HR3C weld joint residual life prediction method and system under multi-working-condition coupling

Abstract

The invention discloses a method and system for predicting the residual life of an HR3C welding seam under multi-working-condition coupling, and relates to the technical field of welding seam life management.The method comprises the steps that a working condition monitoring module is connected, working condition time sequence parameters and welding seam physical parameters of the HR3C welding seam are obtained, accumulated stress analysis is conducted on each partition structure, and stress response distribution of each partition is obtained; and based on the stress response distribution, calculating a damage accumulation index of each partition, and according to the damage accumulation index, carrying out residual life prediction under multiple working condition parameters to obtain weld joint residual life prediction data. According to the method, the technical problems that the actual damage accumulation and the service life state of the welding seam under the complex working condition cannot be accurately reflected by the existing HR3C welding seam service life prediction, and the prediction result and the actual condition have a large deviation are solved, and the purposes of multi-working-condition coupling analysis and partition structure damage evaluation are achieved. And the accuracy and reliability of HR3C weld joint residual life prediction under complex working conditions are improved.

Description

Technical Field

[0001] This invention relates to the field of weld life management technology, specifically to a method and system for predicting the remaining life of HR3C welds under multiple operating conditions. Background Technology

[0002] Traditional weld life prediction methods typically consider only the welding process or static working conditions under a single operating condition. This approach fails to effectively account for the complex behavior of welds under multiple operating conditions. In actual operation, welds are often subjected to the interaction of multiple factors such as high temperature, pressure, mechanical load, and corrosion, and the damage manifestations of different regions (such as the weld metal zone, heat-affected zone, and adjacent base metal zone) vary significantly. Existing technical methods often neglect these complex factors, resulting in low accuracy in life prediction under varying operating conditions. Summary of the Invention

[0003] This application provides a method and system for predicting the remaining life of HR3C welds under multiple working conditions, which is used to solve the technical problem that the existing HR3C weld life prediction is based on a single working condition analysis, which cannot accurately reflect the actual damage accumulation and life status of the weld under complex working conditions, and the prediction results have a large deviation from the actual situation.

[0004] The first aspect of this application provides a method for predicting the remaining life of HR3C welds under multiple coupled operating conditions. The method includes: connecting to an operating condition monitoring module, interacting with HR3C weld operating condition records, and establishing operating condition time series parameters; acquiring HR3C weld physical parameters, including physical partition structure and parameters of each partition structure, wherein the physical partition structure includes weld metal zone, heat-affected zone, and adjacent base material zone; performing cumulative stress analysis on the physical partition structure according to the operating condition time series parameters and the corresponding partition structure parameters to obtain the stress response distribution of each partition; calculating the cumulative damage index of each partition based on the stress response distribution of each partition; and predicting the remaining life under preset multiple operating condition parameters based on the cumulative damage index of each partition and a preset fusion weight to obtain weld remaining life prediction data under multiple operating conditions.

[0005] A second aspect of this application provides a system for predicting the remaining life of HR3C welds under multiple operating conditions. The system includes: a working condition timing parameter acquisition module, which connects to a working condition monitoring module, interacts with HR3C weld working condition records, and establishes working condition timing parameters; a weld physical parameter acquisition module, which acquires HR3C weld physical parameters, including physical partition structures and parameters of each partition structure, wherein the physical partition structure includes a weld metal zone, a heat-affected zone, and an adjacent base metal zone; a cumulative stress analysis module, which performs cumulative stress analysis on the physical partition structure according to the working condition timing parameters and the corresponding partition structure parameters to obtain the stress response distribution of each partition; a damage accumulation index calculation module, which calculates the damage accumulation index of each partition based on the stress response distribution of each partition; and a remaining life prediction module, which predicts the remaining life under preset multiple operating conditions based on the damage accumulation index of each partition and a preset fusion weight, to obtain weld remaining life prediction data under multiple operating conditions.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] This application provides a method and system for predicting the remaining life of HR3C welds under multiple coupled operating conditions, which relates to the field of weld life management technology. It acquires the operating condition time-series parameters and physical partition data of HR3C welds through an operating condition monitoring module, obtains the stress response distribution of each partition based on cumulative stress analysis, calculates the damage accumulation index, and combines preset fusion weights to predict the remaining life under multiple operating conditions. This provides accurate weld remaining life data and solves the technical problem that existing HR3C weld life prediction methods, based on single operating condition analysis, cannot accurately reflect the actual damage accumulation and life status of welds under complex operating conditions, resulting in significant deviations between prediction results and actual conditions. It achieves the technical effect of improving the accuracy and reliability of HR3C weld remaining life prediction under complex operating conditions through multi-condition coupled analysis and partitioned structural damage assessment. Attached Figure Description

[0008] 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.

[0009] Figure 1 A schematic diagram of the HR3C weld remaining life prediction method under multi-condition coupling provided in the embodiments of this application;

[0010] Figure 2 This is a schematic diagram of the HR3C weld remaining life prediction system under multi-condition coupling provided in the embodiments of this application.

[0011] Figure labeling: Module 11 for acquiring working condition time sequence parameters, Module 12 for acquiring weld physical parameters, Module 13 for cumulative stress analysis, Module 14 for calculating cumulative damage index, and Module 15 for predicting remaining life. Detailed Implementation

[0012] This application provides a method and system for predicting the remaining life of HR3C welds under multiple working conditions, which is used to solve the technical problem that the existing HR3C weld life prediction is based on a single working condition analysis, which cannot accurately reflect the actual damage accumulation and life status of the weld under complex working conditions, and the prediction results have a large deviation from the actual situation.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, this application provides a method for predicting the remaining life of HR3C welds under multi-condition coupling, the method comprising:

[0016] P10: Connect to the working condition monitoring module, interact with HR3C weld working condition records, and establish working condition timing parameters.

