High-speed wheel-rail contact crack prediction method based on crystal plasticity-phase field method

Abstract

The invention discloses a high-speed wheel-rail contact crack prediction method based on a crystal plasticity-phase field method, and the method comprises the following steps: S1, building a high-speed wheel-rail model through finite element analysis software, and dividing a local wheel-rail contact region cube; s2, corresponding to the simulation model, cubes with the same geometric size are segmented in a wheel track and rail head contact area, the cubes are processed to be used for shooting an EBSD image, and the crystal size and crystal orientation statistical characteristics of a wheel track are obtained; s3, establishing a crystal plasticity-phase field coupling model and testing; s4, acquiring statistical parameters of crack forms, crack growth rates and crack geometry under the conditions of different load frequencies, load intensities and crystal fracture forms, and training the BP neural network optimized by the sparrow algorithm through the statistical parameters; and the relationship among the wide-range load frequency, load intensity, crystal fracture form, crack form, crack growth rate and crack geometry is obtained, so that the high-speed wheel-rail contact cracks under different loads are predicted.

Description

Technical Field

[0001] The invention belongs to the technical field of damage-fracture calculation, and in particular relates to a high-speed wheel-rail contact crack prediction method based on a crystal plasticity-phase field method. Background Art

[0002] High-speed trains are an important part of the layout of my country's transportation hub system, and they bear most of the country's key transportation capacity needs. Due to the high manufacturing cost, high-speed train tracks are usually not replaced frequently. During the operation of high-speed trains, the relative movement between the wheels and rails and the train wheels produces rolling contact. As the load increases, it will cause damage to the surface material of the track and produce microcracks, which will gradually develop into macro cracks until the material is destroyed, affecting the smooth operation of the train and even causing safety accidents. The traditional method for predicting cracks is the diffuse crack phase field method, but the phase field method only predicts the initiation and growth of cracks from a macro perspective, without considering the microscopic crystal structure of the material. In addition, for the prediction of high-speed wheel-rail cracks, the traditional method for predicting cracks does not fully consider the rolling friction contact conditions in the wheel-rail contact area, resulting in poor prediction results for high-speed wheels and rails. Therefore, it is particularly urgent to realize a method for predicting high-speed wheel-rail contact cracks. This method needs to meet certain prediction reliability and be able to accurately predict the occurrence of wheel-rail cracks. Summary of the invention

[0003] The purpose of the present invention is to solve the above problems and provide a high-speed wheel-rail contact crack prediction method based on the crystal plasticity-phase field method which is efficient and accurate and can greatly improve the prediction accuracy of high-speed wheel-rail contact cracks and ensure the accuracy of the results.

[0004] In order to solve the above technical problems, the technical solution of the present invention is: a high-speed wheel-rail contact crack prediction method based on crystal plasticity-phase field method, comprising the following steps:

[0005] S1. Establish a high-speed wheel-rail model through finite element analysis software and divide the local wheel-rail contact area into cubes;

[0006] S2. corresponding to the simulation model, cutting cubes of the same geometric size in the wheel-rail-rail head contact area, and processing the cubes for taking EBSD images to obtain the crystal size and crystal orientation statistical characteristics of the wheel-rail;

[0007] S3, establish and test the crystal plasticity-phase field coupling model;

[0008] S4. Obtain the statistical parameters of crack form, crack growth rate and crack geometry under different load frequencies, load intensities and crystal fracture forms, and train the BP neural network optimized by the sparrow algorithm through the statistical parameters to obtain the relationship between a wide range of load frequencies, load intensities, crystal fracture forms and crack form, crack growth rate and crack geometry, so as to predict high-speed wheel-rail contact cracks under different loads.

[0009] Furthermore, the S1 comprises the following sub-steps:

[0010] S11. Build a model in finite element software based on wheel-rail geometry;

[0011] S12. Divide the wheel-rail contact area into a local 1×1×1 mm cube and refine the mesh according to the dividing conditions.

