Method for optimally designing profile of small-radius curve steel rail of heavy haul railway

Abstract

The invention provides a heavy haul railway small-radius curve steel rail profile optimization design method, and belongs to the technical field of heavy haul railway steel rails. The method comprises the following steps: establishing a steel rail profile optimization design scheme data set; establishing a simulation analysis model; inputting the steel rail profile optimization design scheme data set into a simulation analysis model for analogue simulation and computational analysis to obtain vehicle dynamics performance and wheel-rail contact characteristics corresponding to the multiple curve steel rail profiles; the freight vehicle operation performance, the steel rail abrasion and damage trend and the steel rail grinding economy of the new profile of each curve steel rail are comprehensively evaluated; and according to a comprehensive evaluation result, selecting an optimal new profile of the curve steel rail as a final design. By means of the method, abrasion, damage trends and grinding economy are comprehensively evaluated, the service life of the steel rail is prolonged, the maintenance cost is reduced, and therefore the optimal new profile can be selected as design, an efficient and economical solution is provided for the heavy haul railway small-radius curve steel rail, and the method is suitable for large-scale popularization and application. And the overall performance and economical efficiency of railway operation are remarkably improved.

Description

Technical Field

[0001] The invention belongs to the technical field of heavy-duty railway rails, and in particular relates to a method for optimizing the profile of a heavy-duty railway small-radius curve rail. Background Art

[0002] In heavy-haul railway transportation, the service life of rails in small-radius curve areas has always been a key issue for railway maintenance and safety. Heavy-haul railways are defined as railways that meet two of the following three conditions: traction mass of 8,000 tons or more, axle weight of 27 tons or more, and annual transportation volume of more than 40 million tons on a line section of at least 150 kilometers. Small-radius curves refer to curves with a radius of 800 meters or less. Under these conditions, rail wear and contact fatigue damage are particularly prominent, seriously affecting the service life of the rails and the safety of railway operation.

[0003] The existing heavy-haul railway rail profile optimization design methods mainly have the following problems:

[0004] Personalized rail profile design method: This method designs rail grinding target profiles for different curves based on the characteristics of different curved lines, through wheel-rail system dynamics analysis and calculation, with the minimum grinding amount as the principle. However, this method lacks analysis and prediction of the long-term evolution of the rail service status, which leads to early contact fatigue damage after grinding.

[0005] Optimal method for grinding profile of heavy-duty railway rails based on wear control: This method is guided by the control of rail wear, establishes a finite element model of steady-state rolling contact between wheel and rail, and obtains the optimal profile by comparing the wheel-rail contact characteristics corresponding to different profiles. However, with the widespread application of high-strength rails and lubrication technology, wear is no longer a key factor restricting the service life of heavy-duty railway rails. This method does not take into account the possible contact fatigue damage of rails, and lacks dynamic wheel-rail interaction analysis when trains pass through curves.

[0006] Multi-objective design method for heavy-duty railway curved rail profile: This method takes wear index, contact stress, derailment coefficient, wheel weight reduction rate and stability index as optimization targets, uses the analytic hierarchy process to establish the weights of each index, and solves the optimal curved rail profile through genetic iteration. Although this method takes many factors into consideration, it does not consider the impact of the difference between the actual profile and the designed profile on the amount of rail grinding. The measured profile may require a lot of grinding work to meet the design profile requirements, which increases the rail maintenance cost and shortens the service life of the rail.

[0007] In summary, the existing technology has limitations in the optimization design of rail profiles and cannot fully meet the demand for extending the service life of heavy-duty railway small-radius curve rails. Therefore, it is necessary to develop a new method for optimizing the profile design of heavy-duty railway small-radius curve rails to extend the service life of rails, ensure the safety of freight train operation and good dynamic performance, and take into account the economy of rail grinding operations. It is based on this background that the present invention proposes a new method for optimizing the profile design of heavy-duty railway small-radius curve rails to solve the deficiencies in the existing technology. Summary of the invention

[0008] The purpose of the present invention is to propose a method for optimizing the profile design of a heavy-duty railway small-radius curve rail to extend the service life of the rail, ensure the safety of freight train operation and good dynamic performance, and take into account the economy of the rail grinding operation.

[0009] To achieve the above-mentioned purpose, the present invention proposes a method for optimizing the profile design of a small-radius curve rail for a heavy-duty railway, wherein a data set of optimized design schemes for the profile of the rail is established; a simulation analysis model is established; the data set of optimized design schemes for the profile of the rail is input into the simulation analysis model for simulation and calculation analysis to obtain multiple new profiles of the curved rail; the running performance of freight vehicles, the wear and damage trend of the rails, and the economy of rail grinding of each new profile of the curved rail are comprehensively evaluated; and according to the results of the comprehensive evaluation, the optimal new profile of the curved rail is selected as the final design.