[0017] Furthermore, step P10 in this embodiment of the application also includes:

[0018] P11: Extract the operating parameters and operating time of the HR3C weld, including temperature, pressure, mechanical load, vibration, and corrosion factor; P12: Establish a multi-dimensional time series matrix of operating conditions using the operating time as an index, where each moment corresponds to a set of operating condition parameters, reflecting the operating parameters in the weld working time series process; P13: Determine the operating condition time series parameters based on the multi-dimensional time series matrix of operating conditions.

[0019] It should be understood that by connecting to the operating condition monitoring module and exchanging the operating condition records of the HR3C weld, operating condition time series parameters are established. This step provides key input data for the accurate prediction of the weld's remaining life, comprehensively reflecting the dynamic characteristics of the weld during actual operation, thus laying the foundation for the accuracy and reliability of the entire prediction method.

[0020] First, the operating condition monitoring module interacts with the operating condition records of the HR3C weld to extract the weld's operating parameters and operating time in real time. Operating parameters are key indicators reflecting the various external loads and environmental factors the weld experiences during actual operation, mainly including temperature, pressure, mechanical load, vibration, and corrosion factors. Accurate acquisition of these parameters is crucial because they directly determine the weld's stress state and damage accumulation rate. For example, temperature changes affect the material's mechanical properties and coefficient of thermal expansion, thus altering the weld's stress distribution; pressure directly acts on the weld's stress-bearing area and is one of the main factors influencing the weld's stress state; mechanical load reflects the external forces the weld experiences during actual use and is a significant factor leading to weld fatigue damage; vibration exacerbates weld fatigue damage, especially under high-frequency vibration; and corrosion factors affect weld integrity from a chemical perspective, particularly in humid or corrosive environments. By monitoring these parameters, the stress and environmental conditions of the weld under actual operating conditions can be comprehensively captured, providing rich data support for subsequent analysis.

[0021] Next, a multi-dimensional time-series matrix of working conditions is established using the operation time as an index. The operation parameter data obtained in the above steps are not simple static data, but dynamic data that changes continuously over time. To effectively manage and process this data, a multi-dimensional time-series matrix of working conditions can be established using the operation time as an index. This matrix is ​​a data structure used to store and organize the operation parameters of the weld at different time points. Each moment corresponds to a set of working condition parameters, which reflect the real-time working status of the weld during the working sequence. Specifically, the working condition monitoring module collects the operation parameters of the weld at each time point and records the corresponding timestamp. The accuracy of the timestamp should be set according to the actual working condition requirements, such as second-level, minute-level, or hour-level. The collected data is organized into a multi-dimensional matrix in chronological order, with each row of the matrix corresponding to a time point and each column corresponding to an operation parameter. For example, the first column of the matrix can be temperature data, the second column can be pressure data, and so on. During the matrix construction process, the collected data is verified and cleaned to ensure the accuracy and integrity of the data. For example, abnormal data points are removed, and missing data is filled in. This time-indexed multidimensional time series matrix allows for the decomposition of the weld operation process into a series of discrete time points, recording parameters such as temperature, pressure, mechanical load, vibration, and corrosion factor at each time point. This data structure is not only clear and ordered but also facilitates subsequent data analysis and processing.

[0022] Finally, based on the constructed multi-dimensional time-series matrix of operating conditions, the operating condition time-series parameters are determined. These parameters are a quantitative description of the changes in various operational parameters over time during weld operation, reflecting the dynamic characteristics of the weld under different operating conditions. For example, representative time-series features, such as the rate of change, fluctuation range, and cumulative time of parameters, can be extracted from the multi-dimensional time-series matrix. These time-series parameters not only contain the instantaneous state of the weld at each time point but also reflect its trends and patterns over long-term operation. Through the analysis and processing of these features, the operating condition time-series parameters can be obtained, providing crucial foundational data for subsequent stress analysis and life prediction.

[0023] Through the above steps, the system can establish a detailed and accurate weld condition time-series database. The establishment of the condition time-series matrix is ​​not merely a data storage process, but also the foundation for subsequent analysis, providing necessary condition parameters for stress analysis, damage calculation, and life prediction. In practical applications, this data can be used to monitor the dynamic changes of the weld, promptly detect any anomalies that may occur during the welding process, identify potential risks in advance, and ensure the safety and reliability of the equipment.

[0024] P20: Obtain the physical parameters of the HR3C weld, including the physical partition structure and the parameters of each partition. The physical partition structure includes the weld metal zone, the heat-affected zone, and the adjacent base metal zone. The parameters of each partition structure include: thermal conductivity, coefficient of thermal expansion, yield strength, grain size, welding heat input parameters, and creep performance indicators of the HR3C material.

[0025] Specifically, to predict the remaining life of HR3C welds under multi-condition coupling, it is necessary to obtain the physical parameters of the HR3C weld, including the physical partitioning structure of the weld and the specific structural parameters of each partition. The physical partitioning of the weld is usually divided into the weld metal zone, the heat-affected zone, and the adjacent base metal zone. These physical parameters are the basis for subsequent stress analysis and life prediction, and their accuracy is directly related to the reliability of the prediction results.

[0026] Specifically, the physical zoning structure of HR3C welds mainly includes the weld metal zone, the heat-affected zone (HAZ), and the adjacent base metal zone. The weld metal zone refers to the area formed by the solidification of molten metal during welding; its physical and mechanical properties differ from those of the base metal. The HAZ refers to the area affected by heat during welding but not melted; its properties change due to thermal cycling. The adjacent base metal zone refers to the original base metal area that is not affected by welding heat. These three zones exhibit different mechanical behaviors and damage characteristics during welding and use, therefore requiring separate analysis.

