A metro wheel-rail matching method based on wear evolution and wheel-rail dynamics characteristics

Abstract

The application discloses a metro wheel-rail matching method based on wear evolution and wheel-rail dynamic characteristics, belongs to the technical field of metro wheel-rail matching methods, selects three hardness states of commonly used wheels and rails, establishes a wheel-rail wear iteration calculation model, and calculates wheel profile and rail profile wear evolution results under different hardness conditions; the wheel and rail wear profiles of the same service stage are matched under typical metro curve working conditions, and wheel-rail dynamic simulation is carried out, and key indexes such as derailment coefficient and lateral creep rate are collected; finally, a comprehensive evaluation method of nine groups of wheel-rail hardness matching schemes is constructed, the wheel-rail hardness matching scheme with better dynamic response is screened out, the limitations of traditional experience type matching and single test result matching are broken through, the hardness matching evaluation system of the long-term service evolution process of the wheel-rail is obtained through comprehensive analysis of material hardness, wear profile and dynamic performance, and perfect hardness matching basis is provided for the metro wheel-rail system.

Description

Technical Field

[0001] This invention belongs to the technical field of metro wheel-rail matching methods, specifically relating to a metro wheel-rail matching method based on wear evolution and vehicle-rail dynamics characteristics. Background Technology

[0002] Existing wheel-rail matching methods primarily focus on wheel-rail profile design, lacking a systematic study of the hardness matching of wheel and rail materials and the evolution of wear. In the field of wheel-rail wear calculation and dynamic simulation, existing methods typically conduct wear calculations and wear evolution analyses under fixed wheel and rail material hardness conditions, rarely considering the impact of wheel-rail matching with different hardness and changes in wheel and rail material hardness on train dynamic performance during long-term service. Therefore, existing wheel-rail systems are prone to problems such as excessively rapid wear rates, wheel-rail profile mismatch, and deterioration of dynamic performance during service, increasing wheel-rail maintenance costs and operational safety hazards.

[0003] To address the aforementioned issues, this invention proposes a metro wheel-rail matching method based on wear evolution and rail dynamics. This method combines the wheel profile wear evolution results under different wheel hardness conditions, the rail profile wear evolution results under different rail hardness conditions, and subsequent dynamic evaluation results to form a systematic wheel-rail hardness matching analysis and evaluation method, providing a reference for metro wheel-rail system material selection and operation and maintenance. Summary of the Invention

[0004] The purpose of this invention is to provide a metro wheel-rail matching method based on wear evolution and vehicle-rail dynamics. This invention achieves a systematic evaluation of metro wheel-rail hardness matching through wear evolution analysis, dynamic simulation, and multi-scheme comparison and optimization, thereby addressing the insufficient consideration of material hardness matching and its long-term service impact in existing wheel-rail matching studies. First, three hardness gradient parameters for CL50 wheel steel and U74 rail steel are selected. An iterative wheel-rail wear calculation model is established based on the UM multibody dynamics platform to calculate the wear profiles of wheels and rails of the same material with different hardnesses. Then, under typical metro curve conditions, dynamic simulations are performed on the wear profiles at different service stages, collecting key indicators such as derailment coefficient and wheel-rail lateral creep rate. Finally, by constructing nine wheel-rail hardness matching schemes, the vehicle-rail dynamic responses of different schemes are comprehensively compared, thereby selecting the wheel-rail hardness matching scheme with superior overall performance.

[0005] This invention is achieved through the following technical solution: A metro wheel-rail matching method based on wear evolution and track dynamics includes the following steps: S1: Select wheel and rail base materials, and select three typical hardness states according to low hardness, medium hardness and high hardness; S2: Establish an iterative calculation model for wheel-rail wear; S3: Input the different hardnesses of the wheel and rail substrate into the wheel-rail wear iterative calculation model for wear calculation, and obtain the wear profiles of wheels and rails with different hardnesses respectively. S4: Match all wheel wear profiles with all rail wear profiles, and conduct vehicle-rail coupled dynamics simulation to select the wheel-rail hardness matching scheme with better overall performance.