[0012] Furthermore, the S2 comprises the following sub-steps:

[0013] S21, corresponding to the simulation model, cutting a cube with the same geometric size in the wheel-rail head physical contact area, and processing the cube to take an EBSD image, so as to obtain the crystal size and crystal orientation statistical characteristics of the wheel-rail;

[0014] S22, using Neper software, the Voronoi unit division of the segmented local 1×1×1 mm cube was performed according to the statistical characteristics of crystal size and crystal orientation obtained in S21;

[0015] S23, write a program to read the Voronoi unit division information obtained by Neper software and the grid node information of the local subdivided cube in step one, compare the grid node information of the first two, set the grid unit of the cube in step one to the corresponding Voronoi unit set, and re-import the finite element model in step one to complete the Voronoi unit division of the local cube of the finite element model.

[0016] Furthermore, S3 includes the following sub-steps:

[0017] S31, cutting a cylindrical specimen in the wheel-rail head contact area, and subjecting the cylindrical specimen to a tensile test to obtain a wheel-rail stress-strain curve. Meanwhile, a tensile test of the wheel-rail cut cylindrical specimen is also conducted in a finite element software; comparing the tensile test results with the simulation results, adjusting the simulated phase field fracture energy density so that the tensile stress-strain curve obtained by simulation coincides with the curve obtained by experiment, and obtaining a phase field fracture energy density parameter;

[0018] S32, on the premise of implementing the above steps, deriving and establishing a crystal plasticity-phase field coupling model of the segmented region, and combining the phase field and crystal plasticity to expand the finite element model of S12;

[0019] S33. Apply contact rolling fatigue load to predict wheel-rail crack initiation and propagation.

[0020] Furthermore, the statistical parameters in S4 include statistical parameters of different load frequencies, load intensities, crack forms under conditions of crystal fracture forms, crack growth rates, mean values, equations, and standard deviations of crack geometry.

[0021] Furthermore, the specific method of training the sparrow algorithm through statistical parameters in S4 is: improving the random initialization population method and position update strategy of the sparrow search algorithm, and writing a BP neural network program optimized by the improved sparrow search algorithm; using the obtained load parameters as input and the obtained crack parameters as output to train the neural network; and obtaining the relationship between a wide range of load frequencies, load intensities, crystal fracture forms and crack types, crack propagation rates, and crack geometries through verification.

[0022] The beneficial effects of the present invention are:

[0023] 1. The present invention provides a high-speed wheel-rail contact crack prediction method based on the crystal plasticity-phase field method. By establishing a wheel-rail finite element model, the local 1×1×1mm cube of the wheel-rail head contact area is divided to perform crystal characteristic test and phase field fracture energy calibration test and Voronoi unit division of the local contact area cube. By applying wheel-rail rolling contact load, a high-speed wheel-rail crystal plasticity-phase field finite element model is established.

[0024] 2. The present invention uses the crystal crack statistical parameters obtained from the finite element results to train the BP neural network optimized by the sparrow algorithm to predict the wheel-rail contact cracks of high-speed trains. This crack prediction method utilizes the advantages of the phase field method, combines the microscopic characteristics of the material crystal plasticity, and fully considers the wheel-rail contact conditions, thereby ensuring the accuracy of wheel-rail contact crack prediction.

[0025] 3. The present invention is different from the traditional phase field crack prediction method. On the basis of the phase field method, the present invention considers the microscopic characteristics of the material by establishing the crystal Voronoi unit, and considers the wheel-rail contact conditions by applying the rolling contact load. As a prediction method that considers the microscopic characteristics of the material and the wheel-rail contact load, the present invention can greatly improve the accuracy of high-speed wheel-rail contact crack prediction and ensure the accuracy of the results. The whole invention is efficient, accurate, and simple to implement, and has strong practical value and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a step diagram of a high-speed wheel-rail contact crack prediction method based on crystal plasticity-phase field method of the present invention;