[0027] The structural parameters for each zone include thermal conductivity, coefficient of thermal expansion, yield strength, grain size, welding heat input parameters, and creep performance indices of HR3C material. Thermal conductivity and coefficient of thermal expansion are important physical parameters of materials during the thermal process, affecting the distribution of thermal stress in the weld. Yield strength is the critical stress at which a material undergoes plastic deformation under stress, and it is crucial for assessing the load-bearing capacity and damage accumulation of the weld. Grain size affects the mechanical properties and corrosion resistance of the material. Welding heat input parameters reflect the amount of energy input during welding, directly impacting the performance of the weld metal zone and heat-affected zone. The creep performance indices of HR3C material describe the deformation behavior of the material under long-term stress and are a key factor in assessing the remaining life of the weld.

[0028] In practice, obtaining these physical parameters requires a combination of experimental testing and theoretical analysis. For example, grain size can be determined through metallographic analysis, yield strength can be measured through tensile testing, thermal conductivity and coefficient of thermal expansion can be measured using thermal analysis instruments, and creep performance indicators can be obtained through creep testing. Meanwhile, welding heat input parameters can be calculated based on welding process parameters. Obtaining these physical parameters requires precise experimental equipment and specialized testing methods to ensure the accuracy and reliability of the data.

[0029] By acquiring the structural parameters of each of the aforementioned partitions and combining them with operating condition data for multi-condition simulation analysis, the performance of the weld under actual working conditions can be accurately simulated. This provides detailed data support for subsequent stress response analysis, damage accumulation calculation, and remaining life prediction. This process ensures that the behavior of each physical region of the HR3C weld is fully considered, avoiding the limitations of traditional single-condition analysis methods and improving the accuracy and reliability of weld life prediction.

[0030] P30: Based on the operating condition timing parameters, perform cumulative stress analysis on the physical partition structure according to the partition structure parameters corresponding to each partition to obtain the stress response distribution of each partition.

[0031] Furthermore, step P30 in this embodiment of the application also includes:

[0032] P31: Based on the regional boundary partitioning method, establish a finite element model of the weld structure including the weld metal zone, heat-affected zone, and adjacent base material zone; P32: Input the working condition time series parameters as time-stepped load terms into the finite element model of the weld structure; P33: Through the finite element model of the weld structure, perform coupled thermal-mechanical-corrosion field simulation under coupled working conditions on each physical partition structure to obtain the stress response distribution of each partition under multiple time series working conditions; P34: Perform time integration processing on the stress response distribution of each partition, calculate the cumulative stress response data of each partition, and obtain the stress response distribution of each partition.

[0033] Optionally, based on the aforementioned operating condition time series parameters, cumulative stress analysis is performed on the physical partition structure of the weld to obtain the stress response distribution of each partition. The purpose of this step is to simulate the stress conditions of the weld under different operating conditions, providing necessary data support for subsequent damage assessment and life prediction.

[0034] First, a finite element model of the weld structure, including the weld metal zone, heat-affected zone, and adjacent base metal zone, is established based on a zoned meshing method using region boundaries. In this step, a three-dimensional finite element model of the weld is first established using the finite element method (FEM) according to the physical zoned structure of the weld (weld metal zone, heat-affected zone, and adjacent base metal zone). To ensure the accuracy of the model and the simulation precision, a zoned meshing method based on region boundaries is adopted, meshing each physical region of the weld. This meshing process divides the weld region into multiple small elements. Through detailed analysis of these small elements, the physical behavior and stress distribution of each region during the welding process can be accurately simulated.

[0035] Next, the aforementioned time-series parameters of the operating conditions (such as temperature, pressure, mechanical load, vibration, corrosion factor, etc.) are input into the finite element model of the weld structure as time-step load terms. Each time step corresponds to a specific operating condition. The model will perform progressive numerical calculations based on the changes in the operating conditions at different time points. In other words, by inputting these parameters as time-step load terms into the model, the dynamic changes in operating conditions experienced by the weld during actual operation are simulated, thereby accurately reflecting the stress response of the weld under different operating conditions in the finite element model.

[0036] Specifically, coupled-field simulation is a multiphysics analysis method that can simultaneously consider the interactions of multiple physical fields such as heat, force, and corrosion. In actual working conditions, welds are not only subjected to temperature, pressure, and mechanical loads, but may also be affected by corrosive environments. In this step, a coupled-condition analysis method is used to perform coupled thermo-mechanical-corrosion field simulations on each physical zone. For example, a finite element model of the weld structure is used for coupled thermo-mechanical-corrosion field simulation. By simulating coupled heat conduction, mechanical deformation, and corrosion reaction processes, the stress response distribution of each zone under multiple working conditions is obtained. During the simulation process, reasonable boundary conditions and initial conditions need to be set according to the actual working conditions and material properties of the weld to ensure the accuracy of the simulation results. This allows for precise simulation of the various mechanical and environmental influences experienced by the weld in the actual working environment, providing detailed stress data for subsequent damage analysis.

[0037] Finally, time integration is performed on the stress response distribution of each zone to calculate the cumulative stress response data for each zone, thus obtaining the stress response distribution of each zone. Time integration is a mathematical method used to accumulate instantaneous stress responses into cumulative stress responses; that is, the stress responses of each zone are gradually summed to obtain the cumulative stress response data for each zone. Through time integration, the instantaneous stress responses of the weld at different time points can be accumulated, thereby obtaining the cumulative stress state of the weld during long-term operation. This accumulated data can accurately describe the stress accumulation situation experienced by the weld in different time periods, thus providing data support for the calculation of damage models.

[0038] This process provides crucial data support for subsequent calculations of cumulative damage indicators and predictions of remaining life, ensuring the accuracy and reliability of the entire prediction method.

