training set and the test set are composed of input and output, the input is the circular arc parameter of the wheel profile, and the output is the vehicle lateral stability and the wheel wear performance.

[0027] As an implementation form, the particle swarm algorithm is used to optimize the circular arc parameter of the wheel profile, and the optimized wheel profile is obtained.

[0028] The third aspect of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the wheel profile optimization method based on composite equivalent conicity as described above.

[0029] The fourth aspect of the present application provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the wheel profile optimization method based on composite equivalent conicity as described above when executing the program.

[0030] Compared with the prior art, the present application has the following beneficial effects:

[0031] (1) The present application proposes a new wheel-rail contact geometry composite equivalent conicity parameter, which is obtained by weighting and summing the linear equivalent conicity under different wheelset lateral displacements according to the proportion of the wheel-rail contact width in the total wheel-rail contact width; compared with the existing wheel-rail contact geometry nonlinear equivalent conicity, the present application does not need to calculate the mean and variance, and requires fewer parameters; compared with the existing composite equivalent conicity, the present application considers the influence of the entire wheel-rail contact area change on the wheel-rail contact geometry, and weights the linear equivalent conicity values under different wheelset lateral displacements, thereby ensuring the accurate description of the wheel-rail contact geometry state.

[0032] (2) The present application proposes a wheel profile optimization design method based on composite equivalent conicity for improving the lateral stability and wheel-rail wear of a railway vehicle, which uses a radial basis neural network model to establish an approximate model of the composite equivalent conicity, uses optimal Latin hypercube design training set and test set to verify the accuracy of the approximate model, and calculates the relationship between the composite equivalent conicity and the vehicle stability and wheel wear performance through a vehicle dynamics model; in the wheel profile optimization calculation, the minimum composite equivalent conicity is taken as the objective function, and the value range of the circular arc parameters describing the wheel profile is taken as the constraint function, to obtain the optimized wheel profile, which improves the wheel profile optimization efficiency while describing the wheel profile with a small number of parameters.

[0033] The advantages of the additional aspects of the present application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0034] The accompanying drawings, which form a part of the present application, are included to provide a further understanding of the application, and are incorporated herein for purposes of illustrating the illustrative embodiments of the present application and the explanations provided herein, and are not intended as a limitation of the present application.

[0035] Figure 1is a flow chart of a wheel profile optimization method based on composite equivalent conicity of an embodiment of the present application;

[0036] Figure 2 is a flow chart of a particle swarm optimization algorithm of an embodiment of the present application;

[0037] Fig. 3(a) is a relationship between composite equivalent conicity and lateral stability index of an embodiment of the present application;

[0038] Fig. 3(b) is a relationship between composite equivalent conicity and wear number of an embodiment of the present application;

[0039] Fig. 3(c) is a relationship between composite equivalent conicity and derailment coefficient of an embodiment of the present application;

[0040] Fig. 3(d) is a relationship between composite equivalent conicity and wheel-rail lateral force of an embodiment of the present application;

[0041] Figure 4 is a comparison chart of LMA wheel profile and optimized wheel profile of an embodiment of the present application;

[0042] Fig. 5(a) is a 60N contact point distribution chart of LMA wheel profile of an embodiment of the present application;

[0043] Fig. 5(b) is a 60N contact point distribution chart of optimized wheel profile of an embodiment of the present application;

[0044] Fig. 6(a) is a comparison chart of initial profile car body lateral stability index of LMA wheel profile and optimized wheel profile at different speeds of an embodiment of the present application;

[0045] Fig. 6(b) is a comparison chart of initial profile car body vertical stability index of LMA wheel profile and optimized wheel profile at different speeds of an embodiment of the present application;

[0046] Fig. 6(c) is a comparison chart of initial profile bogie lateral acceleration RMS value of LMA wheel profile and optimized wheel profile at different speeds of an embodiment of the present application;

[0047] Fig. 6(d) is a comparison chart of initial profile bogie vertical acceleration RMS value of LMA wheel profile and optimized wheel profile at different speeds of an embodiment of the present application;

[0048] Fig. 6(e) is a comparison chart of initial profile derailment coefficient of LMA wheel profile and optimized wheel profile at different speeds of an embodiment of the present application;

[0049] Fig. 6(f) is a comparison chart of initial profile wheel load reduction rate of LMA wheel profile and optimized wheel profile at different speeds of an embodiment of the present application;

[0050] Figure 6(g) is a comparison of the initial wheel-rail lateral forces of the LMA wheel profile and the optimized wheel profile at different vehicle speeds according to an embodiment of the present invention.