[0006] Preferably, in step S1, wheel steel of grade CL50 is selected as the wheel base material, with low hardness, medium hardness and high hardness of 255HB, 271HB and 335HB respectively; rail steel of grade U74 is selected as the rail base material, with low hardness, medium hardness and high hardness of 250HB, 265HB and 296HB respectively.

[0007] Preferably, in step S2, the specific operation method for establishing the wheel-rail wear iterative calculation model is as follows: first, a vehicle-track coupled dynamic model is established, which includes a vehicle subsystem and a track subsystem, and the coupling between the two subsystems is realized through the wheel-rail dynamic contact calculation module; the Arcard wear calculation module and the profile iterative update module are introduced into the wheel-rail dynamic contact calculation module of the vehicle-track coupled dynamic model, thereby constructing the wheel-rail wear iterative calculation model.

[0008] Preferably, the vehicle-track coupled dynamics model is established using Universal Mechanism software; based on the vehicle-track coupled dynamics model, a wheel-rail wear calculation module based on Archard wear theory is introduced to construct an iterative wheel-rail wear calculation model; wherein, the wheel-rail wear volume is directly proportional to the normal contact force and sliding distance within the contact patch, and inversely proportional to the hardness of the wheel or rail material, and its calculation formula is as follows:

[0009] Where V is the wear volume, k v N is the wear coefficient. z d is the normal contact force, d is the sliding distance, and H is the material hardness.

[0010] Preferably, in step S3, when performing wear calculations on rail profiles and wheel profiles with different hardness, CHN60 rail profiles and LM wheel profiles are used as initial geometric matching profiles; and different hardness values ​​of U74 rail steel and CL50 wheel steel are used as material hardness parameters and input into the Archard wear model.

[0011] Preferably, the wheel profile and rail profile are calculated using independent wear evolution; wherein, when calculating the wear of the rail profile, only the hardness parameter of the rail material is changed, while the wheel side uses a uniform preset hardness parameter; when calculating the wear of the wheel profile, only the hardness parameter of the wheel material is changed, while the rail side uses a uniform preset hardness parameter.

[0012] Preferably, when calculating the wear of rail or wheel profile, the total track includes straight sections, transition curve sections and circular curve sections in the calculation process, with a total track length of 200m. The total length of the transition curve section and the circular curve section is 100m. The curve radius of the circular curve section includes two types: 600m and 800m. The wear calculation position is set on the entire curve section, and the wear of the straight section is not calculated.

[0013] Preferably, in step S4, during the vehicle-rail coupled dynamics simulation, three wheel wear profiles under different service stages are selected as the analysis objects, wherein the first stage is 1.0×10 4 km, the second phase is 1.5 × 10 4 km, the third stage is 2.0×10 4 km; the cumulative weight T passing through the corresponding stage of the rail can be expressed as:

[0014] Where G is the total weight of the train, S is the cumulative mileage of the wheels, and L is the length of the track for wear calculation.

[0015] Preferably, in the vehicle-rail coupled dynamics simulation, at each curve radius and each service stage, the wheel wear profile obtained under different hardness conditions is matched with the rail wear profile to obtain nine sets of wheel-rail hardness matching schemes. Dynamic calculations are performed on the nine sets of wheel-rail hardness matching schemes respectively, the corresponding dynamic evaluation indicators are extracted, and the dynamic evaluation indicators of each wheel-rail hardness matching scheme are compared to obtain comprehensive evaluation index data.

[0016] Preferably, the target wheel-rail hardness matching scheme is determined based on dynamic evaluation indicators, including the maximum value of the derailment coefficient, the maximum value of the wheel-rail lateral creep rate, and the maximum value of the friction work.

