Reactor pressure vessel life prediction method and system based on digital twinning
By using digital twin technology to monitor the dynamic characteristics of the reactor pressure vessel in real time, adjusting model parameters, simulating thermo-mechanical behavior, and calculating damage accumulation, the problem of large life prediction errors in existing technologies has been solved, achieving high-precision life prediction and equipment safety assurance.
Patent Information
- Application Number
- CN202511460117.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-14
- Publication Date
- 2026-01-23
AI Technical Summary
Existing technologies for predicting the lifespan of reactor pressure vessels suffer from inaccurate dynamic feature extraction and deficiencies in model building and damage assessment, resulting in large errors in lifespan prediction results and failing to meet the requirements for high accuracy.
By using a digital twin-based approach, the temperature change rate and stress distribution of the reactor pressure vessel are monitored in real time, dynamic characteristic parameters are extracted, the initial model is adjusted in conjunction with parameter adaptability assessment, the digital twin model is reconstructed, thermo-mechanical behavior is simulated, fatigue and creep damage accumulation is calculated, total damage index is integrated and the remaining life is mapped.
It significantly improves the accuracy of damage assessment and the reliability of life prediction, ensuring the scientific validity of the prediction results and the safety of equipment operation and maintenance.
Smart Images
Figure CN121389445A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of digital twin technology, and in particular to a method and system for predicting the lifespan of a reactor pressure vessel based on digital twins. Background Technology
[0002] In the field of reactor pressure vessel lifetime prediction, existing technologies generally suffer from inaccurate dynamic feature extraction. Traditional methods struggle to efficiently separate and process temperature time-series data and strain spatial data from real-time equipment monitoring data, failing to accurately obtain the two core dynamic characteristic parameters: temperature change rate and stress distribution. They often rely on static parameters or offline analysis results to replace real-time dynamic data. This leads to discrepancies between the underlying data used for lifetime prediction and the actual operating state of the equipment, resulting in insufficient accuracy of model input and directly impacting the quality of initial data for lifetime prediction.
[0003] Meanwhile, existing technologies have significant shortcomings in model building and damage assessment. On the one hand, the verification of parameter compatibility between the initial model and actual equipment lacks scientific dynamic threshold and confidence interval analysis methods. The adjustment of model physical parameters relies heavily on empirical judgment, making it difficult to achieve iterative optimization. This results in a gap between the constructed prediction model and the actual thermo-mechanical behavior of the equipment. On the other hand, the damage accumulation calculation does not fully consider the coupling effect and dynamic weight allocation of fatigue damage and creep damage. The integration of total damage indicators lacks reasonable linear superposition and normalization processing, and the mapping relationship between total damage indicators and remaining life lacks an accurate correlation model. Ultimately, this leads to large errors in life prediction results, failing to meet the high-precision life prediction requirements for safe operation and maintenance of reactor pressure vessels. Summary of the Invention
[0004] This invention provides a method and system for predicting the lifespan of a reactor pressure vessel based on digital twins, in order to solve the problems mentioned in the background art.
[0005] To achieve the above objectives, the present invention provides a reactor pressure vessel lifetime prediction method based on digital twins, comprising:
[0006] S1. Extract the temperature change rate and stress distribution from the real-time monitoring data of the reactor pressure vessel to obtain the dynamic characteristic parameters of the reactor pressure vessel.
[0007] S2. Based on the dynamic characteristic parameters, the baseline output in the initial model is verified for consistency to obtain the parameter adaptability evaluation results of the reactor pressure vessel.
[0008] S3. Adjust the values of the physical parameters in the initial model according to the parameter adaptability evaluation results, and reconstruct the initial model based on the adjusted physical parameters to obtain the digital twin model of the reactor pressure vessel.
[0009] S4. Simulate the thermo-mechanical behavior of the reactor pressure vessel under the current operating conditions based on the digital twin model to obtain the cumulative damage record of the reactor pressure vessel;
[0010] S5. Based on the container fatigue damage accumulation and creep damage accumulation in the damage accumulation record, the reactor pressure vessel is damaged and integrated to obtain the total damage index of the reactor pressure vessel.
[0011] S6. Map the total damage index to the remaining life of the reactor pressure vessel.
