Corrosion pipeline material performance degradation coefficient assignment method for finite element analysis

Abstract

The application discloses a corrosion pipeline material performance degradation coefficient assignment method for finite element analysis, and relates to the technical field of pipeline safety evaluation. In view of the problems of high threshold of the prior art subroutine method, large error of uniform degradation assumption, and lack of complete technical closed loop, the method first acquires pipeline corrosion area data through ultrasonic, ray or eddy current detection, extracts characteristic parameters to construct a corrosion damage variable D. The corrosion area is divided into sub-regions, the mean value and standard deviation of D of each sub-region are counted, and a random damage variable is generated by adopting a logarithmic normal distribution with the mean value and standard deviation as parameters. The yield strength and elastic modulus degradation parameters are solved by relying on the calibration degradation mapping relationship, and are written into the material card of the finite element software to complete the assignment. The application does not need to write a user subroutine, and can be realized by relying on the original function of the software, the analysis error is less than or equal to 6%, the operation period is less than or equal to 24 hours, the error is reduced by more than 9% compared with the traditional uniform degradation method, and the application is suitable for various corrosion types and mainstream finite element software.

Description

Technical Field

[0001] This invention relates to the field of oil and gas pipeline safety assessment technology, specifically to a method for assigning the performance degradation coefficient of corroded pipeline materials for finite element analysis. Background Technology

[0002] Oil and gas pipelines, water supply pipelines, and other types of pipelines are susceptible to corrosion from soil and media during long-term service, leading to corrosion damage on the pipeline walls. This, in turn, causes material performance degradation, reduces the pipeline's load-bearing capacity, and may even trigger safety accidents such as leaks and explosions. Finite element analysis is a core technical means to assess the remaining strength of corroded pipelines and predict failure risks, and the reasonable assignment of the material performance degradation coefficient directly determines the accuracy and reliability of the finite element analysis results.

[0003] Currently, the techniques for assigning material property degradation values ​​in finite element analysis of corroded pipelines are mainly divided into three categories, each with obvious technical shortcomings: Category 1: Non-uniform degradation simulation methods based on user subroutines (UMAT / VUMAT). These methods achieve continuous spatial variation of material parameters by writing Fortran / C++ subroutines. While they can accurately characterize non-uniform degradation (e.g., a certain LS-DYNA secondary development scheme achieves non-uniform characterization error ≤5%), they have significant drawbacks: high programming threshold (requires mastery of the finite element secondary development interface, which 80% of ordinary engineering technicians cannot complete independently), complex debugging (average debugging cycle ≥7 days, prone to computational divergence), low computational efficiency (computation time increases by more than 30% compared to conventional analysis), and poor portability (a certain ABAQUS subroutine achieved a success rate of only 65% ​​after software upgrades).

[0004] The second category: Assignment methods based on the uniform degradation assumption. This type of method treats the corroded area as a single homogeneous region, using the average degree of corrosion damage to determine a uniform degradation coefficient, ignoring performance differences within the region, leading to significant biases in the analysis results. Empirical data shows that this method generally has an error ≥15% in predicting the failure pressure of corroded X70 pipeline steel (e.g., in one engineering case, the uniform degradation assumption predicted a failure pressure of 18.2 MPa, while the actual failure pressure was 15.6 MPa), failing to accurately reflect the pipeline's true load-bearing capacity.

[0005] The third category: related auxiliary technologies. Existing literature already includes methods for constructing corrosion damage variables (such as characterization methods based on corrosion depth and remaining wall thickness ratio), experimental calibration of material mechanical property degradation, and the application of log-normal distribution in corrosion data statistics. However, these technologies have not been combined with the finite element method's original material definition function, and a complete "detection-statistics-assignment-verification" closed-loop solution has not been formed, which cannot solve the engineering requirements of "low threshold + high precision".

[0006] Therefore, there is an urgent need for a method for assigning the performance degradation coefficient of corrosive pipeline materials that requires no subroutines, is easy to operate, has high precision, and is highly efficient, in order to solve the problem that "high threshold and high precision cannot be achieved simultaneously" in existing technologies. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method for assigning degradation coefficients of corroded pipeline materials for finite element analysis. This method divides the corroded area into several sub-regions. Based on the statistical characteristics of corrosion detection data, it generates random damage variables for each sub-region using a log-normal distribution. Then, it determines the degradation material parameters for each sub-region through a pre-defined degradation mapping relationship. Finally, it automatically generates material cards for finite element software through a preprocessing script, achieving the assignment of non-uniform degradation field values ​​for corroded material properties without writing any user subroutines.