[0017] Compared with the prior art, the present invention has the following advantages and beneficial effects: This invention constructs a systematic evaluation method for metro wheel-rail matching by analyzing wear evolution, dynamic simulation, and comprehensive comparison of multiple schemes. First, three hardness gradient parameters for CL50 wheel steel and U74 rail steel are selected. Based on the UM multibody dynamics platform, an iterative calculation model for wheel-rail wear is established to calculate the wear evolution of wheel profiles with different hardnesses and rail profiles with different hardnesses of the same material. Then, under typical metro curve conditions, dynamic simulations are performed on wear profiles at different service stages, collecting key indicators such as derailment coefficient and lateral creep rate. Finally, a comprehensive evaluation system of nine wheel-rail hardness matching schemes is constructed, and the scheme with superior overall performance is selected. This method overcomes the limitations of traditional empirical matching and experimental result-based matching. Through comprehensive analysis of material hardness, wear profile, and dynamic performance, it provides a complete hardness matching basis for metro wheel-rail systems, thereby reducing wheel-rail wear and improving train operation stability.

[0018] In this invention, the key to wheel-rail hardness matching is not just the hardness of the wheel or the rail itself, but the combined relationship between the two, i.e., the wheel-rail hardness ratio. Matching different wheel hardness values ​​with different rail hardness values ​​will result in different wheel-rail hardness ratios, which will have varying impacts on the dynamic behavior, contact state, and wear evolution of the wheel-rail system, leading to different service outcomes. Therefore, this invention does not rely solely on a static comparison based on a single initial wheel-rail profile and a single material hardness. Instead, it uses wheels and rails of the same type but with different hardnesses to perform long-term wear evolution calculations, and then conducts a dynamic evaluation based on the worn wheel-rail profile. Thus, the obtained wheel-rail hardness matching results are closer to the actual service state of the wheel-rail system. Furthermore, this invention does not judge the wheel-rail hardness matching relationship using a single indicator, but simultaneously uses the derailment coefficient, wheel-rail lateral creep rate, and friction work as comprehensive evaluation indicators. It systematically evaluates the wheel-rail hardness matching conditions from three aspects: operational safety, wheel-rail contact state, and wear behavior, resulting in a more comprehensive and reliable evaluation. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the wheel-rail hardness matching process in this invention.

[0021] Figure 2 This is a schematic diagram of the nine sets of wheel-rail hardness matching relationships in this invention.

[0022] Figure 3 This is a schematic diagram of the rail wear calculation process in this invention.

[0023] Figure 4 This is a schematic diagram of the wheel wear calculation process in this invention.

[0024] Figure 5 This is a schematic diagram illustrating the influence of different wheel-rail hardness matching schemes on the derailment coefficient in this invention.

[0025] Figure 6 This is a schematic diagram illustrating the influence of different wheel-rail hardness matching schemes on the lateral creep rate of the wheel and rail in this invention.

[0026] Figure 7 This is a schematic diagram illustrating the influence of different wheel-rail hardness matching schemes on friction work in this invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0028] Example 1:

[0029] A subway wheel-rail matching method based on wear evolution and rail dynamics first selects the base materials of the wheel and rail, and then determines three typical hardness states; such as Figure 1 and Figure 2 As shown, CL50 wheel steel and U74 rail steel were selected as base materials, and three typical hardness states—low, medium, and high—were determined. The low, medium, and high hardness of the wheel base material were 255 HB, 271 HB, and 335 HB, respectively; the low, medium, and high hardness of the rail base material were 250 HB, 265 HB, and 296 HB, respectively. Among the selected base materials, the wheel grade and rail grade were the same for all three hardness levels. During the manufacturing process of wheels or rails of the same grade, different microstructures and thus different hardness levels can be achieved by adjusting the heat treatment process, cooling regime, and tempering parameters.

[0030] After determining the base materials for the wheels and rails, a vehicle-track coupled dynamics model was first established in Universal Mechanism (UM) multibody dynamics software. The vehicle model parameters adopted a metro Type A car, mainly composed of the car body, frame, and wheelsets, connected by primary and secondary suspensions. According to the vehicle-track coupled dynamics theory, the car body, frame, and wheelsets were considered rigid bodies, and the primary and secondary suspensions were considered spring-damped units. The track model consisted of rails and fasteners. The Kik-Piotrowski algorithm was used to calculate the contact patch shape and normal stress distribution for wheel-rail normal contact, while the FASTSIM algorithm based on Kalker's simplified theory was used to calculate the tangential stress and creep distribution for wheel-rail tangential contact.

