Design method and system of high-speed railway fastener elastic strip resistant to high-frequency fatigue

Abstract

The application discloses a high-speed railway fastener spring strip design method and system resisting high-frequency fatigue, and comprises the following steps: a parameterized model of the spring strip is established according to basic linear parameters of the fastener spring strip and parameterized equations of each central axis segment; the space geometric linear parameters of the spring strip are taken as design variables, geometric constraint conditions and physical constraint conditions are established, and an optimization objective function fusing a fundamental frequency of the spring strip and static equivalent stress is constructed; and the particle swarm optimization algorithm with constraint conditions is used to perform multi-objective and multi-parameter collaborative optimization of the spring strip resisting high-frequency fatigue according to the established constraint conditions and the optimization objective function, so that optimal design variable results are obtained. Through optimization of the space geometric linear parameters of the spring strip, the application solves the contradiction between high self-frequency and low stress, and provides a scientific reference for prolonging the service life of the high-speed railway fastener system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of high-speed railway fastener spring strip design, and particularly relates to a high-frequency fatigue-resistant high-speed railway fastener spring strip design method and system. BACKGROUND

[0002] The rail fastener system is an important part of the track structure, which is used for fixing the rail, providing elastic support and maintaining the stability and smoothness of the track. The high-frequency fatigue fracture phenomenon occurs in the long-term operation of the high-speed railway fastener spring strip. When the excitation frequency of the external load is close to the natural frequency of the spring strip, resonance will be caused, which will accelerate the fatigue damage of the spring strip. Most of the existing researches are artificial modeling and repeated parameter adjustment, and few of them use a complete optimization algorithm for the optimization design of the spring strip and the overall optimization considering the linear parameters. The optimization efficiency is low, and it is difficult to find the optimal solution. SUMMARY

[0003] The present application provides a high-frequency fatigue-resistant high-speed railway fastener spring strip design method and system to solve the problem of lack of complete spring strip optimization design algorithm and low optimization efficiency in the prior art.

[0004] According to a first aspect, a high-frequency fatigue-resistant high-speed railway fastener spring strip design method is provided in an embodiment, and the method comprises:

[0005] According to the basic linear parameters of the fastener spring strip and the parameterized equation of each central axis segment, a parameterized model of the spring strip is established;

[0006] Taking the spatial geometric linear parameters of the spring strip as design variables, geometric constraint conditions and physical constraint conditions are established, and an optimization objective function fusing the fundamental frequency and the static equivalent stress of the spring strip is constructed;

[0007] According to the established constraint conditions and optimization objective function, a particle swarm optimization algorithm with constraint conditions is used for multi-objective and multi-parameter collaborative optimization of the high-frequency fatigue-resistant spring strip to obtain the optimal design variable result.

[0008] Further, the spatial geometric linear parameters of the spring strip are taken as design variables, which specifically include:

[0009] In order to ensure the reasonable installation of the spring strip, the spatial geometric linear parameters of the spring strip selected as design variables include: the side limb arch height H1, the middle limb arch height H2, the heel end radius R2, the side limb front end radius R3, the center longitudinal distance b1, the front limb straight line b2, and the heel end straight line b3.

[0010] Further, the geometric constraint conditions and the physical constraint conditions are established, which specifically include:

[0011] The geometric constraint conditions ensure the compatibility of the geometric shape of the spring strip and the correct installation on site, and the geometric constraint equation is established, which includes:

[0012]

[0013] In the formula, C i is the i-th constraint equation, R1 is the radius of the middle leg front end;

[0014] The physical constraint conditions include that the elastic strip front leg pressing force, the elastic strip installed base frequency and the initial static maximum equivalent stress of the elastic strip all meet the requirements, and the physical constraint equations include:

[0015]

[0016] In the formula, P is the elastic strip front leg pressing force, f1 is the elastic strip installed base frequency, σ smax is the initial static maximum equivalent stress of the elastic strip.

[0017] Further, an optimization objective function fusing the elastic strip base frequency and the static equivalent stress is constructed, and specifically includes:

[0018]

[0019] In the formula, F is the objective function, , are the elastic strip base frequency and the initial static maximum equivalent stress before optimization respectively.