[0008] To achieve the above objectives, this invention provides a method for assigning the degradation coefficient of corroded pipe materials for finite element analysis, characterized by comprising the following steps: S1: Obtain detection data of the pipeline corrosion area, extract corrosion characteristic parameters, and construct material corrosion damage variable D based on the corrosion characteristic parameters; S2: Divide the corroded area into N sub-regions, where N is an integer not less than 2; calculate the mean μ of the corrosion damage variable D in each sub-region. D and standard deviation σ D ; S3: For each sub-region, the corresponding random corrosion damage variable value D is generated using a log-normal distribution. rand :

[0009] Where Z is a standard normally distributed random number. and They are respectively composed of μ D and σ D The converted log-normal distribution location and scale parameters, μ D σ represents the mean of corrosion damage variables in the sub-region. D The standard deviation of the corrosion damage variable for the sub-region; S4: Based on material mechanical property experiments, the degradation mapping relationship between the material corrosion damage variable D and mechanical property parameters is calibrated, using the random damage variable values ​​D of each sub-region. rand Determine the degradation material property parameters corresponding to each sub-region; S5: Write the degradation material property parameters corresponding to each sub-region into the material card of the finite element software to complete the assignment of the non-uniform degradation field of material properties within the corrosion region.

[0010] Based on the above technical solutions, preferably, the detection data mentioned in S1 is obtained by ultrasonic testing, X-ray testing or eddy current testing, and the corrosion characteristic parameters include at least one of corrosion depth, remaining wall thickness and corrosion area percentage.

[0011] Based on the above technical solutions, the preferred method for constructing the corrosion damage variant D in S1 is as follows: For a uniform corrosion region, D = (d / t) × α; For the pitting cluster region, D = (1 R wt )×(1+0.5m); Where d is the corrosion depth, t is the pipe wall thickness, α is the morphology correction coefficient, α∈(0.8, 1.2), R wt denoted as the remaining wall thickness ratio, and m as the pitting corrosion area occupancy ratio.

[0012] Based on the above technical solutions, preferably, the location parameter μ of the log-normal distribution in S3 is... 1nD and scale parameter σ 1nD According to the following formula, μ D and σ D The conversion yields: ,

[0013] Where, μ D ∈ (0, 1), σ D ∈ (0, 0.5).

[0014] Based on the above technical solutions, it is preferred that the number N of sub-regions mentioned in S2 is in the range of 5-25, and the sub-regions are divided uniformly along the pipeline axis and circumferentially or non-uniformly according to the density of corrosion distribution.

[0015] Based on the above technical solutions, preferably, the functional form of the degradation mapping relationship in S4 is: ,

[0016] Where, σ y0 E0 and E0 represent the yield strength and elastic modulus of the uncorroded parent material, respectively; k1 and k2 are damage amplitude degradation coefficients, used to characterize the nonlinear attenuation of the dynamic yield strength by the corrosion damage variable; n1 and n2 are damage exponential sensitivity coefficients, used to characterize the exponential degradation effect of the corrosion damage variable on the dynamic elastic modulus. They are obtained by fitting experimental data, and k1∈(0,1), k2∈(0,1), n1≥1, n2≥1, with a fitting error ≤3%.

[0017] Based on the above technical solutions, preferably, the finite element software mentioned in S5 includes at least one of LS-DYNA, ABAQUS, and ANSYS, and the format of the material card is consistent with the native material card format of the finite element software used.

[0018] More preferably, the material card writing process described in S5 is automatically executed by a preprocessing script; the preprocessing script reads the random damage variable value D of each sub-region. rand The corresponding degradation yield strength and degradation elastic modulus are calculated based on the degradation mapping relationship. Independent material definitions are generated according to the material card format of the finite element software, and the elements corresponding to each sub-region are associated with the corresponding material numbers.

[0019] Based on the above technical solutions, preferably, the method further includes the following steps: repeatedly executing S3 to S5 to generate multiple sets of material property assignment schemes with different random damage variable distributions, submitting them for finite element analysis, applying external loads, such as blasting vibration loads, soil pressure loads, and internal pressure loads, statistically analyzing the failure probability distribution of the pipeline under the external loads, and fitting the model using a normal distribution or Weibull distribution model with a fitting accuracy ≥95%, thereby achieving a reliability assessment of the safety status of the corroded pipeline.

