High-frequency fatigue resistant high-speed railway fastener elastic strip design method and system

Abstract

The invention discloses a high-frequency fatigue resistant high-speed railway fastener elastic strip design method and system, and the method comprises the steps: building a parameterized model of an elastic strip according to the basic linear parameters of the fastener elastic strip and the parameterized equation of each central axis segment; taking the spatial geometric linear parameters of the elastic strip as design variables, establishing geometric constraint conditions and physical constraint conditions, and constructing an optimized objective function fusing the fundamental frequency and the static equivalent stress of the elastic strip; and according to the established constraint condition and the optimization objective function, performing multi-objective multi-parameter collaborative optimization high-frequency fatigue resistant elastic strip optimization design by using a particle swarm optimization algorithm with the constraint condition to obtain an optimal design variable result. According to the method, the contradiction between high self-frequency and low stress is solved by optimizing the spatial geometric linear parameters of the elastic strip, and scientific reference is provided for prolonging the service life of a high-speed railway fastener system.

Description

Technical Field

[0001] The present invention relates to the technical field of high-speed railway fastener spring bar design, and in particular to a high-frequency fatigue resistant high-speed railway fastener spring bar design method and system. Background Art

[0002] Rail fastening systems are a crucial component of track structures, securing rails, providing elastic support, and maintaining track stability and smoothness. High-speed rail fastener clips have been experiencing high-frequency fatigue fractures during long-term operation. When the external load excitation frequency approaches the clip's natural frequency, resonance is induced, accelerating fatigue damage. Existing research has mostly relied on manual modeling and repeated parameter adjustments. Few studies have utilized comprehensive optimization algorithms for clip design and overall optimization that considers linear parameters. This results in low optimization efficiency and difficulty in finding an optimal solution. Summary of the Invention

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

[0004] According to the first aspect, an embodiment provides a method for designing a high-speed railway fastener spring clip that is resistant to high-frequency fatigue, the method comprising: According to the basic linear parameters of the fastener spring clip and the parametric equations of each central axis segment, a parametric model of the spring clip is established; Taking the spatial geometric linear parameters of the spring clip as design variables, and establishing geometric and physical constraints, an optimization objective function integrating the spring clip fundamental frequency and static equivalent stress is constructed. According to the established constraints and optimization objective function, the particle swarm optimization algorithm with constraints is used to perform multi-objective and multi-parameter collaborative optimization of the anti-high-frequency fatigue spring bar to obtain the optimal design variable results.

[0005] Furthermore, the spatial geometric linear parameters of the spring strip are used as design variables, specifically including: In order to ensure the reasonable installation of the spring clip, the spatial geometric linear parameters of the spring clip selected and used as design variables include: side limb arch height H1, middle limb arch height H2, heel end radius R2, side limb front end radius R3, longitudinal distance from the center of the circle b1, forelimb straight line b2, and heel end straight line b3.

[0006] Furthermore, geometric and physical constraints are established, including: Geometric constraints ensure the geometric compatibility of the spring clips and their correct installation on site. The geometric constraint equations include:

[0007] Where C iis the i-th constraint equation, R1 is the radius of the front end of the middle limb; The physical constraints include the buckle pressure of the front limb of the spring clip, the fundamental frequency of the spring clip after installation, and the initial static maximum equivalent stress of the spring clip, all of which meet the requirements. The physical constraint equations are established as follows:

[0008] Where P is the buckle pressure of the front limb of the spring clip, f1 is the fundamental frequency after the spring clip is installed, σ smax is the initial static maximum equivalent stress of the spring clip.

[0009] Furthermore, an optimization objective function integrating the fundamental frequency of the spring clip and the static equivalent stress is constructed, specifically including:

[0010] Where F is the objective function, 、 They are the fundamental frequency of the spring bar before optimization and the initial static maximum equivalent stress respectively.