[0039] Furthermore, step P33 in this embodiment of the application also includes:

[0040] P33-1: In the process of performing coupled simulation of thermo-mechanical-corrosion field, cross-partition node sharing, boundary continuity constraints or contact surface interaction are adopted to simulate the heat flow, stress and displacement transfer between the weld metal area, heat-affected zone and adjacent base material area, determine the physical spatial continuity of each partition, and obtain the stress response distribution of each partition.

[0041] In one possible embodiment of this application, the process of thermo-mechanical-corrosion field coupling simulation can be further refined to ensure that the heat flow, stress and displacement transfer between the weld metal zone, the heat-affected zone and the adjacent base material zone can be accurately simulated during the thermo-mechanical-corrosion field coupling simulation, thereby determining the continuity of the physical space of each zone and obtaining the stress response distribution of each zone.

[0042] When performing coupled thermo-mechanical-corrosion field simulations, the physical spatial continuity between the weld metal region, the heat-affected zone (HAZ), and the adjacent base metal region must first be considered. To this end, a cross-zone node sharing approach is adopted, sharing nodes at the boundaries of adjacent zones to ensure the continuous transfer of heat flow, stress, and displacement at the zone boundaries. This method effectively avoids numerical errors caused by discontinuous mesh generation, thus ensuring the accuracy of the simulation results. For example, at the boundary between the weld metal region and the HAZ, sharing nodes ensures a smooth transfer of heat flow from the weld metal region to the HAZ, while stress and displacement also remain continuous at the boundary.

[0043] Furthermore, to further ensure the continuity of physical quantities at the boundaries, boundary continuity constraints are applied. These constraints include thermal boundary conditions (such as heat flux or temperature gradient), stress boundary conditions (such as stress equilibrium conditions), and displacement boundary conditions (such as displacement continuity conditions). By applying these constraints, it can be ensured that the changes in physical quantities are smooth at the boundaries between zones, thereby avoiding numerical discontinuities caused by boundary effects. For example, at the boundary between the heat-affected zone and the adjacent base material zone, applying thermal boundary conditions can ensure the continuous transfer of heat flow, while applying stress and displacement boundary conditions can ensure the continuity of mechanical behavior.

[0044] Simultaneously, reasonable contact surface interactions are established for the contact surfaces between the weld metal zone, heat-affected zone, and adjacent base metal zone. These interactions include thermal contact resistance and frictional contact conditions. Thermal contact resistance is used to simulate the heat transfer efficiency at the contact surface, while frictional contact conditions are used to simulate the interaction of stress and displacement at the contact surface. By establishing these contact surface interactions, the physical behavior of the weld under actual working conditions can be simulated more accurately, especially situations such as contact separation or contact sliding that may occur during welding and use.

[0045] By setting up cross-zone node sharing, boundary continuity constraints, and contact surface interaction relationships, the heat flow, stress, and displacement transfer between the weld metal zone, heat-affected zone, and adjacent base material zone can be effectively simulated during the coupled thermo-mechanical-corrosion field simulation. This not only ensures the continuity of the physical space of each zone but also allows for the acquisition of stress response distributions in each zone under multi-condition coupling, providing reliable data support for subsequent damage accumulation calculations and remaining life predictions.

[0046] P40: Based on the stress response distribution of each partition, calculate the cumulative damage index of each partition.

[0047] Furthermore, step P40 in this embodiment of the application also includes:

[0048] P41: Construct a damage model, which is used to calculate fatigue loss, creep loss, and corrosion fatigue loss; P42: Based on the stress response distribution of each partition, perform damage calculation through the damage model and output the cumulative damage index of each partition.

[0049] It should be understood that, based on the stress response distribution of each zone, the damage accumulation index of each zone is calculated separately. This process quantifies the degree of damage accumulation of the weld under multi-condition coupling by constructing a damage model and using the model to analyze the stress response distribution of each zone, thereby providing accurate data support for subsequent weld life prediction.

[0050] Before calculating the damage accumulation index, it is necessary to first construct a damage model that accurately reflects the damage accumulation characteristics of the weld under multiple coupled working conditions. This damage model is used to calculate fatigue loss, creep loss, and corrosion fatigue loss, which are the three main factors affecting the remaining life of HR3C welds. Fatigue loss is mainly related to the damage accumulation of the weld under cyclic loading; creep loss is related to the deformation and damage of the weld under high temperature and long-term stress conditions; and corrosion fatigue loss considers the influence of the corrosive environment on the fatigue performance of the weld. By comprehensively considering these three damage mechanisms, the damage model can more comprehensively reflect the damage accumulation of the weld under actual working conditions.

[0051] When constructing a damage model, it is necessary to select an appropriate damage theory and calculation method based on the characteristics of the weld material and the actual working conditions. For example, for fatigue loss, Miner's linear cumulative damage theory or the Paris fatigue crack propagation model can be used; for creep loss, the Coffin-Manson creep fatigue damage model or the Norton creep model can be used; for corrosion fatigue loss, a modified fatigue crack propagation model can be used, considering the influence of the corrosive environment on the crack propagation rate. The selection of these models and theories needs to be verified and optimized by combining experimental data and theoretical analysis to ensure the accuracy and reliability of the damage model.

[0052] Next, based on the stress response distribution of each zone, damage calculations are performed using the constructed damage model, outputting the cumulative damage index for each zone. Specifically, the stress response distribution data of each zone needs to be input into the damage model first. This stress response distribution data was obtained in step P30 through thermo-mechanical-corrosion field coupling simulation, encompassing the stress states of the weld metal zone, heat-affected zone, and adjacent base material zone under multi-condition coupling. Then, using the calculation formulas and theories in the damage model, the stress response distribution of each zone is analyzed, and fatigue loss, creep loss, and corrosion fatigue loss are calculated respectively. Finally, these three types of damage losses are combined to obtain the cumulative damage index for each zone.