[0051] Figure 6(h) is a comparison of the initial wear count of the LMA wheel profile and the optimized wheel profile at different vehicle speeds according to an embodiment of the present invention.

[0052] Figure 7(a) is a comparison of the tread wear of the LMA wheel profile and the optimized wheel profile within 300,000 kilometers according to the embodiment of the present invention.

[0053] Figure 7(b) is a comparison of the wheel flange wear within 300,000 kilometers between the LMA wheel profile and the optimized wheel profile according to the embodiment of the present invention.

[0054] Figure 7(c) is a comparison of the vehicle body lateral stability index of LMA wheel profile and optimized wheel profile under different operating mileages in this embodiment of the invention.

[0055] Figure 7(d) is a comparison of the vehicle body vertical stability index of the LMA wheel profile and the optimized wheel profile under different operating mileages in this embodiment of the invention.

[0056] Figure 7(e) is a comparison of the frame lateral acceleration RMS values ​​of the LMA wheel profile and the optimized wheel profile under different operating mileages in the embodiment of the present invention.

[0057] Figure 7(f) is a comparison of the frame vertical acceleration RMS values ​​of the LMA wheel profile and the optimized wheel profile under different operating mileages in the embodiment of the present invention.

[0058] Figure 7(g) is a comparison of the derailment coefficients of the LMA wheel profile and the optimized wheel profile under different operating mileages in the embodiment of the present invention.

[0059] Figure 7(h) is a comparison of the wheel load reduction rate of LMA wheel profile and optimized wheel profile under different operating mileages in the embodiment of the present invention;

[0060] Figure 7(i) is a comparison of the wheel-rail lateral forces of the LMA wheel profile and the optimized wheel profile under different operating mileages in the embodiment of the present invention.

[0061] Figure 7(j) is a comparison of the wear count of LMA wheel profile and optimized wheel profile under different operating mileages in the embodiment of the present invention;

[0062] Figure 7(k) is a comparison of the average wear index of the LMA wheel profile and the optimized wheel profile under different operating mileages in the embodiment of the present invention;

[0063] Figure 8 This is a schematic diagram of the wheel profile optimization device based on composite equivalent taper according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The application will be further described below in connection with the drawings and examples.

[0065] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0066] It is also important to note that the terms used herein are not intended to limit the exemplary embodiments to the specific embodiments which are described in detail. Rather, it is contemplated that the terms are intended to also include any and all techniques which are at least equivalent in terms of the functionality used herein, unless otherwise specifically stated. Furthermore, it should be understood that the terms "comprise" and / or "comprising," when used in this specification, are taken to specify the presence of stated features, steps, operations, devices, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, devices, components, and / or groups thereof.

[0067] In order to obtain higher speed under the condition of ensuring the vehicle dynamics performance, many new technologies are applied in high-speed railway, such as vehicle lightweight technology, high smoothness and high stability ballastless track. The application of these technologies will cause the working condition of vehicle suspension components to change: one is the decrease of the relative displacement amplitude between the suspension components and the vehicle; the other is the increase of the relative motion frequency between the suspension components and the vehicle. In order to ensure the safe and stable operation of the vehicle, higher requirements are put forward for the suspension performance of the vehicle. The suspension system of the railway vehicle can absorb and mitigate the vibration and impact caused by road irregularities, and its performance will directly affect the stability, comfort, service life of vehicle components and driving safety of the vehicle.

[0068] The first consideration in the optimization design of wheel profile is how to describe the geometric shape of the wheel profile, that is, how to represent the wheel-rail contact geometry. Wheel-rail contact geometry refers to the geometric state of wheel-rail contact, which has a significant impact on vehicle running quality and curve passing, etc. Under the condition that the track gauge and other parameters are relatively fixed, the geometric contact under the matching of wheel-rail profile becomes an important consideration factor in the design of wheel profile. An excellent wheel profile must have good wheel-rail contact geometric characteristics. Early research on wheel-rail contact geometry often starts from the perspective of linear parameters, but the research on nonlinear railway vehicle system dynamics shows that nonlinear wheel-rail relationship has a very important influence on the running state of the vehicle. From the perspective of nonlinearity, a single variable can accurately describe the state of wheel-rail contact geometry, and equivalently describe the good and bad of the geometric shape of the wheel profile. The geometric state of the wheel profile has a great relevance to the performance of the vehicle dynamics. Exploring the correlation between the nonlinear wheel-rail contact geometry parameters and the evaluation indexes of vehicle dynamics, describing the wheel-rail contact state and the performance of the vehicle dynamics through a single variable, simplifies the optimization design of the wheel profile.

