A method for evaluating the strength of a power iron accessory structure based on finite element simulation data

Abstract

This invention belongs to the field of finite element analysis technology, specifically relating to a method for assessing the structural strength of power railway accessories based on finite element simulation data. The method includes: reconstructing the physical model of the power railway accessory through inverse engineering and generating a geometric deviation field by difference with the theoretical model to identify initial manufacturing defects and guide adaptive mesh refinement; introducing stress triaxiality to nonlinearly polarize the energy deviation of mesh elements, constructing a local potential energy dispersion factor to quantify stress traps within the structure; further calculating the global topological fragility index within a dynamic period to determine the overall strength reliability; and finally, by comparing the causal relationship between stress triaxiality and the local potential energy dispersion factor, providing differentiated reinforcement guidance for local geometrical abrupt changes or insufficient material bearing capacity. This invention solves the problems of traditional static analysis neglecting microscopic damage accumulation and difficulty in distinguishing the nature of stress concentration, improving the realism and reinforcement accuracy of power railway accessory assessments.

Description

Technical Field

[0001] This invention relates to the field of finite element analysis technology. More specifically, this invention relates to a method for evaluating the structural strength of power railway accessories based on finite element simulation data. Background Technology

[0002] In the construction of power transmission and distribution lines, power iron fittings are key load-bearing components that support and fix insulators and conductors. The reliability of their structural strength is directly related to the safe operation of the power grid. Traditional strength testing of power iron fittings mainly relies on destructive sampling inspection using centrifugal tensile testing machines or static simulation using basic finite element analysis software.

[0003] Basic finite element analysis methods typically use standard load conditions as boundary conditions. By calculating the von Mises equivalent stress distribution of the iron fittings under rated tensile force and comparing its maximum stress value with the yield strength of the material, the structural qualification can be determined. Some improved technical solutions attempt to introduce topology optimization algorithms to guide the weight reduction design of iron fittings by identifying stress concentration areas.

[0004] However, under actual operating conditions, power railway fittings are subjected to long-term wind-induced polarization, ice loads, and strain cycles caused by temperature differences. Under the action of rapidly changing dynamic loads, traditional static finite element analysis often ignores the dynamic modulation effect of the accumulation of microscopic material damage on macroscopic stiffness, especially at right-angle bends or bolt fastening hole edges of the fittings, where small geometric deviations can significantly change the local stress triaxiality. Existing strength assessment methods lack a deep decoupling mechanism between the energy consumption characteristics of the simulation mesh and the structural topological vulnerability. Relying solely on a single maximum stress value cannot distinguish whether the stress increase is caused by insufficient redundancy in the overall structure or by pseudo-stress concentration caused by local geometric singularities. If local stress singularities are misjudged as overall strength defects and thickening and reinforcement operations are performed, it will not only cause unnecessary material waste, but may even lead to a decrease in the fundamental frequency of the structure due to increased mass, inducing a more serious risk of vibration fatigue. Summary of the Invention

[0005] To address the technical problems of existing technologies, such as neglecting damage accumulation and difficulty in distinguishing stress concentration properties, leading to distorted assessments, blind reinforcement, and a high risk of vibration fatigue, this invention provides a method for assessing the structural strength of electric railway accessories based on finite element simulation data. The method includes: acquiring the solid model and theoretical design model of the electric railway accessory to be tested; generating a geometric deviation field based on Boolean differential operations; and performing hierarchical meshing of the solid model based on initial defect nodes in the geometric deviation field to generate a tetrahedral mesh model containing multiple mesh elements; retrieving the standard stress model of the electric railway accessory to be tested; and applying a dynamic load time series to the stress-bearing end of the tetrahedral mesh model. The finite element incremental solver extracts the von Mises equivalent stress, three principal stresses, and strain energy density of each mesh element at each time step. Based on the von Mises equivalent stress and three principal stresses, the stress triaxiality of each mesh element is calculated, and an energy consumption characteristic space is constructed in conjunction with the strain energy density to obtain the local potential energy dispersion factor of each mesh element at each time step. Based on the spatial variance of the local potential energy dispersion factor of all mesh elements in the dynamic load cycle, and introducing a load proportional term, a global topological fragility index is calculated. The global topological fragility index is compared with the safety benchmark value. If it is not up to standard, a set of risky elements is selected based on the local potential energy dispersion factor, and a differentiated reinforcement strategy is implemented.