[0053] When calculating the damage accumulation index, the following points should be noted: First, since the material properties and stress states of different zones of the weld are different, damage calculations need to be performed separately for the weld metal zone, heat-affected zone, and adjacent base metal zone to reflect the actual damage accumulation in each zone; second, during the calculation process, the time dependence of stress response distribution and the multi-condition coupling effect need to be considered to ensure that the damage accumulation index can accurately reflect the damage accumulation law of the weld during actual operation; finally, the calculation results of the damage accumulation index need to be reasonably normalized to facilitate subsequent remaining service life prediction and assessment.

[0054] The cumulative damage index for each physical zone incorporates the effects of multiple damage mechanisms, including fatigue, creep, and corrosion. These cumulative damage indices not only quantitatively represent the damage status of each zone of the weld but also provide a reliable basis for subsequent life prediction.

[0055] Furthermore, in constructing the damage model, step P41 of this embodiment also includes:

[0056] P41-1: Collect multi-condition time-series cumulative sample datasets and build training sets for fatigue loss, creep loss, and corrosion fatigue loss respectively; P41-2: Use the fatigue loss, creep loss, and corrosion fatigue loss training sets respectively to train and converge the damage analysis sub-models to obtain the fatigue cumulative sub-model, creep cumulative sub-model, and corrosion fatigue sub-model; P41-3: Integrate the fatigue cumulative sub-model, creep cumulative sub-model, and corrosion fatigue sub-model to construct the damage model.

[0057] Specifically, the process of constructing the damage model can be further refined. First, by collecting multi-condition time-series cumulative sample datasets, independent training sets are established for fatigue loss, creep loss, and corrosion fatigue loss. These training sets are based on stress response data and damage information under actual working conditions, ensuring that the training data can realistically reflect the performance of the weld under different working conditions. By collecting data from different regions such as the weld metal zone, heat-affected zone, and adjacent base metal zone, sufficient sample data can be provided for each damage mechanism, ensuring that each training set covers a sufficient number of working condition variations. The fatigue loss training set mainly contains stress data of the weld under repeated loading, the creep loss training set contains stress and deformation data of the weld at high temperatures, and the corrosion fatigue damage training set combines data on the interaction between corrosion factors and periodic stress.

[0058] After obtaining training sets for fatigue loss, creep loss, and corrosion fatigue loss, damage analysis sub-models are trained using these sets. By employing appropriate machine learning algorithms or numerical analysis methods, each training set is fitted and optimized to ensure the damage analysis sub-model converges and accurately describes the corresponding damage accumulation patterns. Specifically, the training process requires continuous adjustment of model parameters to minimize the error between model predictions and actual sample data. Finally, fatigue accumulation sub-models, creep accumulation sub-models, and corrosion fatigue sub-models are obtained. These sub-models are used to calculate the degree of damage accumulation in welds under fatigue loads, creep conditions, and corrosion fatigue environments, respectively.

[0059] Finally, the fatigue accumulation sub-model, creep accumulation sub-model, and corrosion fatigue sub-model obtained above are integrated to construct a complete damage model. During the integration process, the interactions and coupling effects between the sub-models need to be considered to ensure that the damage model can comprehensively reflect the overall damage accumulation of the weld under multiple coupled working conditions. For example, weighted summation, multi-model fusion, or other advanced integration methods can be used to integrate the output results of each sub-model. The damage model constructed in this way can not only consider the three damage mechanisms of fatigue, creep, and corrosion fatigue separately, but also reflect their mutual influence under actual working conditions, thus providing a more accurate and reliable quantitative basis for predicting the remaining life of the weld.

[0060] Through the above steps, a damage model that comprehensively reflects the damage accumulation characteristics of HR3C welds under multi-condition coupling was successfully constructed. This model lays a solid foundation for subsequent calculations of damage accumulation indices based on the stress response distribution of each zone, ensuring the scientific validity and practicality of the entire prediction method.

[0061] P50: Based on the cumulative damage index of each zone and the preset fusion weight, the remaining life is predicted under preset multi-condition parameters to obtain the predicted data of the weld remaining life under multiple conditions.

[0062] Furthermore, step P50 in this embodiment of the application also includes:

[0063] P51: Set fusion weight parameters for each physical partition structure, the fusion weight parameters reflecting the failure sensitivity of the partition structure in the overall structural life; P52: Weight and combine the cumulative damage index of each partition with its fusion weight parameters to calculate the equivalent damage index of the weld as a whole; P53: Use the equivalent damage index to perform life comparison and matching under preset multi-condition parameters in the historical life sample library to obtain the weld remaining life prediction data under multi-condition.

[0064] Specifically, the next step is the final stage of the HR3C weld remaining life prediction method under multi-condition coupling. Its purpose is to comprehensively analyze the damage accumulation indicators of each zone and predict the overall remaining life of the weld through preset fusion weights and multi-condition parameters.

[0065] Before predicting the remaining weld life, it is necessary to first set fusion weight parameters for each physical structural zone. These zones include the weld metal zone, the heat-affected zone, and the adjacent base metal zone, each with different failure sensitivities in the overall structural life. The fusion weight parameters reflect the degree of influence of each zone on the overall weld life. For example, the weld metal zone may have a higher failure sensitivity due to directly bearing the welding heat-affected zone and mechanical loads, while the adjacent base metal zone may have a relatively lower sensitivity. By analyzing the material properties, stress state, and damage accumulation characteristics of each zone, combined with experimental data and engineering experience, the fusion weight parameters are reasonably allocated to ensure the accuracy of subsequent calculations.

[0066] After obtaining the cumulative damage index and its corresponding fusion weight parameter for each zone, these indices are weighted and combined with the weight parameter. Specifically, by multiplying the cumulative damage index of each zone by its corresponding fusion weight parameter, and then summing the results for all zones, the equivalent damage index of the weld as a whole is calculated. This equivalent damage index comprehensively reflects the overall damage state of the weld under multi-condition coupling and is a key basis for subsequent remaining life prediction. For example, assuming that the cumulative damage indices of the weld metal zone, heat-affected zone, and adjacent base metal zone are D1, D2, and D3, respectively, and the corresponding fusion weight parameters are W1, W2, and W3, respectively, the equivalent damage index D of the weld as a whole can be expressed as: D = W1 × D1 + W2 × D2 + W3 × D3.