[0020] Further, a particle swarm optimization algorithm with constraint conditions is used for multi-objective and multi-parameter collaborative optimization of the high-frequency fatigue-resistant elastic strip, and specifically includes:

[0021] For the constraint equations C3, C4 and C5, the constraints are realized by controlling the upper and lower limits of the design variables;

[0022] The remaining constraint equations C1, C2, C6, C7 and C8 use the penalty function method to realize the introduction of the constraint conditions, and the specific formula is as follows:

[0023] The penalty term e ij of the i-th constraint of the j-th particle is:

[0024]

[0025] In the formula, C ij is the i-th constraint of the j-th particle;

[0026] The constraint penalty term is normalized:

[0027]

[0028] In the formula, N s is the population size;

[0029] The weight of the i-th constraint penalty term is calculated:

[0030]

[0031] wherein L i is the weight of the ith constraint penalty term, N c is the number of constraint equations, and N c = 5.

[0032] Further, the high-frequency fatigue-resistant elastic strip is optimized and designed by using the particle swarm optimization algorithm with constraints for multi-objective and multi-parameter collaborative optimization, specifically including:

[0033] Initializing the population and particle velocity and position, wherein the population refers to the spatial geometric linear parameter vector of the elastic strip as the design variable, and the particle refers to each variable parameter in the population;

[0034] Updating the velocity and position of each particle:

[0035]

[0036] wherein k is the current iteration number, x k , v k are the particle position and velocity of the kth iteration, ω s is an inertia factor; c1 and c2 are constants, representing the influence degree of the particle swarm on the individual particle; r1 and r2 are random numbers between 0 and 1, p k is the optimal position of the current population, p g is the global optimal position experienced by all particles in the population;

[0037] Calculating the fitness value function, and the fitness value function formula is:

[0038]

[0039] wherein, is the fitness value function;

[0040] Comparing the size of the fitness value function before and after iteration, and guiding the group to move towards the optimal solution, and performing the next iteration until the loop program terminates.

[0041] Further, the high-frequency fatigue-resistant elastic strip is optimized and designed by using the particle swarm optimization algorithm with constraints for multi-objective and multi-parameter collaborative optimization, specifically including:

[0042] Joint simulation is performed by using MATLAB and ANSYS software, and the specific process is as follows:

[0043] A particle swarm optimization algorithm program is written by using MATLAB software;

[0044] Based on MATLAB and ANSYS software joint simulation, MATLAB initializes the spatial geometric linear parameters of the spring strip and starts ANSYS;

[0045] ANSYS reads the spatial geometric linear parameters of the spring strip, performs prestressed modal analysis, calculates the static equivalent stress and fundamental frequency of the spring strip, and outputs a file;

[0046] MATLAB extracts the calculated fundamental frequency and static equivalent stress value of the spring strip, and calculates the fitness value function.

[0047] Further, ANSYS reads the spatial geometric linear parameters of the spring strip, performs prestressed modal analysis, calculates the static equivalent stress and fundamental frequency of the spring strip, and outputs, specifically including:

[0048] A three-direction Hertz linear simplified spring element is used to simulate the contact between the spring strip and the adjacent components, and the contact site constraint stiffness of the spring strip is determined by using a particle swarm optimization algorithm and Hertz contact theory;

[0049] Based on the obtained spring strip contact site constraint stiffness, prestressed modal analysis is performed, and the static equivalent stress and fundamental frequency of the spring strip are calculated.

[0050] Further, the method further comprises:

[0051] Based on the optimal design variable result, a high-frequency fatigue-resistant spring strip dynamics model is established to verify the life extension effect.