[0012] In a preferred embodiment, the extraction of temperature change rate and stress distribution from the real-time monitoring data of the reactor pressure vessel to obtain the dynamic characteristic parameters of the reactor pressure vessel includes:
[0013] Temperature time series data and strain space data were separated from real-time monitoring data of the reactor pressure vessel;
[0014] The temperature time series data is differentially processed to obtain the temperature change rate of the reactor pressure vessel;
[0015] The stress distribution of the reactor pressure vessel is obtained by interpolating the strain space data.
[0016] The temperature change rate and the stress distribution are combined to form the dynamic characteristic parameters of the reactor pressure vessel.
[0017] In a preferred embodiment, the step of verifying the consistency of the baseline output in the initial model based on the dynamic characteristic parameters to obtain the parameter adaptability evaluation result of the reactor pressure vessel includes:
[0018] Based on the dynamic feature parameters and the baseline output in the initial model, the statistical characteristics of the residuals in the time series are used to determine the confidence interval for parameter fitting in the initial model.
[0019] A dynamic threshold is established based on the historical operating data of the reactor pressure vessel, and the residual is compared with the dynamic threshold.
[0020] Based on the comparison results of the confidence interval and the dynamic threshold, the parameter adaptability evaluation results of the reactor pressure vessel are generated.
[0021] In a preferred embodiment, adjusting the values of the physical parameters in the initial model based on the parameter adaptability evaluation results, and reconstructing the initial model based on the adjusted physical parameters to obtain a digital twin model of the reactor pressure vessel, includes:
[0022] Based on the parameter adaptability evaluation results, the key physical parameters that need to be adjusted in the initial model are identified, and the parameter adjustment priority sequence of the initial model is obtained.
[0023] The priority sequence is adjusted according to the parameters, and the values of the key physical parameters are iteratively corrected to obtain an optimized set of physical parameters.
[0024] The optimized set of physical parameters is injected into the structural framework of the initial model;
[0025] The simulation accuracy of the reconfigured model is verified, and the verified reconfigured model is output as a digital twin model of the reactor pressure vessel.
[0026] In a preferred embodiment, the step of adjusting the priority sequence according to the parameters and iteratively correcting the values of the key physical parameters to obtain an optimized set of physical parameters includes:
[0027] The highest priority key physical parameter is selected from the parameter adjustment priority sequence to obtain the current adjustment parameters of the initial model;
[0028] The current adjustment parameter is incrementally adjusted to generate the adjusted parameter value;
[0029] The adjusted parameter values are substituted into the initial model for simulation testing to obtain the performance evaluation of the initial model parameters.
[0030] When all key physical parameters meet the convergence condition, the adjusted parameter values are integrated to obtain the optimized set of physical parameters.
[0031] In a preferred embodiment, the step of simulating the thermo-mechanical behavior of the reactor pressure vessel under current operating conditions based on the digital twin model to obtain the fatigue damage accumulation and creep damage accumulation of the reactor pressure vessel includes:
[0032] The temperature field and stress field under the current operating conditions are thermo-mechanically coupled using the digital twin model to obtain the thermo-mechanical response data of the reactor pressure vessel;
[0033] The thermal gradient distribution and stress fluctuation characteristics at key locations are extracted from the thermo-mechanical response data to obtain the damage-sensitive parameters of the reactor pressure vessel.
[0034] Based on the damage-sensitive parameters, the cumulative fatigue damage and cumulative creep damage of the reactor pressure vessel within a preset evaluation period are calculated respectively.
[0035] The fatigue damage accumulation and the creep damage accumulation are integrated over time to obtain the damage accumulation record of the reactor pressure vessel.
[0036] In a preferred embodiment, the formula for calculating the cumulative fatigue damage is as follows:
[0037] ;
[0038] In the formula, Due to the accumulation of fatigue damage, The upper limit of time, To obtain the material fatigue index, To obtain the expected material fatigue strength, Time in the damage sensitivity parameter Stress amplitude at the location, As a time factor, For the temperature sensitivity coefficient to be obtained, Time in the damage sensitivity parameter The temperature at that location For reference temperature, For time differentiation, It is a natural exponential function.