[0067] Finally, the calculated equivalent damage index is used to perform a life comparison and matching under preset multi-condition parameters in the historical life sample library. The historical life sample library contains actual life data of welds under different conditions, which can serve as a reference benchmark. By comparing the equivalent damage index of the current weld with the data in the historical sample library, the sample that best matches it is found, thereby obtaining the predicted remaining life data of the weld under multiple conditions. For example, if the equivalent damage index of the current weld is closest to the damage index of a sample in the historical sample library, and the actual life of that sample is T, then T can be used as the predicted remaining life value of the current weld. This process needs to consider the consistency of multi-condition parameters to ensure the reliability of the prediction results.

[0068] In summary, by setting reasonable fusion weights, weighted combining of damage indicators from each partition, and comparing and matching with a historical life sample database, this application achieves high-precision prediction of weld remaining life. This method not only considers the damage accumulation of the weld under various working conditions but also ensures the accuracy and reliability of the overall life prediction by integrating the damage contributions from different partitions.

[0069] Furthermore, before performing lifetime comparison and matching under preset multi-condition parameters in the historical lifetime sample database using the equivalent damage index, step P50 in this embodiment of the application further includes:

[0070] P53-1a: Set preset multi-condition parameters, which are various condition parameters from the historical records of HR3C welds, including transient impact condition and peak operating parameter condition; P53-2a: Conduct multi-sample weld tests based on the preset multi-condition parameters, wherein the multi-sample welds include multiple equivalent damage index ranges, and fit the life influence coefficient of the preset multi-condition parameters on the life of the multi-sample welds based on the test data of the multi-sample welds; P53-3a: Establish the mapping relationship between the preset multi-condition parameters, equivalent damage index ranges, life influence coefficients, and remaining life, and construct the historical life sample library.

[0071] Specifically, in order to improve the accuracy and reliability of weld remaining life prediction, the construction process of the historical life sample library can be further refined before using equivalent damage indices for life comparison and matching, so as to provide accurate reference data for subsequent life comparison and matching.

[0072] Before conducting lifespan comparison matching, it is necessary to first set preset multi-condition parameters. These parameters are based on the historical records of HR3C welds and cover a variety of actual operating conditions, including transient impact conditions and peak operating parameter conditions. Transient impact conditions mainly reflect the high stress or high strain impacts that the weld experiences in a short period of time, while peak operating parameter conditions focus on the extreme temperature, pressure, or mechanical load conditions that the weld may encounter during long-term operation. By reasonably setting these preset multi-condition parameters, it can be ensured that subsequent tests and sample library construction can cover various complex operating conditions of the weld in actual use.

[0073] Multi-sample weld seam tests were conducted based on preset multi-condition parameters. The purpose of these tests was to obtain weld life data under different operating conditions through experimental methods. The weld seam samples used in the tests should cover multiple equivalent damage index ranges to ensure sample diversity and representativeness. The equivalent damage index ranges can be divided according to the actual usage and damage accumulation characteristics of the weld seam; for example, the damage index can be divided into low, medium, and high ranges. By testing these samples under different operating conditions, the actual life data of each sample under the corresponding operating conditions was obtained. Then, based on the test data of the multi-sample weld seam, the influence coefficients of the preset multi-condition parameters on weld life were fitted. These influence coefficients reflect the quantitative impact of different operating conditions on weld life and are key parameters for subsequently constructing a historical life sample library.

[0074] Finally, a mapping relationship is established between preset multi-condition parameters, equivalent damage index ranges, life influence coefficients, and remaining life, thereby constructing a historical life sample library. Establishing this mapping relationship requires comprehensive consideration of the interactions between multi-condition parameters, equivalent damage indices, and life influence coefficients. For example, for a specific equivalent damage index range, the remaining life of the weld under different preset multi-condition parameters can be determined by combining the corresponding life influence coefficient. The historical life sample library constructed in this way not only contains actual life data of the weld under different operating conditions but also reflects the quantitative impact of multi-condition parameters on weld life, thus providing rich reference information for subsequent life comparison and matching.

[0075] Through the implementation of the above steps, a high-precision historical life sample library was successfully constructed. This library not only contains weld life data under various working conditions but also reflects the specific impact of different working conditions on weld life. By utilizing this sample library and mapping relationships, accurate life comparison and matching can be performed under preset multi-working-condition parameters, thereby providing a scientific basis for predicting the remaining life of welds and ensuring that the life assessment of welds under multi-working-condition conditions has high accuracy and reliability.

[0076] In summary, the embodiments of this application have at least the following technical effects:

[0077] This application employs multi-condition coupled analysis, considering the complex stress, temperature, and corrosion effects on the weld under different operating conditions, enabling more accurate prediction of the weld's remaining life. Simultaneously, by analyzing the stress response and damage accumulation in the weld metal zone, heat-affected zone, and adjacent base metal zone, it ensures an effective assessment of the impact of different regions on the overall weld life. Based on cumulative stress analysis using operating condition time-series parameters and zoned structures, it is possible to predict the weld's remaining life under multiple operating conditions, improving the accuracy of life assessment in complex environments.

[0078] The technology achieves the goal of improving the accuracy and reliability of predicting the remaining life of HR3C welds under complex working conditions through multi-condition coupling analysis and partitioned structural damage assessment.