[0027] Figure 2It is a high-speed wheel-rail finite element model of the present invention and a local section diagram of the rail head contact area;

[0028] Figure 3 It is a diagram of the Voronoi unit grid establishment process of the present invention;

[0029] Figure 4 It is a cross-sectional schematic diagram of the crystal plasticity-phase field model of the present invention;

[0030] Figure 5 is the normal force load distribution diagram of the present invention;

[0031] Figure 6 is the tangential force load distribution diagram of the present invention;

[0032] Figure 7 is a contact crack growth diagram of the present invention;

[0033] Figure 8 It is a diagram of stress distribution and load movement process of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments:

[0035] like Figures 1 to 4 As shown, the present invention provides a high-speed wheel-rail contact crack prediction method based on crystal plasticity-phase field method, comprising the following steps:

[0036] S1. Establish a high-speed wheel-rail model through finite element analysis software and divide the local wheel-rail contact area into cubes.

[0037] Step S1 includes the following sub-steps:

[0038] S11. Build a model in finite element software based on wheel-rail geometry.

[0039] S12. Divide the wheel-rail contact area into a local 1×1×1 mm cube and refine the mesh according to the dividing conditions.

[0040] S2. According to the simulation model, the wheel-rail contact area is cut into cubes with the same geometric size, and the cubes are processed for taking EBSD images to obtain the crystal size and crystal orientation statistical characteristics of the wheel-rail.

[0041] Step S2 includes the following sub-steps:

[0042] S21, corresponding to the simulation model, cuts a cube with the same geometric size in the actual contact area between the wheel and rail head, and processes the cube to take an EBSD image, so as to obtain the crystal size and crystal orientation statistical characteristics of the wheel and rail.

[0043] S22, using Neper software, perform Voronoi unit division on the segmented local 1×1×1 mm cube according to the statistical characteristics of crystal size and crystal orientation obtained in S21.

[0044] S23, write a program to read the Voronoi unit division information obtained by Neper software and the grid node information of the local cube in step 1, compare the grid node information of the first two, set the grid unit of the cube in step 1 to the corresponding Voronoi unit set, and re-import the finite element model in step 1 to complete the Voronoi unit division of the local cube of the finite element model. Figure 3 This method makes the nodes of the local cube area completely coincide with the nodes of the remaining area of ​​the finite element model, avoiding the problem of node misalignment caused by the conventional use of the tied link method, thereby improving the accuracy of the results.

[0045] S3. Establish and test the crystal plasticity-phase field coupling model.

[0046] Step S3 includes the following sub-steps:

[0047] S31, cutting a cylindrical specimen in the wheel-rail head contact area, and subjecting the cylindrical specimen to a tensile test to obtain a wheel-rail stress-strain curve. At the same time, a tensile test of the wheel-rail cut cylindrical specimen is also performed in a finite element software; comparing the tensile test results with the simulation results, adjusting the simulated phase field fracture energy density so that the tensile stress-strain curve obtained by simulation coincides with the curve obtained by experiment, and obtaining a phase field fracture energy density parameter.

[0048] S32, on the premise of implementing the above steps, derive and establish a crystal plasticity-phase field coupling model of the segmented region, and combine the phase field and crystal plasticity to expand the finite element model of S12.

[0049] (1) Derive the governing equation of crystal plastic phase field:

[0050] ψ=ψ e +ψ p +ψ φ ;

[0051] Where ψ is the energy of the entire system, ψ e is the elastic strain energy, ψ p is the plastic strain energy, ψ φ For fracture energy.

[0052]

[0053] Where g(φ) is the degradation function, E e is the elastic strain, L e is the material elastic stiffness matrix, and φ is the phase field.

[0054]

[0055] In the formula, is the shear stress intensity in each slip direction of the crystal, γ i is the shear slip rate in each direction, and N is the number of all slip planes in the crystal.

[0056]

[0057] Where ω c is the fracture energy per unit volume, C is the right Cauchy Green deformation tensor, and ▽0 is the gradient operator of the reference configuration.