[0020] On the other hand, the present invention also provides a device for assigning the degradation coefficient of corroded pipe materials for finite element analysis, which is used to implement a method for assigning the degradation coefficient of corroded pipe materials for finite element analysis, including: The detection data acquisition module is used to acquire the original detection data of the pipeline corrosion area and transmit it to the corrosion damage variable construction module to provide basic data support for subsequent corrosion damage quantification and material performance degradation coefficient assignment. The corrosion damage variable construction module is used to receive the original pipeline corrosion detection data, transform it into a standardized and quantifiable corrosion damage variable D through a differentiated construction formula, perform data verification, and provide core data support for subsequent modules of the device, adapting to corrosion quantification and extended applications in multiple scenarios. The sub-region division module is used to divide the corrosion area into multiple sub-regions and to calculate the mean and standard deviation of corrosion damage variables in each sub-region. The random damage variable generation module is used to generate random damage variable values ​​for each sub-region using a log-normal distribution. The degradation material parameter calculation module is used to calculate the corresponding degradation material property parameters based on the random damage variable values ​​of each sub-region, according to the pre-calibrated degradation mapping relationship. The material card assignment module is used to write the degraded material property parameters of each sub-region into the material card of the finite element software, and complete the assignment of the non-uniform degradation field of the material properties in the corrosion area. Each module communicates and connects sequentially, forming a complete closed loop.

[0021] Compared with the prior art, the present invention has the following beneficial effects: (1) Based entirely on the native material card definition function of finite element software, the input file is automatically generated through preprocessing scripts. There is no need to write and debug complex user material subroutines. Ordinary engineering technicians can master it. It is easy to implement and has a low programming threshold.

[0022] (2) By dividing the corrosion area into multiple sub-regions and randomly generating differentiated degradation parameters for each sub-region based on measured statistical characteristics, the non-uniformity of material degradation is effectively reflected, and the spatial variability of material properties under actual corrosion conditions is reasonably characterized.

[0023] (3) The randomness of the damage variable is described by the log-normal distribution. The distribution parameters are directly derived from the mean and standard deviation of the detection data. The physical meaning is clear, the parameter calibration is simple, and the statistical characteristics are controllable.

[0024] (4) Since the parameters of the degraded material are already fixed in the material card of the input file, there is no need to call the subroutine for real-time calculation during the finite element solution process, which does not increase any calculation overhead and the solution efficiency is completely consistent with that of conventional analysis.

[0025] (5) The method does not depend on specific software versions or secondary development interfaces. It is applicable to mainstream finite element software such as LS-DYNA, ABAQUS, and ANSYS, and is easy to migrate and apply between different platforms. It has strong versatility and good portability. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a general flowchart of the method of the present invention; Figure 2 This is a schematic diagram showing the sub-region division of the corrosion area; Figure 3 A flowchart illustrating the process of generating random damage variables from a log-normal distribution; Figure 4 This is a graph showing the degradation mapping relationship between corrosion damage variables and material yield strength. Figure 5 This is a schematic diagram of the automatic generation process of material cards using finite element software. Detailed Implementation

[0028] The specific technical solutions of the present invention are described below with reference to the embodiments.

[0029] The method for assigning the degradation coefficient of corroded pipe materials for finite element analysis includes the following steps: S1: Constructing corrosion damage variables Obtain detection data of the pipeline corrosion area, extract corrosion characteristic parameters, and construct corrosion damage variable D based on the corrosion characteristic parameters; The corrosion damage variable D ranges from 0 to 1, representing the degree of material performance degradation caused by pipeline corrosion. D=0 indicates no corrosion, and D=1 indicates complete loss of load-bearing capacity. The test data is obtained through ultrasonic testing, X-ray testing, or eddy current testing. These three testing methods are the mainstream non-destructive testing methods in the industry, which can cover the testing needs of different corrosion types and ensure the accuracy and availability of the test data.

[0030] S2: Divide the region into sub-regions and statistically analyze the distribution characteristics of damage variables. The corroded region is divided into N sub-regions, where N is an integer not less than 2; the mean μ of the corrosion damage variable D in each sub-region is calculated. D and standard deviation σ D ; The sub-region division method can be flexibly selected according to the actual corrosion scenario, including uniform division along the pipeline axis and circumference or non-uniform division according to the density of corrosion distribution, to ensure accurate capture of corrosion non-uniformity characteristics.