[0006] This invention constructs a geometric deviation field by using Boolean difference between the inverse model of the entity and the theoretical model, and introduces a local potential energy discretization factor for stress triaxial polarization and a global topological fragility index. This solves the technical problems of existing technologies that neglect micro-damage accumulation under dynamic loads, cannot distinguish the nature of stress concentration, and waste resources due to blind reinforcement. It achieves physical quantification of manufacturing defects and stress traps in iron fittings, and can deeply decouple the causes of stress increase from the dimensions of energy distribution and topological robustness. Thus, while ensuring safe operation throughout the entire cycle, it significantly improves the authenticity of strength assessment and the accuracy of structural optimization, and avoids ineffective reinforcement caused by misjudgment of pseudo-stress concentration.

[0007] Preferably, the step of generating the geometric deviation field based on Boolean operation difference includes: acquiring the surface geometric point cloud of the electric railway accessory to be tested using a 3D scanner, and reconstructing it into the solid model through reverse engineering technology; performing Boolean operation difference between the solid model and the theoretical design model to generate the geometric deviation field, and marking the areas in the geometric deviation field with deviations greater than a preset deviation threshold as initial defect nodes, wherein the preset deviation threshold ranges from 0.5 mm to 2 mm.

[0008] Preferably, the hierarchical meshing of the entity model includes: performing local mesh refinement for the region where the initial defect node is located and the region where the radius of curvature is less than a preset curvature threshold, wherein the preset curvature threshold ranges from 2 mm to 10 mm.

[0009] This invention improves the gradient capture accuracy of stress-concentration sensitive areas such as bolt hole edges and chamfers by performing hierarchical local mesh refinement on initial defects and curvature-sensitive areas, while ensuring overall computational efficiency. It effectively solves the numerical divergence problem caused by traditional uniform meshes at singularity positions.

[0010] Preferably, the method for obtaining the three principal stresses includes: performing eigenvalue decomposition on the stress tensor of each mesh element to extract the normal stress vectors in three mutually perpendicular principal directions.

[0011] Preferably, the formula for calculating the stress triaxiality is: In the formula, For grid cells at time step Stress triaxiality under the following conditions; For grid cells at time step The first principal stress; For grid cells at time step The second principal stress; For grid cells at time step The third principal stress below; For grid cells at time step The von Mises equivalent effect.

[0012] This invention determines the degree to which a local material is constrained by hydrostatic stress by calculating stress triaxiality. It can reflect the failure tendency of a material from ductile to brittle and is a key mechanical criterion for distinguishing geometric singularities from structural defects.

[0013] Preferably, the formula for calculating the local potential energy discretization factor is: In the formula, For grid cells at time step The local potential energy discretization factor under the given conditions; It is the natural logarithm function; For grid cells at time step Strain energy density at the following conditions; For time step Average strain energy density of the entire field grid element; For grid cells at time step Stress triaxiality under the following conditions; It is an exponential function with the natural constant as its base.

[0014] This invention introduces stress triaxiality as an exponential term to nonlinearly polarize energy deviation, giving energy consumption characteristics a high sensitivity to complex multiaxial stress states, enabling it to amplify and capture hidden stress traps inside the structure, and quantify the impact of local defects on the overall fatigue life.

[0015] Preferably, the global topological vulnerability index is calculated as follows: In the formula, This is a global topological fragility index; The total number of time steps; For time step The variance of the local potential energy discretization factor of the entire field grid cell; This represents the sum of strain energy densities of all grid elements in the entire field. For time step The load amplitude below; This is the rated ultimate load; It is a sine function; This is the load ratio term.