[0069] In order to solve the problems of too many description variables in the wheel profile optimization scheme in the background art, difficulty in design, difficulty in determining the nonlinear parameters in the wheel profile scheme described by a small number of parameters, and finally poor wheel profile optimization effect, the application provides a wheel profile optimization method, device and equipment based on composite equivalent taper, which describes the wheel profile with a small number of parameters and improves the wheel profile optimization efficiency.

[0070] The specific scheme of the wheel profile optimization method, device and equipment based on composite equivalent taper of the application is given below in combination with the specific embodiments.

[0071] Embodiment one

[0072] Reference Figure 1 The embodiment provides a wheel profile optimization method based on composite equivalent taper, which comprises the following steps.

[0073] S101: Based on the vehicle dynamics model and the vehicle and track working conditions, the relationship between the composite equivalent taper and the vehicle lateral stability and the wheel wear performance is obtained, as shown in the following formula (1). Figures 3(a)-3(d)

[0074] In the specific implementation process, the linear equivalent tapers of the wheelset under different lateral displacements are weighted and summed to obtain the composite equivalent taper according to the proportion of the wheel-rail contact width in the total wheel-rail contact width.

[0075] In the specific implementation process, a space geometry coordinate system is established, the X-axis is parallel to the track and passes through the center of the wheelset, the Y-axis is parallel to the wheelset axis, and the Z-axis is perpendicular to the ground and upward. When the wheelset is laterally displaced, the horizontal coordinate y C of the wheel-rail contact point on the wheel profile changes with the change of the wheel lateral displacement y WS . The change of the horizontal coordinate position of the wheel-rail contact point can be represented by a function dy c (y ws ) with the wheel lateral displacement y WS as the independent variable, which describes the change rate of the contact point position based on the change of the wheelset lateral displacement, as shown in formula (1). Where dy C (y WS ) represents the change of the horizontal coordinate of the contact point, and dy WS represents the change of the wheel lateral displacement. The right wheel of the wheelset is the research object in this section, and the right displacement of the wheel is defined as positive and the left displacement of the wheel is defined as negative.

[0076]

[0077] In order to reflect the size of the change of the wheel-rail contact point coordinate with the change of the wheelset lateral displacement, the contact width and the contact width change rate can be used to describe the wheel-rail contact area distribution, wherein the wheel-rail contact width L W ​The distance between the contact point position y WS = -A WS (to the left) and the contact point position y WS = A WS (to the right) can be expressed as described in equation (2), where A WS represents the lateral displacement amplitude of the wheelset.

[0078] L W (A WS ) = y C (-A WS ) - y C (A WS ) (2)

[0079] The composite equivalent conicity is based on the linear equivalent conicity and the wheel-rail contact nonlinear parameters, and fully considers the influence of the contact area change on the wheel-rail contact geometry.

[0080] Within the wheelset lateral displacement range 1-k (unit: mm), the linear equivalent conicity is weighted and summed according to the proportion of the wheel-rail contact width at the current wheelset lateral displacement in the total wheel-rail contact width, as shown in equation (3), λ n is the composite equivalent conicity, Lw i and Lw j are the wheel-rail contact widths corresponding to the wheelset lateral displacements imm and jmm, respectively, λ i is the equivalent conicity corresponding to the wheelset lateral displacement imm. For example, k = 8, which can also be other values set by those skilled in the art according to actual conditions.

[0081]

[0082] The composite equivalent conicity of the embodiment considers the influence of the entire wheel-rail contact area change on the wheel-rail contact geometry, and the linear equivalent conicity values at different wheelset lateral displacements are weighted and processed, thereby ensuring the accurate description of the wheel-rail contact geometry state.