[0031] Based on the vehicle-track coupled dynamics model, a wheel-rail wear calculation module based on Archard wear theory is introduced to construct an iterative wheel-rail wear calculation model. The wheel-rail wear volume is directly proportional to the normal contact force and sliding distance within the contact patch, and inversely proportional to the hardness of the wheel or rail material. The calculation formula is as follows:

[0032] Where V is the wear volume, k v N is the wear coefficient. z d is the normal contact force, d is the sliding distance, and H is the material hardness.

[0033] During the simulation, an iterative method was used to predict wheel-rail wear. For the rail, each iteration step corresponded to a preset passing weight; for the wheel, each iteration step corresponded to a preset running mileage. After each iteration step was completed, the wear profile obtained in the previous iteration step was used as the input for the next iteration step, until the preset total passing weight or total running mileage was reached.

[0034] The specific method for constructing the wheel-rail wear iterative calculation model is as follows: First, the vehicle-track coupled dynamics model is used to solve for contact parameters such as wheel-rail normal contact force, tangential stress, creep rate, and sliding distance; then, the contact parameters are input into the Archard wear calculation module, and the wear amount of the current iteration step is calculated in combination with the hardness of the wheel or rail material; subsequently, the profile iteration update module updates the wheel profile or rail profile of the current iteration step according to the calculated wear amount, forming a new wheel profile or rail profile; the updated profile continues to be used as the input for the next iteration step, thus forming the wheel-rail wear iterative calculation model.

[0035] When calculating rail wear, it is necessary to set vehicle conditions, track conditions, and iteration parameters. This embodiment selects 2.5 × 10⁻⁶. 5't' is the weight passed through in a single iteration of the rail wear evolution calculation. The weight passed through does not represent the instantaneous load actually borne by the rail at a given moment, but rather characterizes the cumulative total mass of vehicles passing through the calculated section of the rail within that iteration step. With this setting, within an iteration step, the rail profile remains unchanged. Vehicles pass through the target section under preset track conditions, and the wear amount calculated from wheel-rail contact is first accumulated within that iteration step. After the iteration step ends, the rail wear depth is amplified according to the weight passed through, and the rail profile is updated accordingly. The updated rail profile serves as the input for the next iteration step, continuing subsequent wear evolution calculations until the cumulative weight passed through the rail reaches the preset total weight, thus completing the long-term wear evolution analysis of the rail profile.

[0036] The parameters required for calculating wheel wear are similar to those for calculating rail wear; only the running mileage corresponding to wheel wear in one iteration step needs to be set. This embodiment uses 2000km as the running mileage corresponding to wheel wear in one iteration step. The calculation process for rail and wheel wear is as follows: Figure 3 and Figure 4 As shown.

[0037] from Figure 3 It can be seen that the rail wear calculation adopts an iterative update method. First, the initial rail profile is input into the vehicle-track coupled dynamics model to obtain the wheel-rail contact state and corresponding dynamic parameters. Then, the parameters are input into the wear prediction model to calculate the wear amount of the rail profile in the current iteration step, and the updated rail wear profile is obtained. Subsequently, this wear profile is used as the input profile for the next iteration step, and subsequent calculations are carried out. The above process is repeated cyclically. After each wear iteration step is completed, the corresponding rail weight increases by 2.5 × 10⁻⁶. 5 t, until the cumulative weight reaches the preset total weight, thereby realizing the long-term wear evolution calculation of the rail profile.