[0016] This invention constructs a dynamic index reflecting the structural topology's resistance to disturbances by calculating the spatial variance of energy disorder and combining it with the load ratio term. This index can reveal the overall robustness boundary of the structure under variable load conditions and provides systematic quantitative support for determining whether the overall structural redundancy is insufficient.

[0017] Preferably, the selection of the risk unit set includes: sorting the grid units in descending order of the average value of the local potential energy discretization factor at all time steps, and selecting the top 5% of the grid units to form the risk unit set.

[0018] This invention achieves dimensionality reduction of structural reinforcement targets by sorting and screening risk unit sets based on the average value of local potential energy discrete factors. It focuses massive grid data on a very small number of key units that truly threaten security, greatly improving the execution efficiency of subsequent reinforcement strategies.

[0019] Preferably, comparing the global topology vulnerability index with the security benchmark value includes: if the global topology vulnerability index is greater than the security benchmark value, it is determined to be unqualified.

[0020] Preferably, the implementation of the differentiated reinforcement strategy includes: if the stress triaxiality of all grid cells in the risk cell set is less than a preset triaxiality threshold, a geometric correction strategy is implemented, and a correction parameter for increasing the chamfer radius at the corresponding position is output; if the stress triaxiality of all grid cells in the risk cell set is greater than or equal to the preset triaxiality threshold, a structural reinforcement strategy is implemented, and a correction scheme for adding stiffeners or increasing wall thickness at the corresponding position is output.

[0021] This invention performs differentiated correction based on the quantitative relationship between stress triaxiality and threshold, achieving a precise mapping between failure causes and reinforcement measures. By accurately distinguishing between geometric change causes and insufficient load-bearing capacity causes, it guides the system to provide precise chamfer correction or local reinforcement schemes, fundamentally eliminating the fundamental frequency drop and material waste caused by blindly increasing thickness and reinforcement.

[0022] The beneficial effects of this invention are as follows:

[0023] (1) This invention incorporates the real geometric deviations generated during the casting or processing of electric railway accessories into the simulation benchmark by inversely restoring the solid model and combining it with Boolean difference to generate a geometric deviation field. This effectively solves the problem of evaluation distortion caused by the traditional method relying only on the ideal geometric model. At the same time, by loading the dynamic load time series of simulated wind vibration and performing adaptive mesh refinement, the dynamic response of the material under strain cycle is captured, which significantly improves the authenticity and reliability of the structural strength evaluation of electric railway accessories under actual operating conditions.

[0024] (2) This invention breaks through the single evaluation dimension that relies solely on the absolute value of the von Mises equivalent stress. It introduces stress triaxiality to nonlinearly polarize the strain energy density of the unit and constructs a local potential energy discrete factor that can quantify local stress traps. Combined with the global topological fragility index that reflects the overall topological resistance to disturbances, this invention can deeply identify whether structural weaknesses originate from local geometric singularities or systemic topological redundancy, thereby accurately quantifying the brittle fracture risk and fatigue danger points under multiaxial stress.

[0025] (3) By performing a classification logic based on stress triaxiality threshold on the risk unit set, the present invention achieves a precise mapping between failure causes and reinforcement measures, and can scientifically distinguish between local geometric mutations and insufficient material bearing capacity. This differentiated guidance strategy fundamentally eliminates the waste of materials caused by blindly increasing the thickness and reinforcement, and effectively avoids the problem of the fundamental frequency dropping and the risk of secondary vibration fatigue caused by the disorderly increase of structural mass, thus ensuring the safety and economic benefits of electric railway accessories throughout their entire life cycle. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a method for evaluating the structural strength of electric railway accessories based on finite element simulation data, as described in this invention. Figure 2 This is a schematic diagram illustrating the maximum equivalent stress distribution under the traditional method; Figure 3 This is a schematic diagram illustrating the distribution of the local potential energy dispersion factor under the method of the present invention; Figure 4 This is a schematic diagram illustrating the spatial location of risk units and differentiated reinforcement decisions. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0029] This invention discloses a method for evaluating the structural strength of electric railway accessories based on finite element simulation data, referring to... Figure 1 This includes steps S1 to S4: S1: Use a 3D scanner to acquire the geometric point cloud of the object surface and reconstruct the solid model. Combine the finite element analysis software to perform hierarchical meshing of the solid model and generate a tetrahedral mesh model. Set displacement constraint boundaries and dynamic load time series according to the iron attachments.