[0010] Compared with the prior art, the present invention has the following characteristics and advantages:

[0011] The proposed method for optimizing the profile design of heavy-duty railway small-radius curve rails significantly extends the service life of rails, improves the safety and dynamic performance of freight trains, reduces maintenance costs, and achieves the economy of rail grinding operations. This method is based on actual operation data and uses advanced simulation models and comprehensive evaluation systems to ensure the optimal balance of safety, performance, and economy in the design solution, providing an efficient and reliable solution for railway operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings described herein are only for explanation purposes and are not intended to limit the scope of the present invention in any way. In addition, the shapes and proportional dimensions of the various components in the figures are only schematic, used to help understand the present invention, and are not specifically limited to the shapes and proportional dimensions of the various components of the present invention. Those skilled in the art can select various possible shapes and proportional dimensions to implement the present invention according to the teachings of the present invention.

[0013] Figure 1 It is a schematic diagram of the optimization design concept of the small radius curve rail profile of the railway of the present invention;

[0014] Figure 2 It is a schematic diagram of the method for optimizing the profile design of a small-radius curve rail for heavy-haul railways according to the present invention;

[0015] Figure 3 It is a schematic diagram of the simulation and computational analysis method of the present invention. DETAILED DESCRIPTION

[0016] The details of the present invention can be more clearly understood with reference to the accompanying drawings and the description of the specific embodiments of the present invention. However, the specific embodiments of the present invention described herein are only used for the purpose of explaining the present invention and cannot be understood as limiting the present invention in any way. Under the guidance of the present invention, technicians can conceive of any possible variations based on the present invention, which should all be considered to belong to the scope of the present invention.

[0017] The present invention proposes a method for optimizing the profile of a heavy-duty railway small-radius curve rail, establishing a data set of optimized design solutions for the profile of the rail, establishing a simulation analysis model, inputting the data set of optimized design solutions for the profile of the rail into the simulation analysis model for simulation and calculation analysis to obtain a plurality of new profiles of the curve rail, comprehensively evaluating the running performance of freight vehicles, the wear and damage trend of the rail, and the economy of rail grinding of each new profile of the curve rail, and selecting the optimal new profile of the curve rail as the final design according to the result of the comprehensive evaluation.

[0018] The proposed method for optimizing the profile design of heavy-duty railway small-radius curve rails generates and evaluates multiple new profiles by establishing a data set and a simulation model, thereby ensuring the scientificity and practicality of the design and improving the safety and comfort of train operation. At the same time, by comprehensively evaluating the wear, damage trend, and grinding economy, the optimal new profile can be selected as the design, taking into account the extension of the service life of the rails and the reduction of maintenance costs, providing an efficient and economical solution for heavy-duty railway small-radius curve rails, and significantly improving the overall performance and economy of railway operations.

[0019] In an optional embodiment of the present invention, a data set of rail profile optimization design solutions is established, including: analyzing and summarizing relevant data of heavy-duty railway small-radius curve rails into rail state data and rail service condition data; analyzing key areas of small-radius curve rail profiles that require optimized design based on the rail state data and in combination with the rail service condition data; and changing geometric parameters of key areas to propose rail profile optimization design solutions with different geometric parameters.

[0020] In an optional example of this implementation, the rail status data includes the material, wear, contact fatigue damage and rail profile of the rail in service on site, wherein the rail material mainly includes the rail grade and strength and toughness.

[0021] In an optional example of this embodiment, the rail service condition data includes line parameters, vehicle parameters and wheel profiles, wherein the vehicle parameters and wheel profiles include parameters and wheel profiles of all types of freight vehicles running on the target curve.

[0022] In an optional example, different geometric parameters include geometric dimensions of critical areas of the rail profile and tangency conditions of arcs in different areas.

[0023] Specifically, based on the actual wear and damage of the target small radius curve rail, combined with the measured wheel profile and the measured rail profile wheel-rail geometry matching calculation, the key areas of the small radius curve rail profile that need to be optimized are analyzed. Based on the principle of controlling a single variable, the geometric dimensions of the key areas of the rail profile (arc length, radius, etc.) are changed in a certain step size. The arcs in different areas are connected using a tangent relationship to ensure a smooth transition of the connecting parts of the arcs in different areas. The rail profile optimization design schemes with different geometric parameters are proposed, and a rail profile optimization design scheme data set is established.

[0024] In an optional embodiment of the present invention, the simulation analysis model includes a vehicle-track coupling dynamics model and a wheel-rail contact finite element model.

[0025] In an optional example of this implementation, the vehicle-track coupling dynamics model includes:

[0026] Vehicle system dynamics model, used to simulate the motion response of the vehicle on the track;

[0027] Track system dynamics model, used to simulate the dynamic response of the track under vehicle loads.

[0028] Specifically, based on multi-body dynamics theory and wheel-rail contact theory, the line parameters, vehicle parameters and wheel-rail profile are input to establish a heavy-duty railway freight vehicle-track coupling dynamics model. The dynamics model also needs to reserve data input interfaces such as wheel profile, rail profile, curve parameters and track geometric irregularities.

[0029] In an optional example, the vehicle system dynamics model includes dynamic simulation of the vehicle body, bogie, wheelset, axle box, suspension system and wheel profile.