[0011] Furthermore, the particle swarm optimization algorithm with constraints is used to perform multi-objective and multi-parameter collaborative optimization for the optimization design of high-frequency fatigue-resistant spring bars, specifically including: Constraint equations C3, C4 and C5 are constrained by controlling the upper and lower limits of the design variables; The remaining constraint equations C1, C2, C6, C7, and C8 use the penalty function method to introduce constraint conditions. The specific formulas are as follows: The penalty term e of the jth particle's i-th constraint ij for:

[0012] Where C ij is the i-th constraint of the j-th particle; Normalize the constraint penalty term:

[0013] Where N s is the population size; Calculate the weight of the i-th constraint penalty term:

[0014] Where, L i is the weight of the i-th constraint penalty term, N c is the number of constraint equations, take N c =5.

[0015] Furthermore, the particle swarm optimization algorithm with constraints is used to perform multi-objective and multi-parameter collaborative optimization for the optimization design of high-frequency fatigue-resistant spring bars, specifically including: Initialize the population and particle velocity and position, where the population refers to the spring space geometric linear parameter vector as the design variable, and the particle refers to each variable parameter in the population; Update the velocity and position of each particle:

[0016] Where k is the current iteration number, x k 、v k are the particle position and velocity of the kth iteration, ω s is the inertia factor; c1 and c2 are constants, indicating the degree of influence of the particle group on a single particle; r1 and r2 are random numbers between [0,1], p k is the optimal position of the current population, p g is the global optimal position experienced by all particles in the population; Calculate the fitness value function. The fitness value function formula is:

[0017] Where, is the fitness value function; Compare the size of the fitness value function before and after the iteration, and guide the group to move towards the optimal solution, and proceed to the next iteration until the loop program terminates.

[0018] Furthermore, the particle swarm optimization algorithm with constraints is used to perform multi-objective and multi-parameter collaborative optimization for the optimization design of high-frequency fatigue-resistant spring bars, specifically including: MATLAB and ANSYS software are used for joint simulation. The specific process is as follows: Use MATLAB software to write particle swarm optimization algorithm program; Based on the joint simulation of MATLAB and ANSYS software, MATLAB initializes the spatial geometric line parameters of the spring strip and starts ANSYS; ANSYS reads the spatial geometric linear parameters of the spring clip, performs prestressed modal analysis, calculates the static equivalent stress and fundamental frequency of the spring clip, and outputs the file; MATLAB extracts the calculated fundamental frequency of the spring bar and the static equivalent stress value and calculates the fitness value function.

[0019] Furthermore, ANSYS reads the spatial geometric linear parameters of the spring clip, performs prestressed modal analysis, calculates the static equivalent stress and fundamental frequency of the spring clip, and outputs them, including: The contact between the spring bar and adjacent components is simulated using a three-dimensional Hertz linear simplified spring element. The constraint stiffness of the spring bar contact part is determined using the particle swarm optimization algorithm and Hertz contact theory. Based on the obtained constraint stiffness of the spring bar contact part, prestressed modal analysis is performed to calculate the static equivalent stress and fundamental frequency of the spring bar.

[0020] Furthermore, the method further comprises: Based on the results of the optimal design variables, a dynamic model of high-frequency fatigue-resistant elastic bars was established to verify the life extension effect.

[0021] According to a second aspect, an embodiment provides a high-frequency fatigue resistant high-speed railway fastener spring clip design system, the system comprising: A parametric model building module is used to build a parametric model of the spring clip based on the basic linear parameters of the fastener spring clip and the parametric equations of each central axis segment; The constraint and objective function establishment module is used to use the spring clip's spatial geometric linear parameters as design variables, establish geometric constraints and physical constraints, and construct an optimization objective function that integrates the spring clip's fundamental frequency and static equivalent stress; The optimization solution module is used to perform multi-objective and multi-parameter collaborative optimization of the anti-high-frequency fatigue spring bar based on the established constraints and optimization objective function using the particle swarm optimization algorithm with constraints to obtain the optimal design variable results.