[0039] The formula for calculating the accumulation of creep damage is as follows:
[0040] ;
[0041] In the formula, For the accumulation of creep damage, The upper limit of time, Time in the damage sensitivity parameter Equivalent stress at the point, To obtain the material creep strength, For the pre-obtained stress index, For the pre-acquired activation energy, For the gas constant to be obtained, Time in the damage sensitivity parameter The temperature at that location For time differentiation, It is a natural exponential function.
[0042] In a preferred embodiment, the step of integrating the damage of the reactor pressure vessel based on the vessel fatigue damage accumulation and the creep damage accumulation in the damage accumulation record to obtain the total damage index of the reactor pressure vessel includes:
[0043] Based on the operating condition data of the reactor pressure vessel, the damage mechanism weights of the fatigue damage accumulation and the creep damage accumulation are determined, and a dynamic weight allocation scheme for the reactor pressure vessel is obtained.
[0044] According to the dynamic weight allocation scheme, the fatigue damage accumulation and the creep damage accumulation are weighted to obtain the weighted fatigue damage value and the weighted creep damage value.
[0045] The total damage index of the reactor pressure vessel is obtained by linearly superimposing the weighted fatigue damage value and the weighted creep damage value.
[0046] In a preferred embodiment, the step of linearly superimposing the weighted fatigue damage value and the weighted creep damage value to obtain the total damage index of the reactor pressure vessel includes:
[0047] The weighted fatigue damage value and the weighted creep damage value are interactively corrected based on the damage coupling relationship between the weighted fatigue damage value and the weighted creep damage value to obtain the fatigue damage value and creep damage value of the reactor pressure vessel.
[0048] The fatigue damage value and the creep damage value are weighted and fused to obtain the initial total damage index of the reactor pressure vessel.
[0049] The initial total damage index is normalized to obtain the total damage index of the reactor pressure vessel.
[0050] To address the above problems, the present invention also provides a reactor pressure vessel lifetime prediction system based on digital twins, the system comprising:
[0051] The feature acquisition module is used to extract the temperature change rate and stress distribution from the real-time monitoring data of the reactor pressure vessel to obtain the dynamic feature parameters of the reactor pressure vessel.
[0052] The model parameter adjustment module is used to perform consistency verification on the baseline output in the initial model based on the dynamic feature parameters, and obtain the parameter adaptability evaluation result of the reactor pressure vessel.
[0053] The model parameter configuration module is used to adjust the values of physical parameters in the initial model according to the parameter adaptability evaluation results, and to reconstruct the initial model based on the adjusted physical parameters to obtain a digital twin model of the reactor pressure vessel.
[0054] The thermal simulation module is used to simulate the thermo-mechanical behavior of the reactor pressure vessel under current operating conditions based on the digital twin model, and obtain the cumulative damage record of the reactor pressure vessel.
[0055] The loss determination module is used to perform damage integration on the reactor pressure vessel based on the container fatigue damage accumulation and creep damage accumulation in the damage accumulation record, and obtain the total damage index of the reactor pressure vessel.
[0056] The remaining lifetime generation module is used to map the total damage index to the remaining lifetime of the reactor pressure vessel.
[0057] Compared with the prior art, the present invention has the following beneficial effects:
[0058] 1. This invention precisely extracts the dynamic characteristic parameters of the reactor pressure vessel, uses these parameters to verify the consistency of the initial model's output, and iteratively adjusts the key physical parameters of the initial model based on the evaluation results. The resulting reconstructed digital twin model highly matches the actual operating state of the equipment. This digital twin model can efficiently simulate the thermo-mechanical behavior of the equipment under current operating conditions, accurately capture the thermal gradient distribution and stress fluctuation characteristics at key locations, and generate accurate damage accumulation records. This provides high-quality data support for subsequent life prediction and significantly improves the accuracy of the damage assessment process.
[0059] 2. In the damage integration process, this invention formulates a dynamic weight allocation scheme based on equipment operating condition data, and simultaneously considers the coupling relationship between fatigue damage and creep damage for interactive correction. The total damage index obtained through normalization more accurately reflects the overall damage level of the equipment. This total damage index is precisely mapped to the remaining life of the equipment, forming a complete and accurate closed loop in the entire prediction process. This not only significantly improves the efficiency of reactor pressure vessel life prediction but also ensures the reliability of the remaining life results, providing a scientific and effective reference for equipment operation and maintenance decisions, and further guaranteeing the long-term safety of equipment operation. Attached Figure Description
[0060] Figure 1 A schematic flowchart of a reactor pressure vessel lifetime prediction method based on digital twins is provided in an embodiment of the present invention;
[0061] Figure 2 A functional block diagram of a reactor pressure vessel lifetime prediction system based on digital twins is provided in an embodiment of the present invention.