[0030] It should be noted that, due to casting shrinkage cavities or machining deviations in the actual processing of electric iron accessories, their actual mechanical performance deviates from the ideal model. If simulation is based solely on ideal geometry, the impact of initial manufacturing defects on strength cannot be assessed. Therefore, this invention generates a geometric deviation field by performing Boolean operations to differentiate the inversely reconstructed solid model with the theoretical design model, and marks areas with large deviations as potential risk areas, thereby guiding adaptive mesh refinement.

[0031] Specifically, a 3D scanner is used to perform a full-range scan of the electric railway accessory under test to obtain the geometric point cloud of the object surface; reverse engineering technology is used to restore the geometric point cloud into a solid model; Boolean operation difference is performed between the solid model and the theoretical design model to generate a geometric deviation field; and the area in the geometric deviation field with a deviation greater than a preset deviation threshold is marked as the initial defect node.

[0032] The preset deviation threshold is an initial defect judgment parameter set to quantify the degree of difference between the physical model and the design model. If the value is set too small, normal machining tolerances will be mistakenly judged as structural defects, leading to disordered expansion of the mesh refinement area and masking the real manufacturing risks. If the value is set too large, severe casting shrinkage cavities or dimensional deviations will not be marked as potential risk areas, causing the initial defect features to be smoothed in the simulation and reducing the realism of the strength assessment. Therefore, its value range is set to 0.5 mm to 2 mm. In this embodiment, it is set to 1 mm to achieve accurate positioning of destructive manufacturing defects. In other embodiments, it can be fine-tuned according to the manufacturing grade requirements of the iron accessories.

[0033] Furthermore, in the finite element analysis software, the solid model is hierarchically meshed to generate a tetrahedral mesh model containing multiple mesh elements; during the meshing process, local mesh refinement is performed in the areas where the initial defect nodes are located and in areas where the radius of curvature is less than the preset curvature threshold.

[0034] The preset curvature threshold is a mesh control parameter set to identify subtle geometric features on the surface of the iron fittings. If this value is set too small, the system will generate redundant computational overhead due to capturing too much fine geometric noise, resulting in a significant decrease in simulation efficiency and failing to meet the real-time requirements of online detection. If it is set too large, the system will ignore key stress concentration areas such as chamfers and bolt hole edges, leading to distortion in local gradient capture and a serious risk of missed detection. Therefore, its value is set to a range of 2 mm to 10 mm. In this embodiment, it is set to 5 mm to ensure high-fidelity extraction of stress-sensitive areas while maintaining computational efficiency. In other embodiments, it can be fine-tuned according to the geometric fineness of the iron fittings.

[0035] Furthermore, the system retrieves the standard stress model of this type of power iron accessory under specific meteorological conditions from the power transmission business database, defines the fixed end of the iron accessory as the displacement constraint boundary, and applies a dynamic load time series simulating wind vibration load to the stressed end. The dynamic load time series includes the load amplitude at each time step.

[0036] It should be noted that by introducing a geometric deviation field based on Boolean operations during the modeling stage, this invention enables the actual manufacturing quality to be loaded into the simulation model as an initial defect, which significantly improves the realism of the strength assessment of electric railway accessories under actual working conditions.

[0037] S2: Based on the von Mises equivalent stress and the three principal stresses, the stress triaxiality of each grid element is calculated, and the energy consumption characteristic space is constructed by combining the strain energy density to obtain the local potential energy discretization factor of each grid element at each time step.