[0052] According to the second aspect, an embodiment provides a high-speed railway fastener spring strip design system resistant to high-frequency fatigue, the system comprising:

[0053] A parameterized model construction module is configured to establish a parameterized model of the spring strip according to basic linear parameters of the fastener spring strip and parameterized equations of each central axis segment;

[0054] A constraint and objective function establishment module is configured to take the spatial geometric linear parameters of the spring strip as design variables, establish geometric constraint conditions and physical constraint conditions, and construct an optimization objective function that integrates the fundamental frequency and the static equivalent stress of the spring strip;

[0055] An optimization solving module is configured to perform multi-objective and multi-parameter collaborative optimization of the high-frequency fatigue-resistant spring strip by using a particle swarm optimization algorithm with constraint conditions according to the established constraint conditions and optimization objective function, and obtain an optimal design variable result.

[0056] This invention provides a design method and system for high-speed railway fastener elastic bars resistant to high-frequency fatigue. Based on a particle swarm optimization algorithm with constraints, the invention employs MATLAB and ANSYS software for joint simulation to perform multi-objective and multi-parameter collaborative optimization of the high-frequency fatigue-resistant elastic bar design. Because this invention uses an optimization algorithm to consider the overall optimization of the spatial geometric parameters of the elastic bar, it resolves the contradiction between high natural frequency and low stress, providing a scientific reference for extending the service life of high-speed railway fastener systems. Attached Figure Description

[0057] Figure 1 A flowchart illustrating a design method for high-speed railway fastener elastic bars resistant to high-frequency fatigue, as provided in one embodiment of the present invention;

[0058] Figure 2 A planar development diagram of the elastic bar geometry in a high-speed railway fastener elastic bar design method for resisting high-frequency fatigue, provided in an embodiment of the present invention;

[0059] Figure 3 A side view of the elastic bar geometry in a high-speed railway fastener elastic bar design method for resisting high-frequency fatigue, provided in an embodiment of the present invention;

[0060] Figure 4 The convergence curve of the fitness function value in the particle swarm optimization algorithm in a high-speed railway fastener elastic bar design method for resisting high-frequency fatigue provided in an embodiment of the present invention;

[0061] Figure 5 An iterative curve of design variables in a high-speed railway fastener elastic bar design method for resisting high-frequency fatigue, provided as an embodiment of the present invention;

[0062] Figure 6 A comparison diagram of the spatial curves before and after optimization of the elastic bar in a high-speed railway fastener elastic bar design method for resisting high-frequency fatigue provided in an embodiment of the present invention;

[0063] Figure 7 The first two vibration modes of the optimized elastic bar in a high-speed railway fastener elastic bar design method for resisting high-frequency fatigue provided in an embodiment of the present invention;

[0064] Figure 8 A comparison diagram of the time history response of the vertical acceleration at the highest point of the elastic bar side limb in a high-speed railway fastener elastic bar design method for resisting high-frequency fatigue, provided as an embodiment of the present invention;

[0065] Figure 9 A comparison diagram of equivalent stress time history curves at dangerous points in a high-speed railway fastener elastic bar design method for resisting high-frequency fatigue, provided as an embodiment of the present invention. Detailed Implementation

[0066] The application will be described in further detail below with specific reference to the drawings. Like elements in different embodiments are denoted by like reference numerals. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the application. However, it will be apparent to one skilled in the art that the application can be practiced without these specific details. In other instances, well-known methods have not been described in detail in order not to unnecessarily obscure the application. Embodiments of the application will also be described as they can be implemented in the context of particular apparatus and methods. However, working examples are included to demonstrate particular embodiments of the application and to enable a person of ordinary skill in the art to make and use the application. Changes can be made to these examples without departing from the spirit of the application. Those skilled in the art will further appreciate that the application can be practiced by other than the methods, apparatus and materials specifically used in the examples, and that the present application carries out its principles in these and other examples.

[0067] In addition, features, operations, or steps described in the specification can be combined in any suitable manner without departing from the scope of the present application. Similarly, steps in the methods described do not have to be performed in the precise order described, unless otherwise specified. Thus, another embodiment of the application can be one in which steps are performed in an order other than those described.

[0068] A design method of a high-speed railway fastener spring strip resistant to high-frequency fatigue is provided in the first embodiment of the application, which will be described in detail below in combination with Figure 1 .