[0030] In an optional example, the track system dynamics model includes dynamic simulation of rails, sleepers, ballast bed and track geometric irregularities.

[0031] In an optional example of this embodiment, the wheel-rail contact finite element model is based on Hertz contact theory and Kalker contact algorithm.

[0032] In an optional example of this embodiment, the wheel-rail contact finite element model also includes an input interface for reserved wheel and rail geometric parameters, material parameters, etc.

[0033] Specifically, based on the finite element method and elastic-plastic mechanics theory, finite element analysis software is used to import the wheel profile and rail profile geometry data, mesh the wheel-rail components, establish a wheel-rail contact finite element model, and reserve input interfaces for wheel-rail material performance data and wheel-rail load data.

[0034] In an optional example of this implementation, the simulation and computational analysis include:

[0035] Call rail profiles with different geometric parameters from the rail profile optimization design solution data set;

[0036] Input the called rail profile into the heavy-duty railway vehicle-track coupling dynamics model to calculate the vehicle dynamics performance and wheel-rail contact characteristic indicators;

[0037] Input the calculation results into the wheel-rail contact finite element model to calculate the wheel-rail contact stress and its distribution characteristics;

[0038] Analyze calculation results and evaluate the performance of different rail profile designs.

[0039] Specifically, the rail profiles with different geometric parameters in the rail profile optimization design scheme data set established in the step of establishing the rail profile optimization design scheme data set are input into the heavy-duty railway vehicle-track coupling dynamics model established in the step of establishing the simulation analysis model. Through numerical simulation, the vehicle dynamics performance and wheel-rail interaction force corresponding to different optimization design schemes of the small-radius curve rail profile are calculated, and the calculation results are input into the wheel-rail contact finite element model established in the step of establishing the simulation analysis model to obtain the wheel-rail contact stress and its distribution characteristics corresponding to the rail profiles with different geometric parameters.

[0040] In an optional example, the vehicle dynamics performance index includes: an operating stability index, an operating quality index and an operating smoothness index.

[0041] Furthermore, the operational stability indicators include: derailment coefficient, wheel load reduction rate, wheel axle lateral force and lateral stability.

[0042] Furthermore, the operating quality indicators include: vertical and lateral vibration acceleration of the vehicle body.

[0043] Furthermore, the wheel-rail contact characteristic indicators include: wheel-rail interaction force, wheel-rail contact range, wheel-rail contact stress, rail wear and rail contact fatigue damage.

[0044] Specifically, the calculation results of all the above vehicle dynamics performance indicators and wheel-rail contact characteristic indicators will be input into the subsequent evaluation module to obtain a new small-radius curve rail profile with the best comprehensive performance.

[0045] In an optional embodiment of the present invention, the evaluation of the running performance of a freight vehicle includes: running stability evaluation, running quality evaluation and running stability evaluation, and the method is:

[0046]

[0047] In the formula, w i is the weight of each evaluation index, f i is the normalized result of each evaluation index, and q is the comprehensive evaluation result of freight vehicle operation performance. Different weights are assigned to each index, and a weighted algorithm is used to obtain a comprehensive evaluation of the wheel-rail system performance.

[0048] Specifically, the freight vehicle operation performance evaluation is divided into two stages: operation stability evaluation and operation quality and stability evaluation. If the operation stability does not meet the relevant index limit requirements, the rail profile scheme will no longer be evaluated; if it does, the subsequent operation quality and stability evaluation will continue to complete the vehicle operation performance evaluation. Finally, different rail profile design schemes are ranked according to the evaluation results. The lower the evaluation result score, the higher the ranking. The top 10 rail profile optimization design schemes in the evaluation results enter the rail wear and damage trend evaluation step.

[0049] In an optional example of this embodiment, the operational stability assessment includes: calculating a derailment coefficient, calculating a wheel load reduction rate, calculating a wheel axle lateral force, and assessing lateral stability.

[0050] In an optional example of this implementation, the running stability evaluation includes: calculating a vehicle running stability index and evaluating the stability of the vehicle under different running conditions.

[0051] Specifically, referring to the relevant provisions of the "Road Vehicle Dynamics Performance Evaluation and Test Identification Specification" (GB / T 5599-2019), in the operational stability index, the derailment coefficient limit of freight vehicles is 1.0; the wheel weight reduction rate limit is 0.65; the wheel axle lateral force should not be greater than 15 + static axle weight / 3 (unit: kN); the lateral stability limit is that the peak value of the lateral acceleration of the bogie frame does not reach or exceed 8m / s for 6 consecutive times 2 In the operation stability assessment, if any indicator does not meet the limit value requirement, the operation stability does not meet the requirement.

[0052] In an optional example of this embodiment, the method for evaluating the running quality includes: calculating the vertical vibration acceleration of the vehicle body, calculating the lateral vibration acceleration of the vehicle body, and evaluating the smoothness of the vehicle running.

[0053] In an optional example, the vehicle running stability index is calculated by normalizing the calculation results of various indicators related to running stability, and the method is:

[0054]

[0055] In the formula, f 1 is the normalized result of the vehicle running stability index, and W is the calculated result of the vehicle running stability index.