[0022] The present invention provides a design method and system for high-frequency fatigue-resistant high-speed railway fastener spring clips. Based on a particle swarm optimization algorithm with constraints, MATLAB and ANSYS software are used for joint simulation to perform multi-objective and multi-parameter collaborative optimization of high-frequency fatigue-resistant spring clips. Since the present invention uses an optimization algorithm to consider the overall optimization of the spatial geometric linear parameters of the spring clips, it resolves the contradiction between high self-frequency and low stress, providing a scientific reference for extending the service life of high-speed railway fastener systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flowchart of a method for designing a high-speed railway fastener spring clip that is resistant to high-frequency fatigue, provided by one embodiment of the present invention; Figure 2 A planar expansion diagram of the geometric structure of a spring clip in a method for designing a high-speed railway fastener spring clip that resists high-frequency fatigue provided by one embodiment of the present invention; Figure 3 A side view of the geometric structure of a spring clip in a method for designing a high-speed railway fastener spring clip that resists high-frequency fatigue, provided by one embodiment of the present invention; Figure 4 A graph showing the convergence of the fitness function value in a particle swarm optimization algorithm in a design method for a high-speed railway fastener clip that resists high-frequency fatigue, provided by one embodiment of the present invention; Figure 5 A graph showing an iterative design variable curve in a design method for a high-speed railway fastener spring clip resistant to high-frequency fatigue provided by one embodiment of the present invention; Figure 6 A comparison diagram of the spatial curves before and after optimization of a spring clip in a design method for a high-speed railway fastener spring clip resistant to high-frequency fatigue provided by one embodiment of the present invention; Figure 7 The first two vibration mode shapes of the optimized spring clip in a design method for a high-speed railway fastener spring clip resistant to high-frequency fatigue provided by one embodiment of the present invention; Figure 8 A comparison diagram of the vertical acceleration time-history response of the highest point of the spring clip's side limb in a design method for a high-speed railway fastener spring clip resistant to high-frequency fatigue provided by one embodiment of the present invention; Figure 9 A comparison diagram of equivalent stress time history curves of dangerous points of spring clips in a design method for high-speed railway fastener spring clips resistant to high-frequency fatigue provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below by means of specific embodiments in conjunction with the accompanying drawings. Similar elements in different embodiments are numbered with associated similar elements. In the following embodiments, many detailed descriptions are provided to enable the present invention to be better understood. However, those skilled in the art will readily appreciate that some of the features may be omitted under different circumstances, or may be replaced by other elements, materials, or methods. In some cases, some operations related to the present invention are not shown or described in the specification. This is to avoid the core of the present invention being overwhelmed by excessive descriptions, and for those skilled in the art, it is not necessary to describe these related operations in detail. They can fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0025] In addition, the features, operations, or characteristics described in the specification may be combined in any appropriate manner to form various embodiments. Furthermore, the steps or actions in the method description may be reordered or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various sequences in the specification and drawings are provided solely for the purpose of clearly describing a particular embodiment and are not intended to be mandatory, unless otherwise specified.

[0026] The first embodiment of the present invention provides a method for designing a high-speed railway fastener spring bar that is resistant to high-frequency fatigue. Figure 1 Provide detailed explanation.

[0027] like Figure 1 As shown, in step S100, a parametric model of the spring bar is established according to the basic linear parameters of the spring bar and the parametric equations of each central axis segment.

[0028] In this embodiment, there are 11 basic linear parameters of the spring bar, namely: arch span L, half arch span L1, side limb arch height H1, middle limb arch height H2, spring range H, middle limb front end radius R1, heel end radius R2, side limb front end radius R3, longitudinal distance from the center of the circle b1, front limb straight line b2, heel end straight line b3, as shown in FIG. Figure 2 、 Figure 3 The geometric structure of the bullet bar is shown in the plane unfolding diagram and side view.

[0029] In this embodiment, in order to obtain the constraint stiffness of the spring clip contact part so that the spring clip prestressed modal analysis can be smoothly performed in the subsequent optimization process, a three-dimensional Hertz linear simplified spring unit is used to simulate the contact between the spring clip and the adjacent components. The constraint stiffness of the spring clip contact part is determined using the particle swarm optimization algorithm and the Hertz contact theory. Specifically, the constraint stiffness of the spring clip contact area, the vertical constraint stiffness between the spring clip's middle limb and the flat washer, and the vertical constraint stiffness between the spring clip's heel end and the track gauge plate were calculated using Hertz contact theory. Before optimization, the two stiffnesses of the spring clip were 280 kN / mm and 70.17 kN / mm, respectively. During the optimization process, the two stiffnesses varied with the dimensions of the spring clip's contact position. The constraint stiffnesses of the spring clip's forelimb (vertical, longitudinal, transverse, and vertical), the front end of the middle limb (vertical, longitudinal, transverse, and vertical), and the heel end (vertical, longitudinal, and transverse) were determined using particle swarm optimization to be (0.81, 1.47, 1.37, 27.62, 54.07, 89.17, 13.23, and 81.43) kN / mm.