[0083] It takes 1 to 2 minutes to completely calculate all the optimization objective functions, and considering that a large number of calculations are required when the algorithm is optimized, the use of the original model will consume a large amount of calculation time cost, causing unnecessary waste. In order to save the calculation cost, it is a relatively appropriate choice to use an approximate model with a certain accuracy for calculation. The so-called approximate model is to directly establish the relationship between the input and the output according to a certain number of pre-calculated system input and output data.

[0084] In the specific implementation process, a radial basis neural network model is used to construct an approximate model of the composite equivalent conicity.

[0085] The radial basis function neural network model (RBF model) can approximate any nonlinear function with arbitrary precision and has global approximation capability, fundamentally avoiding local optimization problems, and has a compact topology structure, structure parameters can be learned separately, and has a fast convergence speed.

[0086] The optimal Latin hypercube design is used to train the training set and the test set, and the accuracy of the approximation model is verified; the samples in the training set and the test set are composed of input and output, the input is the arc parameter of the wheel profile, and the output is the vehicle lateral stability and the wheel wear performance.

[0087] Specifically, a CRH380A vehicle dynamics model is established, and preferably, the vehicle and track setting working conditions are as shown in Table 1. Under the current working condition, the vehicle lateral stability is calculated to the relationship between the wheel wear index and the composite equivalent cone, and the calculation results are combined with the corresponding wheel profile description parameters to form a sample group.

[0088] Table 1 Simulation calculation working condition

[0089] Parameter Value Vehicle CRH380A Track geometry design Straight line Wheel-rail matching New design wheel profile + 60N rail Track irregularity UIC-good-1000m Wheel-rail friction coefficient 0.4 Vehicle speed 350km / h

[0090] In this embodiment, the arc parameters for describing the wheel profile include the transition arc, the middle arc of the profile, the horizontal coordinate of the center of the transition arc, and the horizontal coordinate of the center of the middle arc of the profile.

[0091] It should be noted that the arc parameters for describing the wheel profile can include the rim root arc and the outer reverse arc in addition to the above-mentioned parameters, and the person skilled in the art can select them according to the actual situation.

[0092] The design of the rim needs to consider the performance of derailment prevention, curve passing and turnout passing, and in addition to thin rim design, the rim profile is basically determined at the initial stage of wheel design. In addition, based on the current operation status and research results, the existing parameters of the rim height and thickness, the rim angle and the outer segment straight line can also meet the relevant requirements. The throat root radius size affects whether there is two-point contact between the wheel and the rail and the vertical distance size of the two-point contact, and selecting an appropriate throat root radius can significantly reduce the wheel wear. The central part of the profile is the main part of contact with the rail top, and its geometric shape directly affects the train running stability and large radius curve passing performance. The connection part between the profile and the outer end provides the required rolling circle radius difference for different curve passing of the vehicle. In summary, the throat root circle, the central part of the profile and the connection part between the profile and the outer end are the key points of the wheel profile design.

[0093] When describing the wheel profile using circular arc parameters, there are a total of 6 parameters: the circular arc at the rim root R5, the transition circular arc R6, and the x-coordinate of the center of the transition circular arc. R6 The central arc of the profile is R7, and the x-coordinate of the center of the central arc is x. R7 The outer circular arc is R8, where R6, x R6 R7, x R7 These four parameters are most closely related to vehicle dynamics performance, so when designing the wheel profile in this embodiment, these four parameters are given priority as optimization parameters.

[0094] It should be noted here that when optimizing the design for the LMA profile, the rim portion adopts the LMA profile rim.

[0095] The optimal Latin hypercube design is adopted. First, the range of values ​​for the four characteristic parameters describing the wheel profile is given. Within the space formed by the range of values ​​for these four parameters, the most representative sample group is sampled. The training set and the test set are randomly divided from the obtained sample group according to the ratio of 80% for the training set and 20% for the test set.

[0096] To establish the approximate model, the wheel description parameters in each sample group are used as input, and the vehicle lateral stability is expressed as the wheel wear index as output. The approximate model is then trained, established, and validated.

[0097] Using wheel description parameters with different composite equivalent tapers as input, representing 10% of the training set, the relationship between composite equivalent taper and vehicle lateral stability and wear index is calculated through an approximate model.