[0056] Specifically, the calculation results of various indicators related to the running quality and running stability of freight vehicles are normalized in combination with relevant limit values. For example, the "Specifications for the Evaluation and Test Evaluation of the Dynamic Performance of Locomotives and Rolling Stocks" (GB / T 5599-2019) stipulates that the limit value of the qualified level of the running stability index of freight vehicles is 4.25.

[0057] In an optional embodiment of the present invention, the rail wear and damage trend assessment method is:

[0058]

[0059] Where, e is the evaluation result of rail wear and damage trend, w gi is the weight of each evaluation index, g i Calculate results for each evaluation factor.

[0060] In an optional embodiment of the present invention, rail wear and damage trend assessment includes:

[0061] Simulate the wear of rails at different service times;

[0062] Simulate the contact fatigue damage of rails at different service times;

[0063] Analyze the impact of different rail profile designs on wear and damage;

[0064] Assess wear and damage trends.

[0065] Specifically, for each rail profile optimization design scheme, simulation means are used to simulate the wear and contact fatigue damage of the rails after a certain service time through a cyclic iterative method, and the wear and damage trends of the rails corresponding to different profile schemes are analyzed and evaluated.

[0066] First, input the profile optimization design plan. The rail profile optimization design plan and rail material properties are input into the simulation analysis model, the vehicle dynamics performance and wheel-rail contact characteristics after the vehicle passes 50,000 times are calculated, the rail wear and wheel-rail contact fatigue damage are analyzed, and the new rail profile is fitted using rail wear.

[0067] Second, input the new profile after one cycle. The new profile obtained in the above steps is input into the simulation analysis model, and then the vehicle dynamics performance and wheel-rail contact characteristics after the vehicle passes 50,000 times are calculated, the rail wear and wheel-rail contact fatigue damage are analyzed, and the new rail profile is fitted using the rail wear.

[0068] Third, perform cyclic calculations. With 50,000 vehicle passes as a cycle, perform cyclic calculations, update the rail profile, and ultimately obtain the rail wear and contact fatigue damage characteristics after a cumulative 1 million vehicle passes.

[0069] Fourth, evaluate the wear and damage trend of rails. Analyze the changing rules of wear and contact fatigue damage, and draw the curve of rail wear and damage changing with the number of vehicle passes.

[0070] Fifth, ranking of rail profile optimization design schemes. Evaluate the rail wear and damage trends corresponding to different rail profile optimization design schemes, and rank the evaluation results. The smaller the evaluation result, the higher the ranking of the profile optimization design scheme.

[0071] Finally, the top five rail profile optimization design schemes based on the rail wear and damage trend assessment results are input into the rail grinding economic evaluation step.

[0072] In an optional example of this implementation, the method for evaluating rail wear and damage trend is:

[0073] Wear curve envelope area × slope mean + damage curve envelope area × slope mean.

[0074] In the formula, the wear curve envelope area is calculated to reflect the degree of wear; the damage curve envelope area reflects the degree of damage; the slope mean includes the wear and damage rates.

[0075] In an optional embodiment of the present invention, rail grinding economic evaluation includes:

[0076] Calculate the grinding workload required for different rail profile designs;

[0077] Evaluate the labor costs of grinding operations;

[0078] Evaluate the wear and tear costs of grinding tools;

[0079] Comprehensively consider the grinding workload, labor costs and machine wear and tear costs to calculate the economic evaluation results of grinding;

[0080] According to the grinding economic evaluation results, different rail profile design schemes are ranked and the most economical scheme is selected.

[0081] Specifically, by comprehensively considering factors such as grinding workload, labor costs, and grinding machine wear, the weighted average algorithm formula is used to calculate the grinding economy evaluation results for the top 5 rail profiles in terms of rail wear and damage trend evaluation results. Then, the grinding economy of different rail profiles is ranked based on the evaluation results. The lower the evaluation result, the higher the ranking.

[0082] In an optional example of this implementation, the grinding workload required for different rail profile design solutions includes:

[0083] Determine the geometrical difference between the actual rail profile and the designed profile;

[0084] Calculate the required grinding depth and area based on the geometric size differences;

[0085] Estimate the total time for the grinding operation, taking into account the efficiency of the grinding equipment and the operating conditions.

[0086] Specifically, the grinding workload is directly related to the difference in geometric dimensions between the actual profile and the designed profile, and is affected by factors such as the grinding vehicle status and skylight time. The grinding vehicle status and skylight time can be combined to formulate the rail process and determine the grinding workload based on the difference in geometric dimensions between the two rail profiles.

[0087] In an optional example of this implementation, the labor cost of the grinding operation includes:

[0088] Determine the amount of manpower required for the grinding operation;

[0089] Estimate the total man-hours for the grinding operation;

[0090] Calculate labor costs, taking into account worker wages, benefits, and training expenses.