[0030] The specific process of obtaining the constraint stiffness of the forelimb (vertical, longitudinal, transverse and vertical), the front end (vertical, longitudinal, transverse and vertical) and the heel end (vertical and longitudinal) of the spring clip using the particle swarm optimization algorithm is as follows: the natural frequency calculated by the currently commonly used spring clip solid unit finite element model is used as a benchmark, a fitness function describing the frequency difference between the spring clip solid model and the beam model is constructed, the linear spring constraint stiffness of the spring clip beam unit model is taken as the design optimization object, and the optimal value of the beam model constraint stiffness is obtained based on the particle swarm optimization algorithm.

[0031] In this embodiment, the constraint stiffness of the spring clip is determined by the above method for the purpose of being used for the subsequent prestressed modal analysis of the spring clip, that is, for the subsequent MATLAB and ANSYS software joint simulation step using ANSYS to perform prestressed modal analysis and calculate the static equivalent stress and fundamental frequency of the spring clip.

[0032] like Figure 1 As shown, in step S200, the spring strip spatial geometric linear parameters are used as design variables, and geometric constraints and physical constraints are established to construct an optimization objective function that integrates the spring strip fundamental frequency and static equivalent stress.

[0033] The above steps specifically include: In this embodiment, in order to ensure the reasonable installation of the elastic bar, the design variables are selected as the seven geometric linear parameters of the elastic bar, which are: 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 from the center of the circle b1, the forelimb straight line b2, and the heel end straight line b3.

[0034] Constraints include geometric constraints and physical constraints. Geometric constraints ensure the geometric compatibility of the spring clips and correct installation on site, namely the five constraint equations:

[0035] Where C i is the i-th constraint; The physical constraints are that the buckle pressure meets the standard, the fundamental frequency of the spring clip is greater than 800 Hz and is lower than the initial equivalent stress, that is, the three constraint equations:

[0036] Where P is the buckle pressure of the front limb of the spring clip, f1 is the fundamental frequency after the spring clip is installed, σ smax is the initial static maximum equivalent stress of the spring clip.

[0037] The optimization objective function of the integrated spring clip fundamental frequency and static maximum equivalent stress is:

[0038] Where F is the objective function, 、 They are the fundamental frequency of the spring bar before optimization and the initial static maximum equivalent stress respectively.

[0039] like Figure 1 As shown, in step S300, according to the established constraints and optimization objective function, a particle swarm optimization algorithm with constraints is used to perform multi-objective and multi-parameter collaborative optimization of the anti-high-frequency fatigue spring bar to obtain the optimal design variable results.

[0040] The above steps specifically include: In this embodiment, the particle swarm optimization algorithm with constraints implements constraints on constraint equations C3, C4, and C5 by controlling the upper and lower limits of the design variables; the penalty function method is used to introduce constraints for the remaining five constraint equations, as follows: The penalty term e of the jth particle's i-th constraint ij for:

[0041] Where C ij is the i-th constraint of the j-th particle; Normalize the constraint penalty term:

[0042] Where N s is the population size; Calculate the weight of the i-th constraint penalty term:

[0043] Where, L i is the weight of the i-th constraint penalty term, N c is the number of constraint equations, take N c =5.

[0044] In this embodiment, the specific steps of the particle swarm optimization algorithm with constraints are: 1) Initialize the population and particle velocity and position, where the population refers to the geometric linear parameter vector of the spring space as the design variable, and the particle refers to each variable parameter in the population; 2) Update the speed and position of each particle:

[0045] Where k is the current iteration number, x k 、v k are the particle position and velocity of the kth iteration, ω s is the inertia factor, which decreases from 0.9 to 0.4 with the iteration. c1 and c2 are constants, c1=c2=2, which represents the influence of the particle group on a single particle. r1 and r2 are random numbers between [0,1]. k is the optimal position of the current population, p g is the global optimal position experienced by all particles in the population; 3) MATLAB and ANSYS software are used for joint simulation to calculate the fitness value function. The calculation formula is:

[0046] Where, is the fitness value function; 4) Compare the size of the fitness value function before and after the iteration, and guide the group to move towards the optimal solution, and proceed to the next iteration until the loop program terminates.