[0038] from Figure 4 As can be seen, wheel wear calculation also employs an iterative update method. First, the initial wheel profile is input into the vehicle-track coupled dynamics model to obtain the wheel-rail contact state and corresponding dynamic parameters. Then, the parameters are input into the wear prediction model to calculate the wear amount of the wheel profile in the current iteration step, obtaining the updated wheel wear profile. Subsequently, this wear profile is used as the input profile for the next iteration step, and subsequent calculations continue. This process is repeated cyclically. After each wear iteration step is completed, the corresponding wheel mileage increases by 2000 km until the cumulative mileage reaches the preset total mileage, thereby realizing the long-term wear evolution calculation of the wheel profile.

[0039] When calculating the wear of rail and wheel profiles with different hardness, CHN60 rail profile and LM wheel profile are used as the initial geometric matching profiles; the material hardness parameters are input according to different hardness values ​​for U74 rail steel and CL50 wheel steel, respectively. When calculating the wear of the rail profile, only the hardness parameters of the rail material are changed; a uniform preset hardness parameter is used on the wheel side. In this embodiment, the preset hardness parameter of the wheel is 300HB. When calculating the wear of the wheel profile, only the hardness parameters of the wheel material are changed; a uniform preset hardness parameter is used on the rail side. In this embodiment, the preset hardness parameter of the rail is 300HB.

[0040] Wear calculations employ a method that varies hardness on one side while maintaining a uniform preset hardness on the other side. Although this method does not directly simulate the synchronous wear process under a fixed wheel-rail hardness combination, it is not divorced from reality. First, this method is a moderate simplification of the actual service process under uniform reference conditions. This simplification does not change the main geometric evolution law of the wheel-rail profile under long-term wear, and therefore can still retain the main geometric features of the profile evolution after long-term service.

[0041] To establish a correspondence between wheel wear profiles and rail wear profiles across service stages, this invention uses a stage conversion relationship between train gross weight and wheel mileage to match wheel and rail profiles obtained at different stages. In subsequent simulation stage correspondence analysis, a representative train condition of 4 cars with a gross weight of 125t is used, combined with preset representative track conditions, to divide the wheel mileage into three service stages, corresponding to the three passing weight stages of the rail. Thus, the wheel and rail wear profiles formed under the same service stage can be re-matched and input into the vehicle-track coupled dynamics model for simulation evaluation. Through this method, wheel and rail profiles obtained under different hardness conditions can be compared on a unified service stage basis, ensuring that the wheel-rail hardness matching analysis has a consistent time scale and engineering comparability.

[0042] When calculating rail or wheel wear, the overall track conditions in the calculation include straight sections, transition curve sections, and circular curve sections. The length of the transition curve and circular curve sections is 100m, accounting for 50% of the total track length. The radius of the circular curve sections includes two options: 600m and 800m. Wear calculations are performed on the entire curved section; straight sections are not included in the calculations. This is because wheel or rail wear primarily occurs on curved sections, and curved sections better reflect the differences in wear and its evolution under different wheel and rail hardness conditions.

[0043] The material hardness parameters of the wheels and rails under different hardness states are input into the wheel-rail wear iterative calculation model for wear calculation, and the wear profiles of wheels and rails with different hardness are obtained respectively.

[0044] The wheel wear profiles obtained under different hardness conditions were matched with the rail wear profiles in pairs to form nine wheel-rail hardness matching schemes. The vehicle-rail coupled dynamics simulation was carried out to obtain the most suitable wheel-rail hardness matching result.

[0045] Example 2:

[0046] This embodiment verifies the feasibility of the hardness matching method through specific working condition simulation and data analysis. In order to simulate the long-term service state of the wheel-rail system, the wheel wear profile under three service stages is selected as the analysis object in the vehicle-rail coupled dynamics simulation. Among them, the first stage is 1.0×10 4 km, the second phase is 1.5 × 10 4 km, the third stage is 2.0×10 4 km; To achieve wheel-rail service stage matching, a representative train with a 4-car formation and a total weight of 125t is used for conversion in the stage correspondence analysis. The cumulative weight T passing through the corresponding rail position can be expressed as:

[0047] Where G represents the total weight of a representative train, S represents the cumulative wheel mileage, and L represents the track length for wear calculation. Based on the conditions of G=125t and L=0.1km in this embodiment, the rail passing weight corresponding to the three service stages is as follows: the rail passing weight corresponding to the first stage of wheel mileage is 1.250×10 7 t, the weight of the rail passing through the second stage of wheel running mileage is 1.875 × 10 7 t, the weight of the rail passing through the third stage of wheel running mileage is 2.500 × 10 7 t.