[0091] Specifically, labor costs are mainly used for line inspection, plan formulation, process control, quality acceptance and other aspects related to grinding operations. The remuneration of the construction personnel, technicians and managers involved is affected by the amount of grinding operations.

[0092] In an optional example of this embodiment, the loss cost of the grinding tool includes:

[0093] Determine the service life and maintenance cycle of grinding tools;

[0094] Estimate the depreciation cost of grinding equipment;

[0095] Calculate maintenance and replacement costs for grinding tools.

[0096] Specifically, the loss of grinding equipment mainly includes depreciation and maintenance costs of grinding machinery, grinding wheels and other equipment, which is affected by the amount of grinding work.

[0097] In an optional embodiment of the present invention, a new curved rail profile that has a high ranking in both rail wear and damage trend evaluation and rail grinding economy evaluation is selected as the final optimized design.

[0098] The specific implementation process of the present invention is now described in detail with reference to the embodiments:

[0099] With the goal of achieving a dynamic balance between rail wear and contact fatigue damage on a small-radius curve of a heavy-haul railway, a new profile of a small-radius curve rail is designed by adopting an optimization design method for a heavy-haul railway small-radius curve rail profile proposed in the present invention based on the measured rail profile of the small-radius curve.

[0100] Step 1: Parameter parsing

[0101] (1) Rail status data

[0102] 1) Rail material: U75VH rail, strength grade is 1180MPa.

[0103] 2) Rail profile: According to data analysis, the rail profile is mainly composed of R15-R80-R550-R100-R15. The arcs are tangent to each other, and the lengths of the arcs are 12mm-14mm-23mm-12mm-12mm respectively.

[0104] 3) Rail wear: no side wear, vertical wear is about 0.5mm.

[0105] 4) Rail surface damage: There are fish scale scratches at the gauge angle (R15 arc area).

[0106] (2) Rail service conditions

[0107] 1) Line parameters: The curve has a radius of 800m and a superelevation of 75mm. The curve length is 555m, of which the transition curve is 130m long. The track structure is ballasted track. The measured track geometric irregularities are mainly vertical and lateral displacement irregularities.

[0108] 2) Vehicle parameters: Mainly an open freight car with an axle weight of 25t.

[0109] 3) Wheel profile: Measure the wheel profile.

[0110] Step 2: Establish a rail profile optimization design solution dataset

[0111] Judging from the on-site damage situation, the wear of the curved rails is relatively small, and the main manifestation is contact fatigue damage at the gauge angle (R15 arc).

[0112] The occurrence of contact fatigue damage is mainly due to the relatively high wheel-rail contact stress in this area and the concentrated distribution of contact points. Therefore, the optimization of rail profile will focus on optimizing the R15 arc to improve the wheel-rail matching relationship.

[0113] Based on the principle of controlling a single variable, the radius of the R15 arc is changed, and the radius of the adjacent arcs (R100 and R550 arcs) is adjusted to maintain a smooth transition of the profile. Based on this, a data set of rail profile optimization design solutions with different geometric parameters is established. Table 1 extracts part of the content of the database as an illustration:

[0114] Table 1:

[0115]

[0116] Step 3: Create a simulation analysis model

[0117] (1) Coupled dynamic model of heavy-duty railway freight vehicle-track system

[0118] First, a freight vehicle system dynamics model is established based on the vehicle parameters in step 1. The model consists of an open car body, two bogie frames, four wheel sets, eight axle boxes, a primary suspension system, and a secondary suspension system.

[0119] Second, a track system dynamics model is established based on the line parameters in step 1. The track system consists of rails, sleepers and ballasted trackbed, and the track geometric irregularity excitation comes from step 1.

[0120] Third, a wheel-rail contact model is established to realize the connection between the vehicle system and the track system. The wheel-rail normal contact is considered as Hertz contact, and the wheel-rail tangential contact relationship is calculated using the Kalker contact algorithm, with a reserved wheel-rail profile input interface.

[0121] Finally, a complete freight vehicle-track system coupled dynamics model is established, with reserved input interfaces for wheel profile and rail profile data.

[0122] (2) Wheel-rail contact finite element model

[0123] First, input the measured wheel profile and rail profile in step 1, and use CAD software to create the wheel and rail geometric models.

[0124] Second, the wheel and rail geometric models and material performance parameters are input into finite element software (such as ANSYS, ABAQUS, etc.), the wheel and rail solid models are established, the solid models are meshed, and the mesh is refined at the wheel-rail contact area.

[0125] Third, construct wheel and rail constraints and load conditions in the finite element software, and reserve an interface for wheel-rail load data input.

[0126] Finally, the wheel-rail contact finite element model is established, and an interface for inputting wheel-rail load data is reserved.

[0127] Step 4: Simulation and computational analysis

[0128] First, the rail profiles with different geometric parameters in the rail profile optimization design scheme data set established in step 2 are called and input into the heavy-duty railway vehicle-track coupling dynamics model established in step 3. Through numerical simulation, the vehicle dynamics performance, wheel-rail contact characteristics and wheel-rail interaction parameters corresponding to different optimization design schemes of small-radius curve rail profiles are calculated.