[0038] It should be noted that since the structural failure of electric railway accessories usually starts in the high stress triaxiality region, and conventional assessment methods only focus on the absolute value of equivalent stress, they can easily mask the risk of brittle fracture under high hydrostatic pressure. As a result, the assessment results cannot accurately reveal the true stress constraint level under multiaxial stress. Therefore, this invention introduces stress triaxiality as a weight and combines it with the spatial deviation of grid cell energy density to construct an energy consumption characteristic space, thereby realizing the physical quantification of the true fatigue hazard point.

[0039] Specifically, the displacement field, strain components, and stress components of each mesh element are obtained using the solver of the finite element analysis software through the following logic: 1. The system defines an elastic matrix based on the material properties of the solid model, constructs a mesh element stiffness matrix by combining the geometric parameters of the mesh elements, and forms a global stiffness matrix through coordinate transformation and assembly. It constructs an external load vector using displacement constraint boundaries and dynamic load time series, and uses a solution algorithm to calculate the displacement vector of each mesh node, thereby forming the displacement field of the entire mesh element.

[0040] 2. Based on the geometric equations, the spatial derivative of the nodal displacements is obtained using the mesh element shape function to acquire the strain components of each mesh element. These strain components include three normal strain components and three shear strain components, which characterize the degree of geometric deformation of the material element under dynamic loading. Based on this, according to the physical equations, i.e., the material constitutive relation, the strain components are multiplied by the material's elasticity matrix to calculate the stress components of each mesh element. These stress components include three normal stress components and three shear stress components, which quantify the distribution of internal forces generated within the material to resist deformation.

[0041] Furthermore, the system utilizes a finite element incremental solver to calculate and extract the von Mises equivalent stress, first principal stress, second principal stress, third principal stress, and strain energy density of each mesh element at each moment, based on the constitutive model of the material and the displacement field of the mesh elements.

[0042] The first principal stress, the second principal stress, and the third principal stress are obtained by extracting the normal stress vectors in three mutually perpendicular principal directions through eigenvalue decomposition of the stress tensor of the mesh element. They are used to characterize the ultimate stress level inside the material under a specific spatial orientation. The von Mises equivalent stress is a scalar value obtained by mathematical synthesis based on the above three principal stresses using the fourth strength theory, namely the distortion energy criterion. It is used to quantify the comprehensive strength of the material to yield or fail under complex stress combinations.

[0043] Furthermore, the strain energy density is obtained by volume integration of the product of the stress component and the strain component generated by the grid cell under dynamic load, which characterizes the mechanical energy accumulated in a unit volume of material due to elastic deformation. As a basic variable for measuring the non-uniformity of energy distribution, this index provides key physical field data support for identifying stress traps inside the structure and evaluating topological robustness in subsequent steps.

[0044] Specifically, for each grid cell, based on its time step... Calculate the stress triaxiality of the mesh element under the von Mises equivalent stress and the three principal stresses. :

[0045] In the formula, For grid cells at time step Stress triaxiality under the following conditions; , , and These represent the grid cells at time steps. The first principal stress, second principal stress, third principal stress, and von Mises equivalent stress are calculated.

[0046] It should be noted that traditional methods only focus on Mises stress, which can easily mask the risk of brittle fracture under high hydrostatic pressure. This invention, by introducing stress triaxiality, effectively reveals the true stress constraint level of iron fittings under complex stress states such as multiaxial tension.

[0047] Furthermore, the local potential energy discretization factor of the grid cells is calculated. :

[0048] In the formula, For grid cells at time step The local potential energy discretization factor under the given conditions; It is the natural logarithm function; For grid cells at time step Strain energy density at the following conditions; For time step Average strain energy density of the entire field grid element; For grid cells at time step Stress triaxiality under the following conditions; It is an exponential function with the natural constant as its base.

[0049] It should be noted that in the calculation formula of the local potential energy discretization factor, the energy deviation under complex stress state is nonlinearly polarized by the exponential term. When the grid element is under multi-axis stress and energy consumption is abnormal, its value increases rapidly, simulating the stress trap characteristics inside the structure.