[0098] In the above embodiments, the relationship between the composite equivalent taper and vehicle dynamic performance indicators is as follows: Compared with the equivalent taper, the correspondence between the composite equivalent taper and vehicle dynamic performance indicators is more lagging, and the vehicle dynamic performance indicators increase more slowly with the increase of the composite equivalent taper. The smaller the composite equivalent taper, the smaller the vehicle's lateral stability index, wear number, derailment coefficient, and wheel-rail lateral force.

[0099] S102: Based on the relationship between the composite equivalent taper and the vehicle's lateral stability and wheel wear performance, the objective function is to minimize the composite equivalent taper, and the range of values ​​of the arc parameters describing the wheel profile is used as the constraint function. The optimized values ​​of the arc parameters describing the wheel profile are obtained, which is the optimized wheel profile.

[0100] In this embodiment, the arc parameters describing the wheel profile are optimized based on the particle swarm optimization algorithm to obtain the optimized wheel profile. The specific process is as follows: Figure 2 As shown.

[0101] There are many mainstream intelligent algorithms at present, including particle swarm algorithm, genetic algorithm, ant colony algorithm, etc. The above different algorithms can all achieve the required optimization, among which the particle swarm algorithm is simple to operate, fast in convergence speed, and easy to adjust the algorithm according to different actual needs, so the optimization algorithm selects the particle swarm algorithm. In the present embodiment, the learning factor in the particle swarm algorithm is set to 1.5, and the particle update speed range is set to -1 to 1.

[0102] The optimized wheel profile is defined as the Opt wheel profile. Figure 4 The comparison of the optimized profile and the LMA profile is shown by Figure 4 It can be seen that, compared with the LMA profile, the Opt profile has a smaller radius at the throat root circle and a smaller taper at the tread. From Figures 5(a)-5(b) It can be seen from the comparison of the contact point distribution that the contact points of the Opt wheel and the 60N rail are mainly distributed at the tread, and the contact point distribution is more concentrated than that of the LMA profile. The equivalent taper of the Opt profile matched with the 60N is much smaller than that of the LMA wheel matched with the 60N, which is consistent with the relationship between the above vehicle dynamics indicators and the composite equivalent taper.

[0103] In the above embodiment, preferably, based on the established CRH380A vehicle dynamics model, the track geometry is set as a straight line, the track irregularity is set as UIC-Good-1000m, the wheel-rail contact model is selected as the FASTSIM model, the wheel-rail friction coefficient is set as 0.4, and the calculation speed is 100-500km·h -1 (Increment 50km·h -1 ), the vehicle body lateral and vertical stability indicators, the frame lateral and vertical vibration acceleration RMS values, the derailment coefficient, the wheel load reduction rate, the wheel-rail lateral force, and the wear number are selected as the verification indicators, and the dynamic indicators of the Opt and LMA wheel profiles matched with the 60N rail are compared. From Figures 6(a)-6(h) It can be seen that the Opt wheel profile has good optimization effect in the optimization objectives of the present project: the vehicle body lateral stability and the wear index, especially the optimization effect of the vehicle body lateral stability at high speed stage and the wheel wear at low speed stage is more remarkable. In terms of the vehicle body vertical stability, the low-speed frame lateral acceleration RMS value, the frame vertical acceleration RMS, and the wheel load reduction rate, the Opt profile and the LMA profile have nearly consistent effects. In terms of the high-speed frame lateral acceleration RMS value, the derailment coefficient, and the wheel-rail lateral force, the Opt profile performs poorly, but the indicators at each speed are less than the limit values given in the Railway Vehicle Dynamics Performance Evaluation and Test Identification Standard GB / T5599-2019, so it can be concluded that the Opt wheel profile has certain optimization effect in improving the vehicle lateral stability and the wheel-rail wear.