[0048] like Figure 1 As shown, during the vehicle-track coupled dynamics simulation, the wheel wear profile obtained under different hardness conditions is matched with the rail wear profile at each curve radius and each service stage, resulting in nine sets of wheel-rail hardness matching schemes. For example, a wheel hardness of 255 HB is matched with a rail hardness of 250 HB, which is one set.

[0049] Six sets of dynamic calculation conditions were set according to the curve radius and service stage in Table 1. Under each condition, vehicle-rail coupled dynamic calculations were carried out on different wheel-rail hardness matching schemes, and their dynamic evaluation indicators were extracted and compared to obtain comprehensive evaluation index data for each wheel-rail hardness matching scheme.

[0050]

[0051] Table 1: Comparison of Dynamic Simulation Working Condition Parameters As shown in Table 1, this embodiment sets up a total of 6 sets of dynamic simulation conditions. Conditions 1 to 3 correspond to three service stages under a curve radius of 600m, and conditions 4 to 6 correspond to three service stages under a curve radius of 800m. The three service stages are respectively based on a wheel mileage of 1.0 × 10⁻⁶ m. 4 km (initial service), 1.5×10 4 km (mid-service) and 2.0×10 4 The curve radius (km) is used for characterization, and the rail weight at each stage is used as the corresponding value. By combining the curve radius factor with the service stage factor, the dynamic response differences of different wheel-rail stiffness matching schemes under different curve conditions and different long-term service states can be compared within a unified analytical framework.

[0052] The target wheel-rail hardness matching scheme is determined based on the dynamic evaluation indicators, which include the derailment coefficient, wheel-rail lateral creep rate, and the maximum value of friction work.

[0053] Based on the operating parameters shown in Table 1, vehicle-rail coupled dynamics calculations were performed under operating conditions one through six, and the maximum values ​​of the corresponding derailment coefficient, wheel-rail lateral creep rate, and friction work were extracted to obtain... Figure 5 Derailment coefficient curves from working condition A1 to working condition F1 Figure 6 Wheel-rail lateral creep rate curves from working condition A2 to working condition F2 and Figure 7 Friction work curves from operating conditions A3 to F3 are shown. Each sub-graph represents the dynamic evaluation results obtained by pairwise matching of three wheel hardnesses with three rail hardnesses under the corresponding curve radius and service stage conditions. Figure 5 The maximum derailment coefficient under the corresponding matching scheme is given. Figure 6 The maximum lateral creep rate of the wheel and rail under the corresponding matching scheme is given. Figure 7 The maximum frictional work under the corresponding matching scheme is given.

[0054] by Figure 5 Taking working condition A1 as an example, when matching a wheel profile with a hardness of 255HB with a rail profile with a hardness of 250HB, the derailment coefficient calculation result corresponding to a hardness ratio of 1.020 on the horizontal axis can be obtained. When the wheel profile is selected with a hardness of 255HB and the rail profile with a hardness of 265HB, the derailment coefficient calculation result corresponding to a hardness ratio of 0.962 on the horizontal axis can be obtained; the remaining combinations are deduced in the same way. Therefore, by matching the three wheel hardnesses with the three rail hardnesses one by one, a total of nine wheel-rail hardness matching schemes and their corresponding data points can be obtained under this working condition. Figure 6 and Figure 7 The method of obtaining values ​​for each working condition and Figure 5They are the same, except that the evaluation indicators represented by the vertical axis are the maximum value of wheel-rail lateral creep rate and the maximum value of friction work, respectively.