[0069] As shown in Figure 1 , in step S100, a parameterized model of the spring strip is established according to the basic linear parameters of the spring strip and the parameterized equations of the central axis segments.

[0070] In this embodiment, the basic linear parameters of the spring strip include 11 parameters, i.e., arch span L, half arch span L1, side limb arch height H1, middle limb arch height H2, spring range H, front end radius R1 of the middle limb, heel end radius R2, front end radius R3 of the side limb, center longitudinal distance b1, straight line b2 of the front limb, and straight line b3 of the heel end, as shown in Figure 2 , Figure 3 the planar development diagram and the side view of the geometric structure of the middle spring strip.

[0071] In this embodiment, in order to obtain the constraint stiffness of the spring strip contact position, so as to smoothly carry out the prestressed modal analysis of the spring strip in the subsequent optimization process, the three-dimensional Hertz linear simplified spring unit is used to simulate the contact between the spring strip and the adjacent components, and the constraint stiffness of the spring strip contact position is determined by using the particle swarm optimization algorithm and the Hertz contact theory. Specifically, the constraint stiffness of the spring strip contact position, the vertical constraint stiffness of the middle limb of the spring strip and the flat washer, and the vertical constraint stiffness of the heel end of the spring strip and the gauge baffle are calculated and obtained by using the Hertz contact theory. The stiffness of the two before optimization is 280 kN / mm and 70.17 kN / mm respectively, and the stiffness of the two in the optimization process changes with the size change of the spring strip contact position; the constraint stiffness of the front limb (longitudinal, transverse and vertical), the front end of the middle limb (longitudinal, transverse and vertical), and the heel end (longitudinal, transverse and vertical) of the spring strip is determined by the particle swarm optimization as (0.81, 1.47, 1.37, 27.62, 54.07, 89.17, 13.23, 81.43) kN / mm.

[0072] The specific process of obtaining the constraint stiffness of the front limb (longitudinal, transverse and vertical), the front end of the middle limb (longitudinal, transverse and vertical), and the heel end (longitudinal, transverse and vertical) of the spring strip by the particle swarm optimization algorithm is as follows: taking the natural frequency calculated by the currently commonly used spring strip solid element finite element model as the benchmark, a fitness function describing the frequency difference between the spring strip solid model and the beam model is constructed, and the linear spring constraint stiffness of the spring strip beam element model is taken as the design optimization object. The optimal value of the beam model constraint stiffness is obtained based on the particle swarm optimization algorithm.

[0073] In this embodiment, the constraint stiffness of the spring strip is determined by the above method, and the purpose is to be used for subsequent prestressed modal analysis of the spring strip, that is, to be used for prestressed modal analysis by ANSYS in the subsequent MATLAB and ANSYS software joint simulation step, and to calculate the static equivalent stress and fundamental frequency of the spring strip.

[0074] As shown in Figure 1 In step S200, the spatial geometric linear parameters of the spring strip are taken as design variables, and geometric constraint conditions and physical constraint conditions are established to construct an optimization objective function that integrates the fundamental frequency and the static equivalent stress of the spring strip.

[0075] The above steps specifically include:

[0076] In this embodiment, in order to ensure the reasonable installation of the spring strip, the design variables are selected as the 7 geometric linear parameters of the spring strip, which are in turn: the side limb arch height H1, the middle limb arch height H2, the heel end radius R2, the side limb front end radius R3, the longitudinal distance of the center b1, the front limb straight line b2, and the heel end straight line b3.

[0077] The constraint conditions include geometric constraint conditions and physical constraint conditions, and the geometric constraint conditions ensure the compatibility of the geometric shape of the spring strip and the correct installation on site, that is, 5 constraint equations:

[0078]

[0079] In the formula, C i is the ith constraint;

[0080] The physical constraint conditions are that the pressing force meets the standard, the spring strip base frequency is greater than 800 Hz, and the initial equivalent stress is low, i.e. three constraint equations:

[0081]

[0082] In the formula, P is the front limb pressing force of the spring strip, f1 is the base frequency after the spring strip is installed, σ smax is the initial static maximum equivalent stress of the spring strip.