[0047] In this embodiment, the process of joint simulation using MATLAB and ANSYS software is as follows: 1) Use MATLAB software to write particle swarm optimization algorithm program; 2) Based on the joint simulation of MATLAB and ANSYS software, MATLAB initializes the spatial geometric line parameters of the spring clip and starts ANSYS; 3) ANSYS reads the spatial geometric linear parameters of the spring clip, performs prestressed modal analysis, calculates the static equivalent stress and fundamental frequency of the spring clip, and outputs the file; 4) MATLAB extracts the fundamental frequency and equivalent stress and calculates the fitness value function.

[0048] Figure 4 It is the convergence curve of the fitness function value in the particle swarm optimization algorithm. Figure 5 This is the iteration curve diagram of the design variables of the present invention. It can be seen that after 200 iterations, the fitness function value and the 7 design variables are close to a convergence state.

[0049] Figure 6 The figure shows the comparison of the spatial curves before and after the spring bar optimization. Table 1 shows the comparison of the design variables before and after the spring bar optimization. It can be seen that H1, b1, and b3 have large changes, which are speculated to be the key geometric parameters affecting the natural frequency of the spring bar.

[0050] Table 1. Comparison of design variables before and after spring clip optimization (unit: mm)

[0051] Figure 7 The first two vibration modes of the optimized spring bar are shown in the following figure. The first vibration mode is the antisymmetric flip vibration of the side limbs of the spring bar with the front limb and the heel end of the spring bar as the fulcrum, and the second vibration mode is the symmetric flip vibration of the side limbs.

[0052] Table 2 shows a comparison of key parameters before and after the spring clip optimization. After optimization, the first two natural frequencies of the spring clip are significantly improved, with the first natural frequency exceeding 800 Hz. The spring clip buckle pressure meets the standard, and the initial static maximum stress and mass are reduced.

[0053] Table 2. Comparison of key parameters before and after spring bar optimization

[0054] Furthermore, in this embodiment, the method further includes: step S400, establishing a high-frequency fatigue resistant spring bar dynamics model based on the optimal design variable results to verify the life extension effect.

[0055] Considering a high-speed train running at a constant speed of 300 km / h, the wheel-rail excitation is the superposition of measured wheel polygonal wear samples and short-wave random irregularity samples, and the dynamic response of the spring clip is calculated. Figure 8 This is a comparison of the vertical acceleration time history responses at the highest point of the spring clip's side limb. The peak acceleration drops from 331 g to 233 g.

[0056] Figure 9 The comparison of the equivalent stress time history curves at the dangerous point of the spring bar shows that the stress amplitude decreases from 114 MPa to 103 MPa, and the fatigue life decreases from 3.1×106 times increased to 7.2×10 8 times, an increase of about 232 times, verifying the life extension effect of the optimized spring bar.

[0057] Corresponding to the above-disclosed method for designing a high-speed railway fastener spring clip that resists high-frequency fatigue, an embodiment of the present invention further discloses a high-speed railway fastener spring clip design system that resists high-frequency fatigue, which specifically includes: A parametric model building module is used to build a parametric model of the spring clip based on the basic linear parameters of the fastener spring clip and the parametric equations of each central axis segment; The constraint and objective function establishment module is used to use the spring clip's spatial geometric linear parameters as design variables, establish geometric constraints and physical constraints, and construct an optimization objective function that integrates the spring clip's fundamental frequency and static equivalent stress; The optimization solution module is used to perform multi-objective and multi-parameter collaborative optimization of the anti-high-frequency fatigue spring bar based on the established constraints and optimization objective function using the particle swarm optimization algorithm with constraints to obtain the optimal design variable results.