[0055] exist Figure 5 In the diagram, operating conditions A1, B1, and C1 correspond to the initial, middle, and late service stages of wheel-rail operation under a curve radius of 600m, respectively. In operating condition A1, although the derailment coefficients corresponding to each hardness ratio fluctuate somewhat, the 335HB high-hardness wheel region remains relatively low overall. In operating conditions B1 and C1, this difference widens further, with a more pronounced distinction between the 335HB high-hardness wheel region and the 255HB and 271HB wheel regions. This indicates that under smaller curve radii, the advantage of high-hardness wheel matching schemes in reducing the derailment coefficient becomes more prominent with increasing service time. Under a curve radius of 800m, operating conditions D1, E1, and F1 exhibit the same consistent pattern, demonstrating that higher wheel hardness contributes to reducing the derailment coefficient at different service stages.

[0056] exist Figure 6 In Table 1, operating conditions A2, B2, and C2 correspond to the initial, middle, and late service stages of wheel-rail operation under a curve radius of 600m, respectively. Operating conditions D2, E2, and F2 correspond to the initial, middle, and late service stages under a curve radius of 800m, respectively. Under a curve radius of 600m, in operating conditions A2, B2, and C2, the lateral creep rate of the wheel-rail in the 335HB high-hardness wheel region is generally lower than that in the 255HB and 271HB wheel regions. This difference remains clearly observable in B2 and C2 as the service stage progresses from the initial to the late stage, indicating that the high-hardness wheel matching scheme has good stability across different service stages. Under a curve radius of 800m, operating conditions D2, E2, and F2 also exhibit a consistent variation pattern. (Summary) Figure 6 It can be seen that within the curve radius and service stage range studied in this embodiment, when using 335HB high-hardness wheels and matching them with different rail hardness, the overall wheel-rail lateral creep rate is lower, indicating that higher wheel hardness is beneficial to improving wheel-rail contact condition.

[0057] exist Figure 7In Table 1, operating conditions A3, B3, and C3 correspond to the initial, middle, and late service stages of wheel-rail operation under a curve radius of 600m, respectively. Operating conditions D3, E3, and F3 correspond to the initial, middle, and late service stages under a curve radius of 800m, respectively. In operating conditions A3, B3, and C3, the frictional work in the 335HB high-hardness wheel region is significantly lower than in the first two wheel hardness regions, especially in B3 and C3, where this decrease is more pronounced. In contrast, the frictional work in the 255HB and 271HB wheel regions is generally higher, with larger fluctuations. Under a curve radius of 800m, operating conditions D3, E3, and F3 also exhibit the same pattern. Therefore, using 335HB high-hardness wheels with a wheel hardness slightly higher than the rail hardness is more beneficial in reducing the frictional work during wheel-rail contact, thereby mitigating wheel-rail wear to some extent.

[0058] Therefore, under the conditions of subway curve operation, wheel-rail wear stage and vehicle parameters corresponding to this embodiment, using 335HB high-hardness wheel material, and making the hardness of the wheel material slightly higher than that of the rail material, is generally more conducive to reducing the derailment coefficient, wheel-rail lateral creep rate and friction work during train operation.