[0058]

[0059] Where H is the crack driving energy, l φ is the phase field length factor.

[0060] (2) Derivation of crystal plasticity constitutive equation and hardening law:

[0061]

[0062] Where ρ i is the dislocation density in each slip plane direction, b is the Burger vector length, v0 is the reference dislocation velocity, τ i is the analytical shear stress, and n is the rate sensitivity coefficient.

[0063]

[0064] Where k multi and k annih are the dislocation multiplication constant and the dislocation annihilation constant.

[0065]

[0066] Where c is the proportional constant, G is the shear modulus, and χ ij is the Taylor coefficient matrix.

[0067] (3) After the spatial and temporal discretization operations on the above equations, the finite element differential equilibrium equation is obtained. Then, the crystal plasticity and the phase field method are coupled in the following way: the phase field method degeneration function g(φ) controls the evolution of the plastic slip rate in the crystal plasticity; and the plastic work in each direction of the crystal plasticity The accumulation of further controls the phase field evolution equation.

[0068] S33. Apply contact rolling fatigue load to predict wheel-rail crack initiation and propagation.

[0069] like Figure 5 to Figure 6As shown, fatigue load is added to the wheel-rail contact area, and the fatigue load is composed of normal load and tangential traction load. The wheel-rail contact surface is elliptical, and the Hertz contact theory determines the elliptical contact surface and ellipsoidal contact pressure distribution. The tangential traction load is divided into pure slip and adhesion parts. In the case of pure slip, the tangential traction distribution force q is proportional to the normal load distribution force p; however, under adhesion-slip conditions, the tangential traction distribution depends on the length of the pure slip and adhesion areas, which is determined by the strip theory. The tangential traction force and normal load distribution force in the strip theory are shown as follows:

[0070]

[0071] In the formula, p max is the Hertz peak pressure, x and y are the load positions, x c ,y c is the maximum load position, a and b are half of the contact strip width in x (rolling direction) and y (lateral direction), respectively.

[0072] q(x)=q1(x)+q2(x);

[0073]

[0074] Where q1 is the sliding tangential traction load, q2 is the adhesion traction load, and μ is the friction coefficient.

[0075] S4, such as Figure 7 to Figure 8 As shown in the figure, the statistical parameters of crack form, crack growth rate and crack geometry under different load frequencies, load intensities and crystal fracture forms are obtained, and the BP neural network optimized by the sparrow algorithm is trained by statistical parameters to obtain the relationship between a wide range of load frequencies, load intensities, crystal fracture forms and crack forms, crack growth rate and crack geometry, so as to predict high-speed wheel-rail contact cracks under different loads.

[0076] The statistical parameters in step S4 include different load frequencies, load intensities, crack forms under crystal fracture form conditions, crack growth rates, mean values, equations, and standard deviation statistical parameters of crack geometry.

[0077] The specific method of training the sparrow algorithm through statistical parameters in step S4 is: improving the random initialization population method and position update strategy of the sparrow search algorithm, and writing a BP neural network program optimized by the improved sparrow search algorithm; using the obtained load parameters as input and the obtained crack parameters as output to train the neural network; and obtaining the relationship between a wide range of load frequencies, load intensities, crystal fracture forms and crack types, crack propagation rates, and crack geometries through verification.

[0078] The present invention simplifies the analysis process by dividing the local contact area of ​​high-speed wheel and rail, couples the crystal plasticity-phase field method to consider the influence of material micro-plasticity on contact cracks, obtains the phase field fracture energy density of wheel and rail materials through tensile tests, programs the crystal plasticity model and the finite element model nodes to overlap to improve the analysis accuracy, applies wheel and rail head contact load, and trains the BP neural network optimized by the sparrow search algorithm. Finally, a wide range of load frequency, load intensity, crystal fracture form and crack form, crack extension rate, and crack geometry are obtained, and the high-speed wheel and rail contact cracks are predicted and analyzed.