[0058] It should be noted that for the detailed description of a high-speed railway fastener spring bar design system that is resistant to high-frequency fatigue provided in an embodiment of the present invention, reference can be made to the relevant description of a high-speed railway fastener spring bar design method that is resistant to high-frequency fatigue provided in an embodiment of the present application, and no further details will be given here.

[0059] In addition, an embodiment of the present invention also provides an electronic device, comprising: a processor and a memory; the memory is used to store one or more program instructions; the processor is used to run one or more program instructions to execute the steps of a method for designing a high-speed railway fastener spring bar that is resistant to high-frequency fatigue as described in any of the above items.

[0060] It should be noted that for the detailed description of an electronic device provided in an embodiment of the present invention, reference can be made to the relevant description of a high-speed railway fastener spring bar design method that is resistant to high-frequency fatigue provided in an embodiment of the present application, which will not be repeated here.

[0061] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for designing a high-speed railway fastener spring bar that is resistant to high-frequency fatigue as described in any of the above items are implemented.

[0062] It should be noted that for a detailed description of a computer-readable storage medium provided in an embodiment of the present invention, reference can be made to the relevant description of a high-frequency fatigue-resistant high-speed railway fastener spring bar design method provided in an embodiment of the present application, which will not be repeated here.

[0063] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments can be implemented by hardware or by computer program. When all or part of the functions in the above embodiments are implemented by computer program, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above functions can be implemented. In addition, when all or part of the functions in the above embodiments are implemented by computer program, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and saved in the memory of the local device by downloading or copying, or the system of the local device is updated. When the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be implemented.

[0064] The above examples are used to illustrate the present invention, which are only used to help understand the present invention and are not intended to limit the present invention. Those skilled in the art can make several simple deductions, modifications or substitutions based on the concept of the present invention.

Claims

1. A method for designing high-speed railway fastener spring clips that are resistant to high-frequency fatigue, characterized in that: The method comprises: A parametric model of the spring clip is established based on the basic linear parameters of the spring clip and the parametric equations of each central axis segment. Taking the spatial geometric linear parameters of the spring clip as design variables, and establishing geometric and physical constraints, an optimization objective function integrating the spring clip fundamental frequency and static equivalent stress is constructed. According to the established constraints and optimization objective function, the particle swarm optimization algorithm with constraints is used to perform multi-objective and multi-parameter collaborative optimization of the anti-high-frequency fatigue spring bar to obtain the optimal design variable results.

2. The method for designing a high-speed railway fastener spring clip resistant to high-frequency fatigue according to claim 1, characterized in that: The design variables are the geometric linear parameters of the spring strip space, including: In order to ensure the reasonable installation of the spring clip, the spatial geometric linear parameters of the spring clip selected and used as design variables include: side limb arch height H1, middle limb arch height H2, heel end radius R2, side limb front end radius R3, longitudinal distance from the center of the circle b1, forelimb straight line b2, and heel end straight line b3.

3. The method for designing a high-speed railway fastener spring clip resistant to high-frequency fatigue according to claim 2, wherein: Establish geometric and physical constraints, including: Geometric constraints ensure the geometric compatibility of the spring clips and their correct installation on site. The geometric constraint equations include: Where C i is the i-th constraint equation, R1 is the radius of the front end of the middle limb; The physical constraints include the buckle pressure of the front limb of the spring clip, the fundamental frequency of the spring clip after installation, and the initial static maximum equivalent stress of the spring clip, all of which meet the requirements. The physical constraint equations are established as follows: Where P is the buckle pressure of the front limb of the spring clip, f1 is the fundamental frequency after the spring clip is installed, σ smax is the initial static maximum equivalent stress of the spring clip.

4. A method for designing a high-speed railway fastener spring clip resistant to high-frequency fatigue according to claim 3, characterized in that: Construct an optimization objective function that integrates the fundamental frequency of the spring clip and the static equivalent stress, specifically including: Where F is the objective function, 、 They are the fundamental frequency of the spring bar before optimization and the initial static maximum equivalent stress respectively.