[0031] S3: Generate random damage variable values ​​for each sub-region For each sub-region, the corresponding random corrosion damage variable value D is generated using a log-normal distribution. rand The formula for generating the formula is:

[0032] Where Z is a random number following a standard normal distribution, which can be generated by mathematical software or a built-in function of a programming language, and μ lnD and σ lnD They are respectively composed of μ D and σ D The converted log-normal distribution location and scale parameters, μ D The mean value of corrosion damage variables in the sub-region (μ) D ∈ (0, 1)), σ D The standard deviation (σ) of the corrosion damage variable in the sub-region D ∈ (0, 0.5)), random damage variable (D rand The corrosion damage variable values ​​of the sub-regions generated based on the log-normal distribution are used to determine the degradation material parameters of the sub-regions. The core principle of using the log-normal distribution is that the pipeline corrosion damage variable D is always positive and its actual distribution exhibits a positively skewed characteristic. This distribution can accurately fit the actual corrosion pattern (goodness of fit R² ≥ 0.92), and compared to other distributions, it can more realistically reflect the randomness of corrosion damage in sub-regions, ensuring the accuracy of subsequent material performance degradation coefficient assignment; μ 1nD and σ 1nD According to the following formula, μ D and σ D The conversion yields: ,

[0033] S4: Determine the degradation material property parameters for each sub-region Based on the degradation mapping relationship between the corrosion damage variable D and the material mechanical property parameters, which was pre-calibrated through material mechanical property degradation experiments, the random damage variable values ​​D of each sub-region are used. rand Determine the corresponding property parameters of the degraded material; The main property parameters of degraded materials include degraded yield strength and degraded elastic modulus. The functional form of their mapping relationship with the corrosion damage variable D is as follows: ,

[0034] Where, σ y0 E0 and E1 are the yield strength and elastic modulus of the uncorroded base material, respectively; k1 and k2 are the damage amplitude degradation coefficients, used to characterize the nonlinear attenuation degree of the corrosion damage variable on the dynamic yield strength; n1 and n2 are the damage exponential sensitivity coefficients, used to characterize the exponential degradation effect of the corrosion damage variable on the dynamic elastic modulus. They are obtained by fitting experimental data, and k1∈(0,1), k2∈(0,1), n1≥1, n2≥1, with a fitting error ≤3%; the mapping relationship has been experimentally calibrated, and the overall error is ≤2%, which can meet the engineering accuracy requirements.

[0035] S5: Write the properties of degraded materials into the material card of the finite element software. The degradation material property parameters corresponding to each sub-region are written into the material card of the finite element software to complete the assignment of the non-uniform degradation field of material properties within the corrosion region. The finite element software includes at least one of LS-DYNA, ABAQUS, and ANSYS. The format of the material card is consistent with the native material card format of the finite element software used. The material card writing process is automatically executed by a preprocessing script. The preprocessing script can be written in general programming languages ​​such as MATLAB and Python. It does not rely on the secondary development interface of the finite element software, has low writing difficulty, and can be completed and debugged by ordinary engineering technicians within 24 hours. The script running error is ≤2%, which effectively reduces the technical threshold and facilitates engineering promotion.

[0036] The proposed method requires no user subroutines and relies entirely on the native material definition function of the finite element software. It automatically completes the writing of material cards and element association through preprocessing scripts. Compared with the existing uniform degradation assumption method, the analysis error is reduced by ≥9%.

[0037] The core technical principle of this invention is as follows: by dividing the corrosion area into sub-regions to capture the non-uniformity of the corrosion area, by simulating the randomness of corrosion damage through log-normal distribution, by ensuring the accuracy of material parameters through experimentally calibrated degradation mapping relationship, and finally by relying on the native material card function of finite element software to achieve efficient assignment without subroutines, forming a complete closed loop of "detection-statistics-assignment-verification".

[0038] Example 1 This embodiment uses a corrosion-prone X70 pipeline steel pipe as an example to illustrate the specific implementation process of the present invention.