[0104] In this embodiment, the horizontal axis represents vehicle speed. The vertical axis represents the difference between the vehicle dynamics indices calculated by the LMA profile and those calculated by the Opt profile at wear levels of new tires, 60,000, 120,000, 180,000, 240,000, and 300,000 kilometers. A larger vertical axis indicates a better optimization effect of the Opt profile. Figures 7(a)-7(k) It can be seen that the tread wear of the Opt profile is much less than that of the LMA profile, reaching only 20% of the LMA tread wear after 300,000 kilometers of vehicle operation. However, the flange wear of the Opt profile is more severe, reaching approximately 0.55mm at the end of wheel refinishing. Among the vehicle dynamics indicators, the lateral stability index, vertical stability index, frame lateral and vertical acceleration RMS values, wear count, and average wear index all show that as the vehicle's mileage increases, the larger the value on the ordinate, the more significant the optimization effect of the Opt wheel. However, when using the Opt profile, the derailment coefficient, wheel load reduction rate, and wheel-rail lateral force indicators are inferior to those of the LMA profile. The LMA profile, as a classic profile that has been in operation for many years, is itself an excellent wheel profile. Designing the Opt wheel profile is a strategic choice, sacrificing a certain amount of flange wear to ensure that the vehicle maintains certain vehicle dynamics indicators throughout the entire wheel refinishing cycle.

[0105] Considering the technical problems of complex and unstable models, low simulation efficiency, and uncertain parameter attributes in existing technologies, this embodiment proposes a new composite equivalent taper parameter for wheel-rail contact geometry. The new composite equivalent taper is obtained by weighting and summing the linear equivalent tapers under different wheelset lateral displacements, based on the proportion of wheel-rail contact width to the total wheel-rail contact width. An approximate model of the composite equivalent taper is established using a radial basis function neural network model. An optimal Latin hypercube design is used to approximate the training and test sets to verify the accuracy of the approximate model. The relationship between the composite equivalent taper and vehicle stability and wheel wear performance is calculated using a vehicle dynamics model. During wheel profile optimization calculations, four circular arc parameters are used to describe the wheel profile from the throat circle to the outer edge of the tread. This new wheel profile is obtained by combining it with the target profile wheel flange. The composite equivalent taper is used as the optimization objective and set as the fitness function. A particle swarm optimization algorithm is used to optimize the circular arc parameters describing the wheel profile, resulting in the optimized wheel profile. The vehicle's dynamic performance and wheel wear performance before and after wheel profile optimization are compared to verify the optimization results.

[0106] Example 2

[0107] Reference Figure 8 This embodiment provides a wheel profile optimization device based on composite equivalent taper, which includes:

[0108] (1) Relationship determination module, which is used to obtain the relationship between composite equivalent taper and vehicle lateral stability and wheel wear performance based on vehicle dynamics model and vehicle and track conditions.

[0109] In the implementation process, the composite equivalent conicity is obtained by weighting and summing the linear equivalent conicities of the wheelset under different lateral displacement amounts according to the proportion of the wheel-rail contact width in the total wheel-rail contact width. In this way, the influence of the change of the entire wheel-rail contact area on the wheel-rail contact geometry is considered, and the linear equivalent conicities under different lateral displacement amounts of the wheelset are weighted and processed, so as to ensure the accurate description of the wheel-rail contact geometry state.

[0110] (2) a parameter optimization module, configured to: based on the relationship between the composite equivalent conicity and the vehicle lateral stability and the wheel wear performance, take the minimization of the composite equivalent conicity as an objective function, and take the value range of the circular arc parameters of the wheel profile as a constraint function, to obtain the optimized value of the circular arc parameters of the wheel profile, that is, the optimized wheel profile.

[0111] The circular arc parameters of the wheel profile include the transition circular arc, the middle circular arc of the profile, the horizontal coordinate of the center of the transition circular arc, and the horizontal coordinate of the center of the middle circular arc of the profile.

[0112] It should be noted that the circular arc parameters of the wheel profile can include the rim root circular arc and the outer side reverse circular arc in addition to the above-mentioned parameters, and the person skilled in the art can select them according to the actual situation.

[0113] In the implementation process, a radial basis function neural network model is used to construct an approximate model of the composite equivalent conicity.

[0114] The radial basis function neural network model (RBF model) can approximate any nonlinear function with arbitrary precision and has global approximation capability, which fundamentally avoids local optimization problems, and has a compact topology structure, structure parameters can be learned separately, and the convergence speed is fast.

[0115] Optimal Latin hypercube design is used to train and test sets to verify the accuracy of the approximate model. The samples in the training set and the test set are composed of input and output. The input is the circular arc parameter of the wheel profile, and the output is the vehicle lateral stability and the wheel wear performance. In some embodiments, the particle swarm optimization algorithm is used to optimize the circular arc parameters of the wheel profile to obtain the optimized wheel profile. The particle swarm optimization algorithm is simple to operate, fast in convergence, and easy to adjust the algorithm according to different actual needs, so the optimization algorithm selects the particle swarm optimization algorithm.