5. The method for designing a high-speed railway fastener spring clip resistant to high-frequency fatigue according to claim 4, characterized in that: The particle swarm optimization algorithm with constraints is used to perform multi-objective and multi-parameter collaborative optimization for the optimization design of high-frequency fatigue-resistant spring bars, specifically including: Constraint equations C3, C4 and C5 are constrained by controlling the upper and lower limits of the design variables; The remaining constraint equations C1, C2, C6, C7, and C8 use the penalty function method to introduce constraint conditions. The specific formulas are as follows: The penalty term e of the i-th constraint of the j-th particle ij for: Where C ij is the i-th constraint of the j-th particle; Normalize the constraint penalty term: Where N s is the population size; Calculate the weight of the i-th constraint penalty term: Where, L i is the weight of the i-th constraint penalty term, N c is the number of constraint equations, take N c =5.

6. A method for designing a high-speed railway fastener spring clip resistant to high-frequency fatigue according to claim 5, characterized in that: The particle swarm optimization algorithm with constraints is used to perform multi-objective and multi-parameter collaborative optimization for the optimization design of high-frequency fatigue-resistant spring bars, specifically including: Initialize the population and particle velocity and position, where the population refers to the spring space geometric linear parameter vector as the design variable, and the particle refers to each variable parameter in the population; Update the velocity and position of each particle: Where k is the current iteration number, x k 、v k are the particle position and velocity of the kth iteration, x k+1 、v k+1 are the particle position and velocity of the k+1th iteration, ω s is the inertia factor; c1 and c2 are constants, indicating the degree of influence of the particle group on a single particle; r1 and r2 are random numbers between [0,1], p k is the optimal position of the current population, p g is the global optimal position experienced by all particles in the population; Calculate the fitness value function. The fitness value function formula is: Where, is the fitness value function; Compare the size of the fitness value function before and after the iteration, and guide the group to move towards the optimal solution, and proceed to the next iteration until the loop program terminates.

7. A method for designing a high-speed railway fastener spring clip resistant to high-frequency fatigue according to claim 6, characterized in that: The particle swarm optimization algorithm with constraints is used to perform multi-objective and multi-parameter collaborative optimization for the optimization design of high-frequency fatigue-resistant spring bars, specifically including: MATLAB and ANSYS software are used for joint simulation. The specific process is as follows: Use MATLAB software to write particle swarm optimization algorithm program; Based on the joint simulation of MATLAB and ANSYS software, MATLAB initializes the spatial geometric line parameters of the spring strip and starts ANSYS; ANSYS reads the spatial geometric linear parameters of the spring bar, performs prestressed modal analysis, calculates the static equivalent stress and fundamental frequency of the spring bar, and outputs them; MATLAB extracts the calculated fundamental frequency of the spring bar and the static equivalent stress value and calculates the fitness value function.

8. The method for designing a high-speed railway fastener spring clip resistant to high-frequency fatigue according to claim 7, characterized in that: ANSYS reads the spatial geometric linear parameters of the spring clip, performs prestressed modal analysis, calculates the static equivalent stress and fundamental frequency of the spring clip, and outputs them, including: The contact between the spring bar and adjacent components is simulated using a three-dimensional Hertz linear simplified spring element. The constraint stiffness of the spring bar contact part is determined using the particle swarm optimization algorithm and Hertz contact theory. Based on the obtained constraint stiffness of the spring bar contact part, prestressed modal analysis is performed to calculate the static equivalent stress and fundamental frequency of the spring bar.

9. The method for designing a high-speed railway fastener spring clip resistant to high-frequency fatigue according to claim 1, wherein: The method further comprises: Based on the results of the optimal design variables, a dynamic model of high-frequency fatigue-resistant elastic bars was established to verify the life extension effect.

10. A high-speed railway fastener spring clip design system that resists high-frequency fatigue, characterized in that: The system comprises: A parametric model building module is used to build a parametric model of the spring clip based on the basic linear parameters of the fastener spring clip and the parametric equations of each central axis segment; The constraint and objective function establishment module is used to use the spring clip's spatial geometric linear parameters as design variables, establish geometric constraints and physical constraints, and construct an optimization objective function that integrates the spring clip's fundamental frequency and static equivalent stress; The optimization solution module is used to perform multi-objective and multi-parameter collaborative optimization of the anti-high-frequency fatigue spring bar based on the established constraints and optimization objective function using the particle swarm optimization algorithm with constraints to obtain the optimal design variable results.

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