[0050] S3: Based on the spatial variance of the local potential energy discretization factor of the whole field grid element during the dynamic load cycle, and by introducing the load ratio term, calculate the global topological fragility index.

[0051] It should be noted that since the overall robustness of the iron fittings is modulated by the dynamic fluctuation of the load, if the structure exhibits severe energy disorder distribution under variable load, it indicates that there is a systematic defect in its topological layout. Therefore, this invention constructs an evaluation index that can reflect the structure's ability to resist disturbances by calculating the spatial variance of the discrete factor and introducing a load proportional term, thereby determining whether the overall strength of the structure meets the requirements for long-term operation.

[0052] Specifically, the global topological vulnerability index of power railway accessories is calculated over the entire dynamic load cycle. :

[0053] In the formula, This is a global topological fragility index; The total number of time steps; For time step The variance of the local potential energy discretization factor of the entire field grid cell; This represents the sum of strain energy densities of all grid elements in the entire field. For time step The load amplitude below; This is the rated ultimate load; It is a sine function; This is the load ratio term.

[0054] It should be noted that this calculation term uses a sinusoidal feedforward factor to simulate the increased sensitivity of the load to energy disorder. When the load amplitude increases, the structural disorder characterized by the first term is given a higher weight, thereby capturing the structural vulnerability under extreme conditions.

[0055] For example, Figure 2 This diagram illustrates the maximum equivalent stress distribution under traditional methods, showing the spatial distribution of the maximum von Mises equivalent stress of electric ferroelectric components extracted using traditional finite element analysis during dynamic load cycles. Different contour lines represent the magnitude of stress values ​​in the mesh elements. While the main high-stress concentration zones have significant values, if their stress triaxiality is low, they do not actually possess the high-risk characteristics of brittle fracture. Traditional methods rely solely on the absolute value of equivalent stress, easily misclassifying areas of increased stress under uniaxial loading as high-risk zones and tending to thicken and reinforce such areas. This fails to identify the true physical causes of failure, leading to design redundancy and material waste, and also fails to reveal hidden fatigue hazards.

[0056] For example, Figure 3 This is a schematic diagram of the local potential energy dispersion factor distribution under the method of the present invention, showing the spatial distribution of the local potential energy dispersion factor calculated by the method of the present invention on the surface of the electric railway accessory structure. This index is obtained by introducing stress triaxiality to perform nonlinear polarization processing on the strain energy density deviation of the grid element. The region where the value in the figure shows the peak value represents the physical characteristics of the location having both highly disordered energy distribution and strong multiaxial stress constraint. Figure 2 The high value area in Figure 3The low numerical value indicates that the method of the present invention significantly suppresses the pseudo-stress concentration areas in traditional methods that are high in stress value but belong to simple uniaxial forces, and successfully highlights the hidden risk areas under high hydrostatic pressure. This shows that the local potential energy dispersion factor can effectively decouple the simple increase in numerical value from the actual physical damage tendency, accurately quantify the stress traps inside the structure, and thus identify the brittle fracture risk points that are easily missed by traditional methods.

[0057] S4: Compare the global topological fragility index with the safety benchmark value. If it fails to meet the standard, select a set of risk units based on the local potential energy dispersion factor and implement a differentiated reinforcement strategy.

[0058] It should be noted that, due to the differences in the physical mechanisms that cause structural fragility, precise reinforcement guidance cannot be provided without distinguishing the indicative causes. Therefore, this invention determines whether the defect is caused by a local geometrical abrupt change or insufficient material bearing capacity by comparing the relative relationship between stress triaxiality and dispersion factor, and then performs geometric correction or structural reinforcement actions accordingly.

[0059] Specifically, setting safety benchmark values To perform a pass / fail determination; the safety benchmark value The setting method is as follows: by performing an ideal state simulation of the rated maximum design load of the accessory of this model, the vulnerability index under standard working conditions is calculated and obtained.