[0129] Secondly, the wheel-rail interaction parameters obtained by dynamic calculation are input into the wheel-rail contact finite element model through the load input interface, and the wheel-rail contact stress and its distribution state corresponding to rail profiles with different geometric parameters are obtained through finite element calculation.

[0130] Finally, the calculation results are divided into four categories and 13 indicators are associated with different profile optimization design schemes. Table 2 lists the corresponding relationships of various indicators taking some rail profiles as examples.

[0131] Table 2:

[0132]

[0133]

[0134] Step 5: Freight vehicle operation performance evaluation

[0135] Input the calculation results of step 4 into this step, first evaluate the vehicle running stability, then evaluate the running quality and stability.

[0136] (1) Operation stability assessment

[0137] According to the relevant provisions of the "Road Vehicle Dynamics Performance Evaluation and Test Identification Specification" (GB / T 5599-2019), the derailment coefficient limit is 1.0; the wheel load reduction rate limit is 0.65; the wheel axle lateral force should not exceed 23kN; the lateral stability limit is that the peak value of the bogie frame lateral acceleration does not reach or exceed 8m / s for 6 consecutive times. 2 If any indicator does not meet the limit value requirement, the operating stability does not meet the requirement.

[0138] Examples of running stability evaluation results are listed in Table 3. The rail profile optimization design scheme that meets the running stability requirements enters the subsequent evaluation stage.

[0139] Table 3:

[0140] plan Derailment coefficient Wheel load reduction rate Axle lateral force Lateral stability Evaluation results R16-R100-R550 √ × √ √ × R17-R100-R550 √ √ √ √ √ R18-R100-R550 √ √ √ √ √ R19-R100-R550 √ √ √ √ √ R16-R60-R400 √ √ × √ × R15-R90-R550 × √ √ √ × R16-R90-R550 √ √ √ √ √ R17-R90-R550 √ √ √ √ √ R18-R90-R550 √ √ √ √ √ R19-R90-R550 √ √ √ √ √ R20-R90-R550 √ √ √ √ √

[0141] (2) Operation quality and stability assessment

[0142] First, the calculation results of various indicators related to the running quality and running stability of freight vehicles are normalized in combination with relevant limit values. For example, the "Specification for Evaluation and Test Evaluation of Locomotive and Rolling Stock Dynamics Performance" (GB / T 5599-2019) stipulates that the limit value of the qualified level of freight vehicle running stability indicators is 4.25. The normalization method of vehicle running stability indicators is as follows:

[0143]

[0144] In the formula, f 1 is the normalized result of the vehicle running stability index, and W is the calculated result of the vehicle running stability index.

[0145] Second, different weights are assigned to each indicator. Considering that open freight cars are usually loaded with coal, excessive acceleration of the car body may cause freight dissipation. Therefore, higher weights are assigned to the lateral acceleration and vertical acceleration of the car body.

[0146] Third, calculate the running quality and stability evaluation results. Use the weighted algorithm to obtain the comprehensive evaluation results of the wheel-rail system performance. Among them, w i is the weight of each evaluation index, f i Normalized results for each evaluation index.

[0147] Finally, different rail profile design schemes are ranked according to the evaluation results. The lower the evaluation score, the higher the ranking.

[0148] Step 5: Examples of freight vehicle running performance evaluation results are listed in Table 4. The top 10 rail profile optimization design solutions in the evaluation results enter the rail wear and damage trend evaluation step.

[0149] Table 4:

[0150] plan Evaluation results Sorting R17-R100-R550 0.49 5 R18-R100-R550 0.45 4 R19-R100-R550 0.53 7 R16-R60-R400 0.32 1 R17-R90-R550 0.35 2 R18-R90-R550 0.40 3 R19-R90-R550 0.51 6 R20-R90-R550 0.55 8

[0151] Step 6: Rail wear and damage trend assessment

[0152] First, input the top 10 optimized design schemes after the freight vehicle running performance evaluation. Input the optimized design scheme of rail profile and rail material performance parameters into the simulation analysis model, calculate the vehicle dynamics performance and wheel-rail contact characteristics after 50,000 vehicle passes, analyze the rail wear and wheel-rail contact fatigue damage, and use the rail wear to fit the new rail profile.

[0153] Second, input the new profile after one cycle. The new profile obtained in the above steps is input into the simulation analysis model, and then the vehicle dynamics performance and wheel-rail contact characteristics after the vehicle passes 50,000 times are calculated, the rail wear and wheel-rail contact fatigue damage are analyzed, and the new rail profile is fitted using the rail wear.

[0154] Third, perform cyclic calculations. With 50,000 vehicle passes as a cycle, perform cyclic calculations, update the rail profile, and ultimately obtain the rail wear and contact fatigue damage characteristics after a cumulative 1 million vehicle passes.

[0155] Fourth, evaluate the wear and damage trend of rails. Analyze the change pattern of wear and contact fatigue damage, draw the curve of rail wear and damage changing with the number of vehicle passes, and calculate the curve envelope area (reflecting the degree of wear and damage) and the slope mean (reflecting the wear and damage rate).