[0079] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A high-speed wheel-rail contact crack prediction method based on crystal plasticity-phase field method, characterized by: The following steps are involved: S1. Establish a high-speed wheel-rail model through finite element analysis software and divide the local wheel-rail contact area into cubes; S2. corresponding to the simulation model, cutting cubes of the same geometric size in the wheel-rail-rail head contact area, and processing the cubes for taking EBSD images to obtain the crystal size and crystal orientation statistical characteristics of the wheel-rail; S3, establish and test the crystal plasticity-phase field coupling model; S4. Obtain the statistical parameters of crack form, crack growth rate and crack geometry under different load frequencies, load intensities and crystal fracture forms, and train the BP neural network optimized by the sparrow algorithm through the statistical parameters to obtain the relationship between a wide range of load frequencies, load intensities, crystal fracture forms and crack form, crack growth rate and crack geometry, so as to predict high-speed wheel-rail contact cracks under different loads.

2. The high-speed wheel-rail contact crack prediction method based on crystal plasticity-phase field method according to claim 1 is characterized in that: The S1 comprises the following sub-steps: S11. Build a model in finite element software based on wheel-rail geometry; S12. Divide the wheel-rail contact area into a local 1×1×1 mm cube and refine the mesh according to the dividing conditions.

3. The high-speed wheel-rail contact crack prediction method based on crystal plasticity-phase field method according to claim 1, characterized in that: The S2 comprises the following sub-steps: S21, corresponding to the simulation model, cutting a cube with the same geometric size in the wheel-rail head physical contact area, and processing the cube to take an EBSD image, so as to obtain the crystal size and crystal orientation statistical characteristics of the wheel-rail; S22, using Neper software, the Voronoi unit division of the segmented local 1×1×1 mm cube was performed according to the statistical characteristics of crystal size and crystal orientation obtained in S21; S23, write a program to read the Voronoi unit division information obtained by Neper software and the grid node information of the local subdivided cube in step one, compare the grid node information of the first two, set the grid unit of the cube in step one to the corresponding Voronoi unit set, and re-import the finite element model in step one to complete the Voronoi unit division of the local cube of the finite element model.

4. The high-speed wheel-rail contact crack prediction method based on crystal plasticity-phase field method according to claim 1, characterized in that: The S3 comprises the following sub-steps: S31, cutting a cylindrical specimen in the wheel-rail head contact area, and subjecting the cylindrical specimen to a tensile test to obtain a wheel-rail stress-strain curve. Meanwhile, a tensile test of the wheel-rail cut cylindrical specimen is also conducted in a finite element software; comparing the tensile test results with the simulation results, adjusting the simulated phase field fracture energy density so that the tensile stress-strain curve obtained by simulation coincides with the curve obtained by experiment, and obtaining a phase field fracture energy density parameter; S32, on the premise of implementing the above steps, deriving and establishing a crystal plasticity-phase field coupling model of the segmented region, and combining the phase field and crystal plasticity to expand the finite element model of S12; S33. Apply contact rolling fatigue load to predict wheel-rail crack initiation and propagation.

5. The high-speed wheel-rail contact crack prediction method based on crystal plasticity-phase field method according to claim 1, characterized in that: The statistical parameters in S4 include different load frequencies, load intensities, crack forms under crystal fracture form conditions, crack growth rates, mean values, equations, and standard deviation statistical parameters of crack geometry.

6. The high-speed wheel-rail contact crack prediction method based on crystal plasticity-phase field method according to claim 1, characterized in that: The specific method of training the sparrow algorithm through statistical parameters in S4 is: improving the random initialization population method and position update strategy of the sparrow search algorithm, and writing a BP neural network program optimized by the improved sparrow search algorithm; using the obtained load parameters as input and the obtained crack parameters as output to train the neural network; and obtaining the relationship between a wide range of load frequencies, load intensities, crystal fracture forms and crack types, crack propagation rates, and crack geometries through verification.

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