[0083] The optimization objective function of the fusion of the spring strip base frequency and the static maximum equivalent stress is:

[0084]

[0085] In the formula, F is the target function, , respectively, are the base frequency of the spring strip before optimization and the initial static maximum equivalent stress.

[0086] As Figure 1 shown, in step S300, according to the established constraint conditions and optimization objective function, the multi-objective multi-parameter collaborative optimization of the high-frequency fatigue-resistant spring strip is designed by using the particle swarm optimization algorithm with constraint conditions, and the optimal design variable result is obtained.

[0087] The above steps specifically include:

[0088] In the embodiment, the particle swarm optimization algorithm with constraint conditions realizes the constraint through controlling the upper and lower limits of the design variables for the constraint equations C3, C4 and C5; the introduction of the constraint conditions is realized by using the penalty function method for the remaining five constraint equations, and the specific implementation is as follows:

[0089] The penalty term e ij of the ith constraint of the jth particle is:

[0090]

[0091] In the formula, C ij is the ith constraint of the jth particle;

[0092] The constraint penalty term is normalized:

[0093]

[0094] In the formula, N s is the population size;

[0095] The weight of the ith constraint penalty term is calculated:

[0096]

[0097] wherein L i is the weight of the ith constraint penalty term, N c is the number of constraint equations, and N c = 5.

[0098] In this embodiment, the specific steps of the particle swarm optimization algorithm with constraints are as follows:

[0099] 1) initializing the population and the particle velocity and position, wherein the population refers to the spatial geometric linear parameter vector of the strip as the design variable, and the particle refers to each variable parameter in the population;

[0100] 2) updating the velocity and position of each particle:

[0101]

[0102] wherein k is the current iteration number, x k and v k are the particle position and velocity at the kth iteration, ω s is the inertia factor, which gradually decreases from 0.9 to 0.4 with iteration, c1 and c2 are constants, and c1 = c2 = 2, indicating the influence degree of the particle swarm on the individual particle, r1 and r2 are random numbers between 0 and 1, p k is the optimal position of the current population, and p g is the global optimal position experienced by all particles in the population;

[0103] 3) using MATLAB and ANSYS software for joint simulation to calculate the fitness value function, and the calculation formula is as follows:

[0104]

[0105] wherein f(x) is the fitness value function;

[0106] 4) comparing the size of the fitness value function before and after iteration, guiding the population to move towards the optimal solution, and performing the next iteration until the loop program terminates.

[0107] In this embodiment, the process of using MATLAB and ANSYS software for joint simulation is as follows:

[0108] 1) using MATLAB software to write the particle swarm optimization algorithm program;

[0109] 2) based on MATLAB and ANSYS software joint simulation, MATLAB initializes the spatial geometric linear parameters of the strip, and starts ANSYS;​

[0110] 3) ANSYS reads the spatial geometric linear parameters of the elastic strip, performs prestressed modal analysis, and calculates the static equivalent stress and the fundamental frequency of the elastic strip and outputs a file;

[0111] 4) MATLAB extracts the fundamental frequency and equivalent stress, and calculates the fitness value function.

[0112] Figure 4 The convergence curve diagram of the fitness function value in the particle swarm optimization algorithm, Figure 5 The design variable iteration curve diagram of the present application can be seen, and after 200 iterations, the fitness function value and the seven design variables tend to be in a convergent state.

[0113] Figure 6 The spatial curve comparison diagram of the elastic strip before and after optimization, and table 1 is the comparison of design variables of the elastic strip before and after optimization, it can be seen that H1, b1 and b3 have a large change range, and it is speculated that the elastic strip natural frequency is a key influencing geometric parameter.

[0114] Table 1. Comparison of design variables of the elastic strip before and after optimization (unit: mm)

[0115]

[0116] Figure 7 The first two order mode diagrams of the elastic strip after optimization of the present application, the first order mode is characterized by the opposite sides of the elastic strip being flipped and vibrated with the elastic strip forelimb and the heel end as the fulcrum, and the second order mode is characterized by the opposite sides of the elastic strip being flipped and vibrated symmetrically.