[0156] Fifth, ranking of rail profile optimization design schemes. According to "wear curve envelope area × slope average + damage curve envelope area × slope average", the rail wear and damage trends corresponding to different rail profile optimization design schemes are evaluated, and the evaluation results are ranked. The smaller the evaluation result, the higher the ranking of the profile optimization design scheme.

[0157] Examples of rail wear and damage trend assessment results are given in Table 5.

[0158] Table 5:

[0159]

[0160] Finally, the top five rail profile optimization design schemes based on the rail wear and damage trend assessment results are input into the rail grinding economic evaluation step.

[0161] Step 7: Economic evaluation of rail grinding

[0162] The economic cost of rail grinding is mainly related to factors such as grinding workload, labor costs, and grinding machine wear and tear.

[0163] The amount of grinding work is directly related to the difference in geometric dimensions between the actual profile and the designed profile, and is affected by factors such as the grinding vehicle status and skylight time. The grinding process and the amount of grinding work can be determined based on the difference in geometric dimensions between the two rail profiles, in combination with the grinding vehicle status and skylight time.

[0164] Labor costs are mainly used for line inspection, plan formulation, process control, quality acceptance and other aspects related to grinding operations. The remuneration of the construction personnel, technicians and managers involved is affected by the amount of grinding operations.

[0165] The loss of grinding equipment mainly includes depreciation and maintenance costs of grinding machinery, grinding wheels and other equipment, which is affected by the amount of grinding work.

[0166] Taking into account factors such as grinding workload, labor cost and grinding machine loss, the weighted average algorithm formula is used

[0167] The grinding economic evaluation results are calculated for the top five rail profiles in the rail wear and damage trend evaluation results, among which w gi is the weight of each evaluation index, g i Calculate results for each evaluation factor.

[0168] The grinding economy of different rail profiles is then ranked based on the evaluation results. The lower the evaluation result, the higher the ranking.

[0169] The results of rail grinding economic evaluation are listed in Table 6. The more “★”, the better the grinding economic performance. As can be seen from Table 6, the R16-R60-R400 scheme has the best grinding economic performance.

[0170] Table 6:

[0171]

[0172] Step 8: Propose the optimal new rail profile

[0173] Finally, the R16-R60-R400 scheme, which ranked high in both rail wear and damage trend assessment and rail grinding economic assessment, was selected as the new profile for small-radius curve rails for heavy-duty railways.

[0174] The detailed explanation of the above-mentioned embodiments is only intended to explain the present invention so as to facilitate a better understanding of the present invention. However, these descriptions cannot be interpreted as limitations on the present invention for any reason. In particular, the various features described in different embodiments may also be arbitrarily combined with each other to form other embodiments. Unless there is a clear description to the contrary, these features should be understood to be applicable to any embodiment and are not limited to the described embodiments.

Claims

1. A method for optimizing the profile of a heavy-haul railway small-radius curve rail, characterized in that: Establish a rail profile optimization design scheme data set; establish a simulation analysis model; input the rail profile optimization design scheme data set into the simulation analysis model for simulation and calculation analysis to obtain vehicle dynamics performance and wheel-rail contact characteristics corresponding to multiple new curved rail profiles; comprehensively evaluate the freight vehicle running performance, rail wear and damage trends, and rail grinding economy of each of the new curved rail profiles; and select the optimal new curved rail profile as the final design based on the results of the comprehensive evaluation.

2. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 1, characterized in that: The step of establishing a rail profile optimization design solution data set includes: The relevant data of heavy-haul railway small-radius curve rails are analyzed and summarized into rail status data and rail service condition data; Based on the rail state data and in combination with the rail service condition data, analyzing the key areas where the small radius curve rail profile needs to be optimized; The geometric parameters of the key area are changed to propose rail profile optimization design solutions with different geometric parameters.

3. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 2, characterized in that: The rail status data includes the material, wear, contact fatigue damage and rail profile of the rails in service on site.

4. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 2, characterized in that: The rail service condition data includes line parameters, vehicle parameters and wheel profile.

5. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 4, characterized in that: The different geometric parameters include geometric dimensions of key areas of the rail profile and tangency conditions of arcs in different areas.

6. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 1, characterized in that: The simulation analysis model includes a vehicle-track coupling dynamics model and a wheel-rail contact finite element model.

7. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 6, characterized in that: The vehicle-track coupling dynamics model includes: Vehicle system dynamics model, used to simulate the motion response of the vehicle on the track; Track system dynamics model, used to simulate the dynamic response of the track under vehicle loads.

8. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 7, characterized in that: The vehicle system dynamics model includes dynamic simulation of the vehicle body, bogie, wheelset, axle box, suspension system and wheel profile.

9. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 7, characterized in that: The track system dynamics model includes dynamic simulation of rails, sleepers, ballast and track geometric irregularities.

10. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 6, characterized in that: The wheel-rail contact finite element model is based on Hertz contact theory and Kalker contact algorithm.