[0079] Example 2, based on the same inventive concept as the HR3C weld remaining life prediction method under multi-condition coupling in the foregoing examples, such as... Figure 2 As shown, this application provides a system for predicting the remaining life of HR3C welds under multi-condition coupling. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0080] The working condition timing parameter acquisition module 11 is used to connect to the working condition monitoring module, interact with the HR3C weld working condition record, and establish working condition timing parameters.

[0081] The weld physical parameter acquisition module 12 is used to acquire the physical parameters of the HR3C weld, including the physical partition structure and the parameters of each partition structure. The physical partition structure includes the weld metal zone, the heat-affected zone, and the adjacent base metal zone. The parameters of each partition structure include: thermal conductivity, coefficient of thermal expansion, yield strength, grain size, welding heat input parameters, and creep performance indicators of the HR3C material.

[0082] The cumulative stress analysis module 13 is used to perform cumulative stress analysis on the physical partition structure according to the working condition time sequence parameters and the partition structure parameters corresponding to each partition, so as to obtain the stress response distribution of each partition.

[0083] The damage accumulation index calculation module 14 is used to calculate the damage accumulation index of each partition based on the stress response distribution of each partition.

[0084] The remaining life prediction module 15 is used to predict the remaining life under preset multi-condition parameters based on the damage accumulation index of each partition and the preset fusion weight, so as to obtain the weld remaining life prediction data under multiple conditions.

[0085] Furthermore, the operating condition timing parameter acquisition module 11 is also used to perform the following steps:

[0086] The operating parameters and operating time of the HR3C weld are extracted. The operating parameters include temperature, pressure, mechanical load, vibration, and corrosion factor. A multi-dimensional time series matrix of operating conditions is established with the operating time as the index. Each moment corresponds to a set of operating condition state parameters, reflecting the operating parameters in the weld working time series process. Based on the multi-dimensional time series matrix of operating conditions, the operating condition time series parameters are determined.

[0087] Furthermore, the cumulative stress analysis module 13 is also used to perform the following steps:

[0088] A finite element model of the weld structure, comprising the weld metal zone, heat-affected zone, and adjacent base material zone, is established based on a regional boundary-based meshing method. The time-series parameters of the operating conditions are input into the finite element model of the weld structure as time-stepped load terms. Through the finite element model of the weld structure, coupled thermal-mechanical-corrosion field simulations are performed on each physical partition structure under coupled operating conditions to obtain the stress response distribution of each partition under multiple time-series operating conditions. The stress response distribution of each partition is then processed by time integration to calculate the cumulative stress response data of each partition, thus obtaining the stress response distribution of each partition.

[0089] Furthermore, the cumulative stress analysis module 13 is also used to perform the following steps:

[0090] In the process of performing coupled thermal-mechanical-corrosion field simulation, cross-zone node sharing, boundary continuity constraints, or contact surface interaction are adopted to simulate the heat flow, stress, and displacement transfer between the weld metal zone, heat-affected zone, and adjacent base material zone, determine the physical spatial continuity of each zone, and obtain the stress response distribution of each zone.

[0091] Furthermore, the cumulative damage index calculation module 14 is also used to perform the following steps:

[0092] A damage model is constructed to calculate fatigue loss, creep loss, and corrosion fatigue loss. Based on the stress response distribution of each partition, damage is calculated using the damage model, and the cumulative damage index of each partition is output.

[0093] Furthermore, the cumulative damage index calculation module 14 is also used to perform the following steps:

[0094] Collect a multi-condition time-series cumulative sample dataset and build training sets for fatigue loss, creep loss, and corrosion fatigue loss respectively; use the training sets for fatigue loss, creep loss, and corrosion fatigue loss respectively to train and converge the damage analysis sub-models to obtain the fatigue cumulative sub-model, creep cumulative sub-model, and corrosion fatigue sub-model; integrate the fatigue cumulative sub-model, creep cumulative sub-model, and corrosion fatigue sub-model to construct the damage model.

[0095] Furthermore, the remaining lifetime prediction module 15 is also used to perform the following steps:

[0096] A fusion weight parameter is set for each physical partition structure, which reflects the failure sensitivity of the partition structure in the overall structural lifespan; the cumulative damage index of each partition is weighted and combined with its fusion weight parameter to calculate the equivalent damage index of the weld as a whole; the equivalent damage index is used to perform lifespan comparison and matching under preset multi-condition parameters in the historical lifespan sample library to obtain the weld remaining lifespan prediction data under multi-condition.

[0097] Furthermore, the remaining lifetime prediction module 15 is also used to perform the following steps:

[0098] Preset multi-condition parameters are set, which are various condition parameters from the historical records of HR3C welds. The preset multi-condition parameters include transient impact conditions and peak operating parameter conditions. Multi-sample weld tests are conducted based on the preset multi-condition parameters, wherein the multi-sample welds include multiple equivalent damage index ranges. The life influence coefficient of the preset multi-condition parameters on the multi-sample welds is fitted according to the test data of the multi-sample welds. The mapping relationship between the preset multi-condition parameters, equivalent damage index ranges, life influence coefficients, and remaining life is established to construct the historical life sample library.

[0099] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0100] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0101] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for predicting the remaining life of HR3C welds under multi-condition coupling, characterized in that, include: Connect to the working condition monitoring module, interact with HR3C weld working condition records, and establish working condition timing parameters; Obtain the physical parameters of HR3C weld, including the physical partition structure and the structural parameters of each partition. The physical partition structure includes the weld metal zone, the heat-affected zone, and the adjacent base metal zone. Based on the operating condition timing parameters, the physical partition structure is subjected to cumulative stress analysis according to the partition structure parameters corresponding to each partition, and the stress response distribution of each partition is obtained. Based on the stress response distribution of each partition, the cumulative damage index of each partition is calculated. Based on the cumulative damage index of each zone and the preset fusion weight, the remaining life is predicted under preset multi-condition parameters to obtain the predicted data of the weld remaining life under multiple conditions.