[0060] Furthermore, compare the global topological fragility index. With safety benchmark value ;like Greater than If the structural strength is deemed unqualified, all grid cells are sorted in descending order of the average local potential energy dispersion factor of the grid cells at all time steps, and the top 5% of grid cells are selected to form a risk cell set.

[0061] Furthermore, the system executes the following differentiated guidance logic for the risk unit set: 1. If the stress triaxiality of all mesh elements in the risk element set is less than the preset triaxiality threshold, it is determined that the current defect is caused by a local geometric change. A geometric correction strategy is executed, and the correction parameter of increasing the chamfer radius at the corresponding position is output.

[0062] 2. If the stress triaxiality of all mesh elements in the risk element set is greater than or equal to the preset triaxiality threshold, it is determined that the current defect is caused by insufficient material bearing capacity. The structural reinforcement strategy is executed, and the correction scheme of adding stiffeners or increasing wall thickness at the corresponding position is output.

[0063] The preset triaxiality threshold is a logical classification parameter set to distinguish stress failure induction. If this value is set too small, the system will misjudge normal uniaxial stress characteristics as complex multiaxial tensile states, leading to incorrect reinforcement suggestions pointing to increasing wall thickness and wasting material. If it is set too large, mesh cells under significant brittle fracture risk will be misjudged as simple geometric abrupt changes, resulting in suggestions that only involve chamfering and cannot fundamentally solve the problem of insufficient load-bearing capacity. Therefore, its value range is set to 0.33 to 0.6, and in this embodiment it is set to 0.4 to ensure accurate identification of the mechanical degradation tendency of the material under multiaxial constraints. In other embodiments, it can be fine-tuned according to the ductility characteristics of the material.

[0064] It should be noted that by distinguishing the combination relationship between local potential energy dispersion factor and stress triaxiality, this invention provides differentiated reinforcement guidance for geometric abrupt changes or insufficient material bearing capacity, thereby achieving intelligent classification of structural optimization. While ensuring the safe operation of power grid fittings, it avoids the material redundancy and equipment vibration risks caused by blindly thickening the fittings in traditional methods.

[0065] For example, Figure 4 This diagram illustrates the spatial location of risk units and differentiated reinforcement decisions. It shows the specific structural distribution of the top 5% of risk units selected based on the global topological fragility index, and classifies and marks these risk units according to stress triaxiality thresholds. Different shaped markers in the diagram represent the differentiated guidance strategies provided by the system: circular markers correspond to areas where stress triaxiality is less than a preset threshold, suggesting geometric corrections such as adding chamfers; triangular markers correspond to areas where stress triaxiality is greater than or equal to the preset threshold, suggesting structural reinforcement such as adding stiffeners or increasing wall thickness. This visually demonstrates the differentiated guidance strategy of this invention: by introducing physical mechanism-level criteria, the system accurately classifies failure causes into two categories: local geometrical abrupt changes and insufficient material bearing capacity. For areas requiring only geometrical modification, unnecessary overall thickening is avoided; for areas with genuine multiaxial stress risks, targeted reinforcement solutions are provided.

Claims

1. A method for evaluating the structural strength of electric railway accessories based on finite element simulation data, characterized in that, include: Obtain the physical model and theoretical design model of the electric railway accessory to be tested, generate a geometric deviation field based on Boolean operation difference, and perform hierarchical meshing of the physical model based on the initial defect nodes in the geometric deviation field to generate a tetrahedral mesh model containing multiple mesh elements. The standard stress model of the electric iron accessory to be tested is retrieved, and a dynamic load time series is applied to the stress end of the tetrahedral mesh model. The von Mises equivalent stress, three principal stresses and strain energy density of each mesh element at each time step are extracted by the finite element incremental solver. Based on the von Mises equivalent stress and the three principal stresses, the stress triaxiality of each grid element is calculated, and the energy consumption characteristic space is constructed in combination with the strain energy density to obtain the local potential energy discretization factor of each grid element at each time step. Based on the spatial variance of the local potential energy discretization factor of the whole field grid cells during the dynamic load cycle, and by introducing the load ratio term, the global topological fragility index is calculated. The global topological vulnerability index is compared with the security benchmark value. If the index is not up to standard, a set of risk units is selected based on the local potential energy dispersion factor, and a differentiated hardening strategy is implemented.