11. The method for optimizing the profile of a heavy-haul railway small-radius curve rail according to claim 6, characterized in that: The wheel-rail contact finite element model also includes a reserved wheel profile input interface.

12. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 1, characterized in that: The simulation and computational analysis include: Call rail profiles with different geometric parameters from the rail profile optimization design solution data set; Input the called rail profile into the heavy-duty railway vehicle-track coupling dynamics model to calculate the vehicle dynamics performance and wheel-rail contact characteristic indicators; Input the calculation results into the wheel-rail contact finite element model to calculate the wheel-rail contact stress and its distribution characteristics; Analyze calculation results and evaluate the performance of different rail profile designs.

13. The method for optimizing the profile of a heavy-haul railway small-radius curve rail according to claim 12, characterized in that: The vehicle dynamics performance indicators include: running stability indicators, running quality indicators and running smoothness indicators.

14. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 13, characterized in that: The operational stability indexes include: derailment coefficient, wheel load reduction rate, wheel axle lateral force and lateral stability.

15. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 13, characterized in that: The running quality index includes: vertical and lateral vibration acceleration of the vehicle body.

16. The method for optimizing the profile of a heavy-haul railway small-radius curve rail according to claim 13, characterized in that: The wheel-rail contact characteristic indicators include: wheel-rail interaction force, wheel-rail contact range, wheel-rail contact stress, rail wear and rail contact fatigue damage.

17. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 1, characterized in that: The evaluation of the freight vehicle operation performance includes: operation stability evaluation, operation quality evaluation and operation stability evaluation, and the method is: In the formula, w i is the weight of each evaluation index, f i is the normalized result of various evaluation indicators, and q is the comprehensive evaluation result of the freight vehicle operation performance.

18. The method for optimizing the profile of a heavy-haul railway small-radius curve rail according to claim 17, characterized in that: The running stability evaluation includes: calculating the derailment coefficient, calculating the wheel weight reduction rate, calculating the wheel axle lateral force and evaluating the lateral stability.

19. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 17, characterized in that: The running quality evaluation includes: calculating the vertical vibration acceleration of the vehicle body, calculating the lateral vibration acceleration of the vehicle body and evaluating the stability of vehicle running.

20. The method for optimizing the profile of a heavy-haul railway small-radius curve rail according to claim 17, characterized in that: The running stability evaluation includes: calculating the vehicle running stability index and evaluating the stability of the vehicle under different running conditions.

21. The method for optimizing the profile of a heavy-haul railway small-radius curve rail as claimed in claim 20, characterized in that: The calculation of the vehicle running stability index is to normalize the calculation results of various indicators related to running stability, and the method is: Where f1 is the normalized result of the vehicle running stability index, and W is the calculated result of the vehicle running stability index.

22. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 1, characterized in that: The rail wear and damage trend assessment method is as follows: Where, e is the evaluation result of rail wear and damage trend, w gi is the weight of each evaluation index, g i Calculate results for each evaluation factor.

23. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 1, characterized in that: The rail wear and damage trend assessment includes: Simulate the wear of rails at different service times; Simulate the contact fatigue damage of rails at different service times; Analyze the impact of different rail profile designs on wear and damage; Assess wear and damage trends.

24. The method for optimizing the profile of a heavy-haul railway small-radius curve rail as claimed in claim 23, characterized in that: The method for evaluating the rail wear and damage trend is: Wear curve envelope area × slope mean + damage curve envelope area × slope mean.

25. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail as claimed in claim 1, characterized in that: The rail grinding economic evaluation includes: Calculate the grinding workload required for different rail profile designs; Evaluate the labor costs of grinding operations; Evaluate the wear and tear costs of grinding tools; Comprehensively consider the grinding workload, labor costs and machine wear and tear costs to calculate the economic evaluation results of grinding; According to the grinding economic evaluation results, different rail profile design schemes are ranked and the most economical scheme is selected.

26. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail according to claim 25, characterized in that: The grinding workload required for the different rail profile design solutions includes: Determine the geometrical difference between the actual rail profile and the designed profile; Calculate the required grinding depth and area based on the geometric size differences; Estimate the total time for the grinding operation, taking into account the efficiency of the grinding equipment and the operating conditions.

27. The method for optimizing the profile of a heavy-haul railway small-radius curve rail as claimed in claim 25, characterized in that: The labor cost of the grinding operation includes: Determine the amount of manpower required for the grinding operation; Estimate the total man-hours for the grinding operation; Calculate labor costs, taking into account worker wages, benefits, and training expenses.

28. The method for optimizing the profile of a heavy-haul railway small-radius curve rail as claimed in claim 25, characterized in that: The loss cost of the grinding machine includes: Determine the service life and maintenance cycle of grinding tools; Estimate the depreciation cost of grinding equipment; Calculate maintenance and replacement costs for grinding tools.

29. The method for optimizing the profile design of a heavy-haul railway small-radius curve rail as claimed in claim 1, characterized in that: The new curved rail profile that ranked high in both rail wear and damage trend evaluation and rail grinding economy evaluation was selected as the final optimized design.

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