[0116] It should be noted that each module in this embodiment corresponds to each step in Embodiment One, and the specific implementation process is the same, which will not be repeated here.

[0117] Embodiment Three

[0118] The embodiment provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize steps in the wheel profile optimization method based on composite equivalent conicity as described above.

[0119] Embodiment Four

[0120] The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor realizes steps in the wheel profile optimization method based on composite equivalent conicity as described above when executing the program.

[0121] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system) and computer program product according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one block or multiple blocks.

[0122] The above only describes the preferred embodiments of the present application and is not used to limit the present application. The present application can have various changes and modifications for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing a wheel profile based on a composite equivalent conicity, characterized in that, The method comprises the following steps: obtaining the relationship between the composite equivalent conicity and the vehicle lateral stability and the wheel wear performance based on a vehicle dynamics model and vehicle and track working conditions; obtaining the optimized wheel profile based on the relationship between the composite equivalent conicity and the vehicle lateral stability and the wheel wear performance, taking minimization of the composite equivalent conicity as an objective function, and taking the value range of the circular arc parameters of the wheel profile as a constraint function. The composite equivalent conicity is obtained by weighting and summing the linear equivalent conicities of the wheelset under different lateral displacements according to the proportion of the wheel-rail contact width in the total wheel-rail contact width.

2. The composite equivalent conicity based wheel profile optimization method of claim 1, wherein, The circular arc parameters of the wheel profile include the transition circular arc, the middle profile circular arc, the horizontal coordinate of the center of the transition circular arc, and the horizontal coordinate of the center of the middle profile circular arc.

3. The composite equivalent conicity based wheel profile optimization method of claim 1, wherein, An approximate model of the composite equivalent conicity is constructed by using a radial basis neural network model.

4. The composite equivalent conicity based wheel profile optimization method of claim 3, wherein, The accuracy of the approximate model is verified by using optimal Latin hypercube design training sets and test sets, wherein the input quantity is the circular arc parameters of the wheel profile, and the output quantity is the vehicle lateral stability and the wheel wear performance.

5. The composite equivalent conicity based wheel profile optimization method of claim 1, wherein, The circular arc parameters of the wheel profile are optimized based on a particle swarm algorithm to obtain the optimized wheel profile.

6. A device for optimizing a wheel profile based on a composite equivalent conicity, characterized in that, The method comprises the following steps: a relationship determination module is configured to obtain the relationship between the composite equivalent conicity and the vehicle lateral stability and the wheel wear performance based on a vehicle dynamics model and vehicle and track working conditions; a parameter optimization module is configured to obtain the optimized wheel profile based on the relationship between the composite equivalent conicity and the vehicle lateral stability and the wheel wear performance, taking minimization of the composite equivalent conicity as an objective function, and taking the value range of the circular arc parameters of the wheel profile as a constraint function. The composite equivalent conicity is obtained by weighting and summing the linear equivalent conicities of the wheelset under different lateral displacements according to the proportion of the wheel-rail contact width in the total wheel-rail contact width.

7. The composite equivalent conicity based wheel profile optimization apparatus of claim 6, wherein, The circular arc parameters of the wheel profile include the transition circular arc, the middle profile circular arc, the horizontal coordinate of the center of the transition circular arc, and the horizontal coordinate of the center of the middle profile circular arc.

8. The composite equivalent conicity based wheel profile optimization apparatus of claim 6, wherein, An approximate model of the composite equivalent conicity is constructed by using a radial basis neural network model.

9. The composite equivalent conicity based wheel profile optimization apparatus of claim 8, wherein, The accuracy of the approximate model is verified by using optimal Latin hypercube design training sets and test sets, wherein the input quantity is the circular arc parameters of the wheel profile, and the output quantity is the vehicle lateral stability and the wheel wear performance.

10. The composite equivalent conicity based wheel profile optimization apparatus of claim 6, wherein, The circular arc parameters of the wheel profile are optimized based on a particle swarm algorithm to obtain the optimized wheel profile.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by a processor to implement the steps in the wheel profile optimization method based on the composite equivalent conicity according to any one of claims 1-5.

12. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps in the wheel profile optimization method based on the composite equivalent conicity according to any one of claims 1-5.

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