[0117] Table 2 is a comparison of key parameters of the elastic strip before and after optimization, after optimization, the first two order natural frequencies of the elastic strip are greatly improved, the first order natural frequency is greater than 800 Hz, the elastic strip pressure meets the standard, the initial static maximum stress and the mass are reduced.

[0118] Table 2. Comparison of key parameters of the elastic strip before and after optimization

[0119]

[0120] Further, in the embodiment, the method further includes: step S400, based on the optimal design variable result, a high-frequency fatigue-resistant elastic strip dynamics model is established, and the service life extension effect is verified.

[0121] Considering that the high-speed train runs at a speed of 300 km / h, the wheel-rail excitation is the superposition of the measured wheel polygonal wear sample and the short-wave random irregularity sample, and the elastic strip dynamics response is calculated. Figure 8 The vertical acceleration time history response comparison diagram of the elastic strip side limbs is shown, and the acceleration peak value is reduced from 331 g to 233 g.

[0122] Figure 9For the stress-time curve contrast chart of the dangerous point of the spring strip, the stress amplitude decreases from 114 MPa to 103 MPa, the fatigue life increases from 3.1*10 6 seconds to 7.2*10 8 seconds, and is increased by about 232 times, which verifies the life prolonging effect of the optimized spring strip.

[0123] Corresponding to the above-mentioned anti-high-frequency fatigue high-speed railway fastener spring strip design method, the embodiment of the application further discloses an anti-high-frequency fatigue high-speed railway fastener spring strip design system, which specifically comprises:

[0124] A parameterized model construction module is configured to establish a parameterized model of the spring strip according to basic linear parameters of the fastener spring strip and parameterized equations of each central axis segment.

[0125] A constraint and objective function establishment module is configured to take the spatial geometric linear parameters of the spring strip as design variables, establish geometric constraint conditions and physical constraint conditions, and construct an optimization objective function that fuses the fundamental frequency and static equivalent stress of the spring strip.

[0126] An optimization solving module is configured to perform multi-objective multi-parameter collaborative optimization of the anti-high-frequency fatigue spring strip by using a particle swarm optimization algorithm with constraint conditions, according to the established constraint conditions and optimization objective function, and obtain the optimal design variable result.

[0127] It should be noted that the detailed description of the anti-high-frequency fatigue high-speed railway fastener spring strip design system provided by the embodiment of the application can refer to the related description of the anti-high-frequency fatigue high-speed railway fastener spring strip design method provided by the embodiment of the application, which will not be repeated here.

[0128] In addition, the embodiment of the application further provides an electronic device, which comprises a processor and a memory; the memory is used to store one or more program instructions; and the processor is used to run the one or more program instructions to execute the steps of the anti-high-frequency fatigue high-speed railway fastener spring strip design method according to any one of the above.

[0129] It should be noted that the detailed description of the electronic device provided by the embodiment of the application can refer to the related description of the anti-high-frequency fatigue high-speed railway fastener spring strip design method provided by the embodiment of the application, which will not be repeated here.

[0130] In addition, the embodiment of the application further provides a computer readable storage medium, which stores a computer program; and the computer program is executed by a processor to implement the steps of the anti-high-frequency fatigue high-speed railway fastener spring strip design method according to any one of the above.

[0131] It should be noted that the detailed description of the computer readable storage medium provided by the embodiment of the present application can refer to the related description of the design method of the high-frequency fatigue-resistant high-speed railway fastener elastic strip provided by the embodiment of the present application, which will not be repeated here.

[0132] Those skilled in the art can understand that all or part of the functions of the various methods in the above embodiments can be realized by hardware or by a computer program. When all or part of the functions in the above embodiments are realized by a computer program, the program can be stored in a computer readable storage medium, which can include read-only memory, random access memory, magnetic disk, optical disk, hard disk, etc. The above functions are realized by executing the program by a computer. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, the above all or part of the functions can be realized. In addition, when all or part of the functions in the above embodiments are realized by a computer program, the program can also be stored in a server, another computer, a storage medium such as a disk, an optical disk, a flash disk or a mobile hard disk, and is downloaded or copied into the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be realized.