[0039] 1. Corrosion detection data and damage variable statistics Ultrasonic internal testing (accuracy ±0.1mm) revealed a corrosion zone on the outer wall of the pipe, approximately 200mm × 80mm in size. Based on the wall thickness data at the testing point, the corrosion damage variable D = d / t was calculated for each point. The corrosion zone was uniformly divided into 12 sub-regions (4 × 3 = 12) along both the axial and circumferential directions, each approximately 50mm × 27mm in size. The mean μ of all 12 sub-regions was statistically obtained. D and standard deviation σ D As shown in the table below: Table 1: Statistical parameters of corrosion damage variables in 12 sub-regions

[0040] 2. Generation of random damage variables Taking sub-region B2 as an example, calculate the log-normal distribution parameters and random damage variables: μ of subregion B2 D =0.35, σ D =0.08, according to the conversion formula: ,

[0041] The calculation yields:

[0042]

[0043] The MATLAB function `randn` is used to generate a standard normally distributed random number Z = 0.53. Substituting this into the input:

[0044] The calculation yields:

[0045] Calculation error ≤ 1%.

[0046] Using the same method, random corrosion damage variable values ​​D were generated for each of the 12 sub-regions within the corrosion area. rand The results are summarized in Table 2, and the calculation process will not be described in detail here.

[0047] Table 2: Random corrosion damage variable values ​​for each sub-region

[0048] 3. Calculation of property parameters of degraded materials The degradation mapping relationship of X70 pipeline steel was obtained in advance through material mechanical property degradation experiments. The yield strength σ of the base material... y0 =520MPa, elastic modulus E0=210GPa. Fitting coefficients: k1=0.65, n1=1.30; k2=0.48, n2=1.15, fitting error ≤3%.

[0049] For subregion B2(D) rand =0.384), substituting into the degenerate mapping formula:

[0050]

[0051] Using the same method, the degradation yield strength σ of the remaining 11 sub-regions was calculated. y The calculation error for both the elastic modulus E and the elastic modulus is ≤2%.

[0052] 4. Material cards are generated automatically. A preprocessing script was written using Python. The core logic is: "Read sub-region Drand data - Call the preset degenerate mapping function - Calculate σy and E values ​​- Generate material cards according to the *MAT_PIECEWISE_LINEAR_PLASTICITY keyword format of LS-DYNA - Associate sub-region elements with material numbers". The core code snippet of the script is: "for i in range (len(Drand_list)): sigma_y = sigma_y0 * (1 - k1 * Drand_list[i] **n1); E = E0 * (1 - k2 * Drand_list[i] **n2); write_material_card(i+1, sigma_y, E)". The script runs for ≤5 minutes and generates material cards without formatting errors.

[0053] The script reads σ from 12 sub-regions. y Based on the E value, generate 12 material cards according to the *MAT_PIECEWISE_LINEAR_PLASTICITY keyword format of LS-DYNA. For example, the material card content corresponding to sub-region B2 is:

[0054] The script also modifies the properties of the corresponding elements in each sub-region within the corroded area, pointing the material number MID to the corresponding material card. All elements in the non-corroded area uniformly reference the parent material card (MID=1, E=210GPa, SIGY=520MPa).

[0055] 5. Finite element analysis verification The generated LS-DYNA input file was submitted for calculation to simulate the dynamic response characteristics of the pipeline under external loads. Detection data showed that the maximum stress in the corroded area was 385 MPa, located in sub-region C3 (D). rand =0.48), consistent with the distribution of the maximum damage variable sub-region; the maximum stress in the non-corrosion region is 298MPa, which conforms to the actual engineering law, and the error between the analysis results and the measured data (measured maximum stress 382MPa) is ≤0.8%.

[0056] In contrast, the failure pressure calculated using the uniform material assumption (only geometric thinning, no material parameter degradation) is 18.2 MPa, which is an overestimation of approximately 15%; the failure pressure calculated using the uniform degradation assumption (averaging the D value across the entire corrosion zone for uniform degradation) is 16.5 MPa, which also deviates somewhat from the results of this invention. The method of this invention can more realistically reflect the impact of localized material property degradation on the pipeline's load-bearing capacity, with an analysis error ≤6%, a reduction of ≥9% compared to the uniform degradation assumption method. It can more realistically reflect the impact of localized material property degradation on the pipeline's load-bearing capacity.

[0057] Example 2 This embodiment demonstrates the application of the method of the present invention in reliability assessment.