2. The method for evaluating the structural strength of electric railway accessories based on finite element simulation data according to claim 1, characterized in that, The method of generating a geometric deviation field based on Boolean operation difference includes: acquiring the surface geometric point cloud of the electric railway accessory to be tested using a 3D scanner, and reconstructing it into a solid model using reverse engineering technology; performing Boolean operation difference between the solid model and the theoretical design model to generate the geometric deviation field, and marking the areas in the geometric deviation field with deviations greater than a preset deviation threshold as initial defect nodes, wherein the preset deviation threshold ranges from 0.5 mm to 2 mm.

3. The method for evaluating the structural strength of electric railway accessories based on finite element simulation data according to claim 1, characterized in that, The hierarchical meshing of the entity model includes: performing local mesh refinement for the region where the initial defect node is located and the region where the radius of curvature is less than a preset curvature threshold, wherein the preset curvature threshold ranges from 2 mm to 10 mm.

4. The method for evaluating the structural strength of electric railway accessories based on finite element simulation data according to claim 1, characterized in that, The method for obtaining the three principal stresses includes: performing eigenvalue decomposition on the stress tensor of each mesh element and extracting the normal stress vectors in the three mutually perpendicular principal directions.

5. The method for evaluating the structural strength of electric railway accessories based on finite element simulation data according to claim 1, characterized in that, The formula for calculating the stress triaxiality is: ； In the formula, For grid cells at time step Stress triaxiality under the following conditions; For grid cells at time step The first principal stress; For grid cells at time step The second principal stress; For grid cells at time step The third principal stress below; For grid cells at time step The von Mises equivalent effect.

6. The method for evaluating the structural strength of electric railway accessories based on finite element simulation data according to claim 1, characterized in that, The formula for calculating the local potential energy discretization factor is: ； In the formula, For grid cells at time step The local potential energy discretization factor under the given conditions; It is the natural logarithm function; For grid cells at time step Strain energy density at the following conditions; For time step Average strain energy density of the entire field grid element; For grid cells at time step Stress triaxiality under the following conditions; It is an exponential function with the natural constant as its base.

7. The method for evaluating the structural strength of electric railway accessories based on finite element simulation data according to claim 1, characterized in that, The formula for calculating the global topological vulnerability index is: ； In the formula, This is a global topological fragility index; The total number of time steps; For time step The variance of the local potential energy discretization factor of the entire field grid cell; This represents the sum of strain energy densities of all grid elements in the entire field. For time step The load amplitude below; This is the rated ultimate load; It is a sine function; This is the load ratio term.

8. The method for evaluating the structural strength of electric railway accessories based on finite element simulation data according to claim 1, characterized in that, The selection of the risk unit set includes: sorting the grid units in descending order of the average value of the local potential energy discretization factor at all time steps, and selecting the top 5% of the grid units to form the risk unit set.

9. The method for evaluating the structural strength of electric railway accessories based on finite element simulation data according to claim 1, characterized in that, The comparison of the global topology vulnerability index with the security benchmark value includes: if the global topology vulnerability index is greater than the security benchmark value, it is determined to be unqualified.

10. The method for evaluating the structural strength of electric railway accessories based on finite element simulation data according to claim 1, characterized in that, The implementation of the differentiated hardening strategy includes: If the stress triaxiality of all mesh elements in the risk element set is less than the preset triaxiality threshold, a geometric correction strategy is executed, and a correction parameter that increases the chamfer radius at the corresponding position is output. If the stress triaxiality of all grid cells in the risk cell set is greater than or equal to a preset triaxiality threshold, a structural reinforcement strategy is executed, and a correction scheme is output that adds stiffeners or increases wall thickness at the corresponding positions.

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