[0133] The above application of specific examples is used to illustrate the present application, which is only used to help understand the present application, and does not limit the present application. According to the idea of the present application, those skilled in the art can make several simple deductions, deformations or substitutions.

Claims

1. A design method of high-speed railway fastener elastic strip against high-frequency fatigue, characterized in that, The method comprises: According to the basic linear parameters of the fastener elastic strip and the parametric equation of each central axis segment, a parametric model of the elastic strip is established; Taking the spatial geometric linear parameters of the elastic strip as design variables, geometric constraint conditions and physical constraint conditions are established, and an optimization objective function integrating the fundamental frequency and static equivalent stress of the elastic strip is constructed; According to the established constraint conditions and optimization objective function, a particle swarm optimization algorithm with constraint conditions is used for multi-objective and multi-parameter collaborative optimization of the high-frequency fatigue-resistant elastic strip to obtain optimal design variable results; Taking the spatial geometric linear parameters of the elastic strip as design variables, the specific steps include: In order to ensure the reasonable installation of the elastic strip, the spatial geometric linear parameters of the elastic strip selected as design variables include: the arch height H1 of the side limb, the arch height H2 of the middle limb, the radius R2 of the heel end, the radius R3 of the front end of the side limb, the longitudinal distance b1 of the center, the straight line b2 of the front limb, and the straight line b3 of the heel end; The geometric constraint conditions and physical constraint conditions are established, which include: The geometric constraint conditions ensure the compatibility of the geometric shape of the elastic strip and the correct installation on site, and the geometric constraint equations include: In the formula, C i is the ith constraint equation, R1 is the radius of the middle leg front end; The physical constraint conditions include that the front limb pressing force of the elastic strip, the fundamental frequency after installation of the elastic strip, and the initial static maximum equivalent stress of the elastic strip all meet the requirements, and the physical constraint equations include: In the formula, P is the front limb pressing force of the elastic strip, f1 is the base frequency after the elastic strip is installed, σ smax is the initial static maximum equivalent stress of the elastic strip.

2. The design method of a high-speed railway fastener elastic strip with high-frequency fatigue resistance according to claim 1, characterized in that, The optimization objective function integrating the fundamental frequency and static equivalent stress of the elastic strip is constructed, which includes: In the formula, F is the objective function. , These represent the fundamental frequency of the spring bar before optimization and the initial static maximum equivalent stress, respectively.

3. The method of designing a high-speed railway fastener elastic strip resistant to high-frequency fatigue according to claim 2, characterized in that, The particle swarm optimization algorithm with constraint conditions is used for multi-objective and multi-parameter collaborative optimization of the high-frequency fatigue-resistant elastic strip, which includes: The constraint equations C3, C4 and C5 are realized by controlling the upper and lower limits of the design variables; The remaining constraint equations C1, C2, C6, C7 and C8 are realized by introducing the constraint conditions using the penalty function method, and the specific formula is as follows: Penalty term for the jth particle for the ith constraint e ij is: where C ij is the ith constraint for the jth particle; The constraint penalty term is normalized: In the formula, N s is the population size; The weight of the i-th constraint penalty term is calculated: In the formula, L i is the weight of the ith constraint penalty term, N c is the number of constraint equations, and N c = 5.

4. The design method of a high-speed railway fastener elastic strip with high-frequency fatigue resistance according to claim 3, characterized in that, The particle swarm optimization algorithm with constraint conditions is used for multi-objective and multi-parameter collaborative optimization of the high-frequency fatigue-resistant elastic strip, which includes: The population and particle velocity and position are initialized, where the population refers to the spatial geometric linear parameter vector of the elastic strip as the design variable, and the particle refers to each variable parameter in the population; The velocity and position of each particle are updated: where k is the current iteration number, x k , v k are the particle position and velocity at the kth iteration, x k+1 , v k+1 are the particle position and velocity at the (k+1)th iteration, ω s is the inertia factor; c1, c2 are constants, representing the degree of influence of the particle group on the individual particle; r1, r2 are random numbers between 0 and 1, p k is the optimal position of the current population, p g is the global optimal position experienced by all particles in the population; The fitness value function is calculated, and the formula of the fitness value function is: In the formula, fitness value function; The size of the fitness value function before and after iteration is compared, and the group is guided to move towards the optimal solution, and the next iteration is performed until the loop program terminates.