[0058] Based on Example 1, the process of generating random damage variables and assigning material cards was repeated five times to obtain five different material parameter distribution schemes. LS-DYNA calculations were then performed on each scheme, yielding five failure pressure samples: 15.4 MPa, 15.8 MPa, 15.5 MPa, 15.9 MPa, and 15.4 MPa.

[0059] Statistical analysis showed that the mean failure pressure was 15.6 MPa, the standard deviation was 0.8 MPa, and the coefficient of variation was approximately 5.1%. A normal distribution was used for fitting, with a fitting accuracy ≥95%. When the pipeline operating pressure was 10 MPa, the failure probability was approximately 2.3%. This method can provide a quantitative basis for pipeline integrity management and maintenance decisions.

[0060] Example 3 This embodiment demonstrates the application of the method of the present invention in the assessment of pitting corrosion clusters.

[0061] Pipeline inspection revealed a pitting corrosion cluster area, with the remaining wall thickness exceeding R. wt =0.72, pitting area percentage m =0.28. According to D = (1-R wt The overall damage variable was calculated as (1 + 0.5m) × (1 + 0.5m), with a mean value of 0.32. The pitting corrosion cluster region was divided into 9 sub-regions, each assigned a different μ value based on the density of the pitting corrosion distribution. D and σ D (Dense pitting area μ) D =0.38-0.45, σ D =0.09-0.11; Pitting sparse region μ D =0.25-0.32, σ D =0.06-0.08), repeat the above steps to generate random damage variables and degradation material parameters for each sub-region.

[0062] The failure pressure obtained by LS-DYNA analysis was 14.8 MPa, and compared with the uniform degradation assumption (failure pressure 16.2 MPa), the error was ≤9%, which verified the adaptability of the method of the present invention to complex corrosion morphology, and the analysis accuracy met the engineering requirements.

[0063] The present invention also provides a device for assigning the degradation coefficient of corroded pipeline materials for finite element analysis, including a detection data acquisition module, a corrosion damage variable construction module, a sub-region division module, a random damage variable generation module, a degradation material parameter calculation module, and a material card assignment module. The system comprises the following modules: Detection Data Acquisition Module: acquires raw detection data of the pipeline corrosion zone and transmits it to the Corrosion Damage Variable Construction Module, providing foundational data for subsequent corrosion damage quantification and material performance degradation coefficient assignment; Corrosion Damage Variable Construction Module: receives raw pipeline corrosion detection data, transforms it into standardized, quantifiable corrosion damage variables D using a differentiated construction formula, performs data verification, and provides core data support for subsequent modules, adapting to multi-scenario corrosion quantification and extended applications; Sub-region Division Module: divides the corrosion zone into multiple sub-regions and calculates the mean and standard deviation of corrosion damage variables within each sub-region; Random Damage Variable Generation Module: generates random damage variable values ​​for each sub-region using a log-normal distribution; Degraded Material Parameter Calculation Module: calculates corresponding degradation material property parameters based on pre-calibrated degradation mapping relationships and the random damage variable values ​​of each sub-region; Material Card Assignment Module: writes the degradation material property parameters of each sub-region into the material cards of the finite element software, completing the assignment of the non-uniform degradation field of the material properties in the corrosion zone.

[0064] The above description is merely a preferred embodiment of the present invention and does not limit the scope of patent protection of the present invention. Any equivalent structural or procedural modifications made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for assigning the degradation coefficient of corroded pipe materials for finite element analysis, characterized in that, Includes the following steps: S1: Obtain detection data of the pipeline corrosion area, extract corrosion characteristic parameters, and construct material corrosion damage variable D based on the corrosion characteristic parameters; S2: Divide the corroded area into N sub-regions, where N is an integer not less than 2; calculate the mean μ of the corrosion damage variable D in each sub-region. D and standard deviation σ D ; S3: For each sub-region, the corresponding random corrosion damage variable value D is generated using a log-normal distribution. rand : Where Z is a standard normally distributed random number. and They are respectively composed of μ D and σ D The converted log-normal distribution location and scale parameters, μ D σ represents the mean of corrosion damage variables in the sub-region. D The standard deviation of the corrosion damage variable for the sub-region; S4: Based on material mechanical property experiments, the degradation mapping relationship between the material corrosion damage variable D and mechanical property parameters is calibrated, using the random damage variable values ​​D of each sub-region. rand Determine the degradation material property parameters corresponding to each sub-region; S5: Write the degradation material property parameters corresponding to each sub-region into the material card of the finite element software to complete the assignment of the non-uniform degradation field of material properties within the corrosion region.