[0059] The conclusion is that, under the conditions of this embodiment, a better overall response level can be achieved when using 335HB high-hardness wheels for matching. The other parts of this embodiment are the same as those in the previous embodiments and will not be repeated here.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A subway wheel-rail matching method based on wear evolution and track dynamics characteristics, characterized in that, Includes the following steps: S1: Select wheel and rail base materials, and select three typical hardness states according to low hardness, medium hardness and high hardness; S2: Establish a wheel-rail wear iterative calculation model. First, establish a vehicle-track coupled dynamics model, which includes a vehicle subsystem and a track subsystem. The coupling between the two subsystems is realized through a wheel-rail dynamic contact calculation module. Then, a wear calculation module and a profile iterative update module are introduced into the wheel-rail dynamic contact calculation module of the vehicle-track coupled dynamics model to construct a wheel-rail wear iterative calculation model. S3: Input the material hardness parameters of the wheel and rail under different hardness states into the wheel-rail wear iterative calculation model for wear calculation, and obtain the wear profile of the wheel and rail with different hardness respectively. S4: Match all wheel wear profiles with all rail wear profiles and conduct vehicle-rail coupled dynamics simulation to select the wheel-rail hardness matching scheme with better overall performance; in the vehicle-rail coupled dynamics simulation, wheel wear profiles under three service stages are selected as the analysis objects; the cumulative weight T of the rail in the corresponding stage can be expressed as: ； Where G is the total weight of the train, S is the cumulative mileage of the wheels, and L is the length of the track for wear calculation. In the vehicle-rail coupled dynamics simulation, at each curve radius and each service stage, the wheel wear profile obtained under different hardness conditions is matched with the rail wear profile to obtain nine sets of wheel-rail hardness matching schemes. Dynamic calculations are performed on each of the nine sets of wheel-rail hardness matching schemes, and the corresponding dynamic evaluation indicators are extracted. The dynamic evaluation indicators of each wheel-rail hardness matching scheme are compared to obtain comprehensive evaluation index data. Based on the dynamic evaluation indicators, the target wheel-rail hardness matching scheme is determined. The dynamic evaluation indicators include the maximum value of the derailment coefficient, the maximum value of the wheel-rail lateral creep rate, and the maximum value of the friction work.

2. The metro wheel-rail matching method based on wear evolution and track dynamics as described in claim 1, characterized in that, In step S1, wheel steel of grade CL50 is selected as the wheel base material, with low hardness, medium hardness and high hardness of 255HB, 271HB and 335HB respectively; rail steel of grade U74 is selected as the rail base material, with low hardness, medium hardness and high hardness of 250HB, 265HB and 296HB respectively.

3. The metro wheel-rail matching method based on wear evolution and track dynamics as described in claim 1, characterized in that, The vehicle-track coupled dynamics model was established using Universal Mechanism software. Based on this model, a wheel-rail wear calculation module based on Archard wear theory was introduced to construct an iterative wheel-rail wear calculation model. The wheel-rail wear volume is directly proportional to the normal contact force and sliding distance within the contact patch, and inversely proportional to the hardness of the wheel or rail material. The calculation formula is as follows: ； Where V is the wear volume, k v N is the wear coefficient. z d is the normal contact force, d is the sliding distance, and H is the material hardness.

4. The metro wheel-rail matching method based on wear evolution and track dynamics as described in claim 1, characterized in that, In step S3, when calculating the wear of rail profiles and wheel profiles with different hardness, CHN60 rail profile and LM wheel profile are used as the initial geometric matching profiles; and different hardness values ​​of U74 rail steel and CL50 wheel steel are used as material hardness parameters and input into the Archard wear model.

5. The metro wheel-rail matching method based on wear evolution and track dynamics as described in claim 4, characterized in that, The wear evolution of the wheel profile and the rail profile is calculated independently. When calculating the wear of the rail profile, only the hardness parameter of the rail material is changed, while the wheel side uses a uniform preset hardness parameter. When calculating the wear of the wheel profile, only the hardness parameter of the wheel material is changed, while the rail side uses a uniform preset hardness parameter.

6. The metro wheel-rail matching method based on wear evolution and track dynamics as described in claim 4, characterized in that, When calculating the wear of rails or wheel profiles, the total track in the calculation process includes straight sections, transition curve sections, and circular curve sections. The total track length is 200m, of which the total length of the transition curve sections and circular curve sections is 100m. The curve radius of the circular curve section includes two types: 600m and 800m. The wear calculation position is set on the entire curve section, and the wear of the straight section is not calculated.

7. The metro wheel-rail matching method based on wear evolution and track dynamics as described in claim 1, characterized in that, In step S4, the specific mileage of the wheels under the three service stages is selected as follows: the first stage is 1.0 × 10 4 km, the second phase is 1.5 × 10 4 km, the third stage is 2.0 × 10 4 km.

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