2. The method for predicting the remaining life of HR3C welds under multi-condition coupling as described in claim 1, characterized in that, The structural parameters of each partition include: thermal conductivity, coefficient of thermal expansion, yield strength, grain size, welding heat input parameters, and creep performance indicators of HR3C material.

3. The method for predicting the remaining life of HR3C welds under multi-condition coupling as described in claim 1, characterized in that, Interactive HR3C weld condition recording establishes condition timing parameters, including: Extract the operating parameters and operating time of the HR3C weld, including temperature, pressure, mechanical load, vibration, and corrosion factor; A multi-dimensional time series matrix of working conditions is established with the working time as the index. Each moment corresponds to a set of working condition parameters, which reflect the working parameters in the welding working time series process. Based on the multi-dimensional time series matrix of the operating conditions, the time series parameters of the operating conditions are determined.

4. The method for predicting the remaining life of HR3C welds under multi-condition coupling according to claim 3, characterized in that, Based on the structural parameters corresponding to each partition, cumulative stress analysis is performed to obtain the stress response distribution of each partition, including: Based on the regional boundary partitioning method, a finite element model of the weld structure including the weld metal zone, heat-affected zone, and adjacent base material zone is established. The operating condition timing parameters are input as time-stepped load terms into the finite element model of the weld structure; Using the finite element model of the weld structure, a coupled thermal-mechanical-corrosion field simulation under coupled working conditions is performed on each physical partition structure to obtain the stress response distribution of each partition under multiple time-series working conditions. The stress response distribution of each partition is processed by time integration to calculate the cumulative stress response data of each partition, thereby obtaining the stress response distribution of each partition.

5. The method for predicting the remaining life of HR3C welds under multi-condition coupling according to claim 4, characterized in that, Using the finite element model of the weld structure, coupled thermal-mechanical-corrosion field simulations were performed on each physical partition structure under coupled operating conditions to obtain the stress response distribution of each partition under multiple time-series operating conditions, including: In the process of performing coupled thermal-mechanical-corrosion field simulation, cross-zone node sharing, boundary continuity constraints, or contact surface interaction are adopted to simulate the heat flow, stress, and displacement transfer between the weld metal zone, heat-affected zone, and adjacent base material zone, determine the physical spatial continuity of each zone, and obtain the stress response distribution of each zone.

6. The method for predicting the remaining life of HR3C welds under multi-condition coupling according to claim 4, characterized in that, Based on the stress response distribution of each partition, the cumulative damage index of each partition is calculated, including: A damage model is constructed, which is used to calculate fatigue loss, creep loss, and corrosion fatigue loss. Based on the stress response distribution of each partition, damage is calculated using the damage model, and the cumulative damage index of each partition is output.

7. The method for predicting the remaining life of HR3C welds under multi-condition coupling as described in claim 6, characterized in that, Constructing a damage model includes: Collect time-series cumulative sample datasets under multiple working conditions, and build training sets for fatigue loss, creep loss, and corrosion fatigue loss respectively; The damage analysis sub-models were trained and converged using the fatigue loss, creep loss and corrosion fatigue loss training sets respectively, to obtain the fatigue accumulation sub-model, creep accumulation sub-model and corrosion fatigue sub-model. The fatigue accumulation sub-model, creep accumulation sub-model, and corrosion fatigue sub-model are integrated to construct the damage model.

8. The method for predicting the remaining life of HR3C welds under multi-condition coupling as described in claim 1, characterized in that, Based on the cumulative damage index of each zone and the preset fusion weight, the remaining life is predicted under preset multi-condition parameters to obtain the weld remaining life prediction data under multiple conditions, including: A fusion weight parameter is set for each physical partition structure, and the fusion weight parameter reflects the failure sensitivity of the partition structure in the overall structural lifetime. The cumulative damage index of each zone is weighted and combined with its fusion weight parameter to calculate the equivalent damage index of the weld as a whole. The equivalent damage index is used to perform life comparison and matching under preset multi-condition parameters in the historical life sample library to obtain the predicted data of the remaining life of the weld under multiple conditions.

9. The method for predicting the remaining life of HR3C welds under multi-condition coupling according to claim 8, characterized in that, Using the equivalent damage index, life comparison and matching under preset multi-condition parameters is performed in a historical life sample database, which includes the following steps: Set preset multi-condition parameters, which are various condition parameters from the historical records of HR3C welds, including transient impact condition and peak working parameter condition; Multi-sample weld tests are conducted based on the preset multi-condition parameters, wherein the multi-sample welds include multiple equivalent damage index ranges, and the influence coefficient of the preset multi-condition parameters on the life of the multi-sample welds is fitted based on the test data of the multi-sample welds. Establish the mapping relationship between the preset multi-condition parameters, equivalent damage index range, life influence coefficient, and remaining life, and construct the historical life sample library.

10. A multi-condition coupled HR3C weld remaining life prediction system, characterized in that, The system includes: The working condition timing parameter acquisition module is used to connect to the working condition monitoring module, interact with the HR3C weld working condition record, and establish working condition timing parameters. The weld physical parameter acquisition module is used to acquire the physical parameters of HR3C welds, including the physical partition structure and the parameters of each partition structure. The physical partition structure includes the weld metal zone, the heat-affected zone, and the adjacent base metal zone. The cumulative stress analysis module is used to perform cumulative stress analysis on the physical partition structure according to the working condition time sequence parameters and the partition structure parameters corresponding to each partition, so as to obtain the stress response distribution of each partition. The damage accumulation index calculation module is used to calculate the damage accumulation index of each partition based on the stress response distribution of each partition. The remaining life prediction module is used to predict the remaining life of the weld under preset multi-condition parameters based on the damage accumulation index of each zone and the preset fusion weight, so as to obtain the weld remaining life prediction data under multiple conditions.

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