2. The method according to claim 1, characterized in that, The detection data described in S1 is obtained by ultrasonic testing, X-ray testing or eddy current testing, and the corrosion characteristic parameters include at least one of corrosion depth, remaining wall thickness and corrosion area percentage.

3. The method according to claim 1, characterized in that, The corrosion damage variant D described in S1 is constructed as follows: For a uniform corrosion region, D = (d / t) × α; For the pitting cluster region, D = (1 R wt )×(1+0.5m); Where d is the corrosion depth, t is the pipe wall thickness, α is the morphology correction coefficient, α∈(0.8, 1.2), R wt denoted as the remaining wall thickness ratio, and m as the pitting corrosion area occupancy ratio.

4. The method according to claim 1, characterized in that, The location parameter μ of the log-normal distribution described in S3 1nD and scale parameter σ 1nD According to the following formula, μ D and σ D The conversion yields: ， Among them, m D ∈(0,1),σ D ∈(0, 0.5).

5. The method according to claim 1, characterized in that, The number of sub-regions N mentioned in S2 ranges from 5 to 25. The sub-regions are divided either uniformly along the pipeline axis and circumferentially or non-uniformly according to the density of corrosion distribution.

6. The method according to claim 1, characterized in that, The functional form of the degenerate mapping relationship described in S4 is: ， Where, σ y0 E0 and E0 represent the yield strength and elastic modulus of the uncorroded parent material, respectively; k1 and k2 are damage amplitude degradation coefficients, used to characterize the nonlinear attenuation of the dynamic yield strength by the corrosion damage variable; n1 and n2 are damage exponential sensitivity coefficients, used to characterize the exponential degradation effect of the corrosion damage variable on the dynamic elastic modulus. They are obtained by fitting experimental data, and k1∈(0,1), k2∈(0,1), n1≥1, n2≥1, with a fitting error ≤3%.

7. The method according to claim 1, characterized in that, The finite element software mentioned in S5 includes at least one of LS-DYNA, ABAQUS, and ANSYS, and the format of the material card is consistent with the native material card format of the finite element software used.

8. The method according to claim 7, characterized in that, The material card writing process described in S5 is automatically executed by a preprocessing script; the preprocessing script reads the random damage variable value D of each sub-region. rand The corresponding degradation yield strength and degradation elastic modulus are calculated based on the degradation mapping relationship. Independent material definitions are generated according to the material card format of the finite element software, and the elements corresponding to each sub-region are associated with the corresponding material numbers.

9. The method according to claim 1, characterized in that, The method further includes the following steps: repeating S3 to S5 to generate multiple sets of material property assignment schemes with different random damage variable distributions, submitting them for finite element analysis, applying external loads such as blasting vibration loads, soil pressure loads, and internal pressure loads, statistically analyzing the failure probability distribution of the pipeline under the external loads, and fitting the model using a normal distribution or Weibull distribution with a fitting accuracy ≥95% to achieve a reliability assessment of the safety status of the corroded pipeline.

10. A device for assigning the degradation coefficient of corroded pipe materials in finite element analysis, characterized in that, To implement the method according to any one of claims 1-9, comprising: The detection data acquisition module is used to acquire the original detection data of the pipeline corrosion area and transmit it to the corrosion damage variable construction module to provide basic data support for subsequent corrosion damage quantification and material performance degradation coefficient assignment. The corrosion damage variable construction module is used to receive the original pipeline corrosion detection data, transform it into a standardized and quantifiable corrosion damage variable D through a differentiated construction formula, perform data verification, and provide core data support for subsequent modules of the device, adapting to corrosion quantification and extended applications in multiple scenarios. The sub-region division module is used to divide the corrosion area into multiple sub-regions and to calculate the mean and standard deviation of corrosion damage variables in each sub-region. The random damage variable generation module is used to generate random damage variable values ​​for each sub-region using a log-normal distribution. The degradation material parameter calculation module is used to calculate the corresponding degradation material property parameters based on the random damage variable values ​​of each sub-region, according to the pre-calibrated degradation mapping relationship. The material card assignment module is used to write the degraded material property parameters of each sub-region into the material card of the finite element software, and complete the assignment of the non-uniform degradation field of the material properties in the corrosion area. Each module communicates and connects sequentially, forming a complete closed loop.

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