5. The method of designing a high-speed railway fastener elastic strip resistant to high-frequency fatigue according to claim 4, characterized in that, The particle swarm optimization algorithm with constraint conditions is used for multi-objective and multi-parameter collaborative optimization of the high-frequency fatigue-resistant elastic strip, which includes: MATLAB and ANSYS software are used for joint simulation, and the specific process is as follows: The particle swarm optimization algorithm program is written using MATLAB software; Based on the joint simulation of MATLAB and ANSYS software, the spatial geometric linear parameters of the elastic strip are initialized by MATLAB, and ANSYS is started; ANSYS reads the spatial geometric linear parameters of the elastic strip, performs prestressed modal analysis, calculates the static equivalent stress and fundamental frequency of the elastic strip, and outputs them; The calculated fundamental frequency and static equivalent stress values of the elastic strip are extracted by MATLAB, and the fitness value function is calculated.

6. The method of designing a high-speed railway fastener elastic strip resistant to high-frequency fatigue according to claim 5, characterized in that, ANSYS reads the spatial geometric linear parameters of the elastic strip, performs prestressed modal analysis, and calculates and outputs the static equivalent stress and fundamental frequency of the elastic strip, specifically including: A three-direction Hertz linear simplified spring unit is used to simulate the contact between the elastic strip and adjacent components, and the constraint stiffness of the elastic strip contact position is determined by using a particle swarm optimization algorithm and Hertz contact theory; Based on the obtained constraint stiffness of the elastic strip contact position, prestressed modal analysis is performed to calculate the static equivalent stress and fundamental frequency of the elastic strip.

7. The method of designing a high-speed railway fastener elastic strip with high-frequency fatigue resistance according to claim 1, characterized in that, The method further includes: Based on the optimal design variable result, a high-frequency fatigue-resistant elastic strip dynamics model is established to verify the service life extension effect.

8. A system for designing a high-speed railway fastener clip resistant to high-frequency fatigue, characterized in that, The system includes: A parameterized model construction module is configured to establish a parameterized model of the elastic strip according to basic linear parameters of the elastic strip and parameterized equations of each central axis segment; A constraint and objective function establishment module is configured to take the spatial geometric linear parameters of the elastic strip as design variables, establish geometric constraint conditions and physical constraint conditions, and construct an optimization objective function that integrates the fundamental frequency and the static equivalent stress of the elastic strip; An optimization solving module is configured to perform multi-objective and multi-parameter collaborative optimization of the high-frequency fatigue-resistant elastic strip by using a particle swarm optimization algorithm with constraint conditions according to the established constraint conditions and optimization objective function, and obtain an optimal design variable result; The spatial geometric linear parameters of the elastic strip are taken as design variables, specifically including: In order to ensure the reasonable installation of the elastic strip, the spatial geometric linear parameters of the elastic strip selected and taken as design variables include: the side limb arch height H1, the middle limb arch height H2, the heel end radius R2, the side limb front end radius R3, the center longitudinal distance b1, the front limb straight line b2, and the heel end straight line b3; The geometric constraint conditions and the physical constraint conditions are established, specifically including: The geometric constraint conditions ensure the compatibility of the geometric shape of the elastic strip and the correct installation on site, and the geometric constraint equation includes: In the formula, C i is the ith constraint equation, Ri is the radius of the middle leg at the front end; The physical constraint conditions include that the elastic strip front limb clamping pressure, the elastic strip installation fundamental frequency, and the initial static maximum equivalent stress of the elastic strip all meet the requirements, and the physical constraint equation includes: In the formula, P is the front limb pressing force of the elastic strip, f1 is the base frequency after the elastic strip is installed, σ smax is the initial static maximum equivalent stress of the elastic strip.

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