[0062] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0063] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
[0064] This application provides a method for predicting the lifetime of a reactor pressure vessel based on digital twins. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for predicting the lifetime of a reactor pressure vessel based on digital twins can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cluster of cloud servers. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.
[0065] Reference Figure 1 The diagram shown is a flowchart illustrating a reactor pressure vessel lifetime prediction method based on digital twins according to an embodiment of the present invention. In this embodiment, the reactor pressure vessel lifetime prediction method based on digital twins includes:
[0066] S1. Extract the temperature change rate and stress distribution from the real-time monitoring data of the reactor pressure vessel to obtain the dynamic characteristic parameters of the reactor pressure vessel.
[0067] In this embodiment of the invention, the extraction of temperature change rate and stress distribution from real-time monitoring data of the reactor pressure vessel to obtain dynamic characteristic parameters of the reactor pressure vessel includes:
[0068] Temperature time series data and strain space data were separated from real-time monitoring data of the reactor pressure vessel;
[0069] The temperature time series data is differentially processed to obtain the temperature change rate of the reactor pressure vessel;
[0070] The stress distribution of the reactor pressure vessel is obtained by interpolating the strain space data.
[0071] The temperature change rate and the stress distribution are combined to form the dynamic characteristic parameters of the reactor pressure vessel.
[0072] Specifically, when extracting data from the real-time monitoring data of the reactor pressure vessel, the characteristics of temperature time series data are first defined as temperature values of each monitoring point of the reactor pressure vessel recorded sequentially over time, and the characteristics of strain space data are strain values recorded at different spatial locations of the corresponding reactor pressure vessel. Then, each record in the real-time monitoring data is identified one by one. If the record contains time information and temperature value, it is classified as temperature time series data, and if the record contains spatial location information and strain value, it is classified as strain space data, thereby completing the separation of the two types of data.
[0073] Furthermore, when performing differential processing on the obtained temperature time series data, the temperature values corresponding to two adjacent time points are selected in chronological order. The temperature value of the previous time point is subtracted from the temperature value of the later time point to obtain the temperature difference within the adjacent time period. This temperature difference is then divided by the time length between the two time points, and the result is the temperature change rate of the reactor pressure vessel within the adjacent time period. The temperature values of all adjacent time points in the temperature time series data are processed in this way to obtain the complete temperature change rate of the reactor pressure vessel.
[0074] Furthermore, when processing strain space data to obtain stress distribution, first determine all spatial locations of the reactor pressure vessel where stress distribution needs to be analyzed. Check the spatial locations corresponding to the existing strain values in the strain space data. For spatial locations that need to be analyzed but whose strain values are not directly recorded, calculate the strain value at that location based on the strain conditions of the surrounding spatial locations where strain values have been recorded. The closer the distance, the greater the weight of the strain values of the surrounding locations. After the strain values of all spatial locations that need to be analyzed are determined, convert the strain value of each spatial location into the corresponding stress value according to the fixed correspondence between strain and stress. Arrange the stress values of all spatial locations according to their corresponding spatial locations to form the stress distribution of the reactor pressure vessel.
[0075] Furthermore, when combining the previously obtained temperature change rate and stress distribution of the reactor pressure vessel, all the data of the temperature change rate and all the data of the stress distribution are integrated into the same dataset to ensure that the time information of the temperature change rate data and the spatial location information of the stress distribution data are clearly correlated. This integrated dataset is the dynamic characteristic parameter of the reactor pressure vessel.
[0076] In summary, this provides core input data that closely reflects the real-time operating status of the equipment for the consistency verification of the initial model benchmark output, avoiding verification bias caused by relying on static or offline data, ensuring that the residual statistical analysis and dynamic threshold comparison in parameter adaptability evaluation have a reliable basis, and laying the foundation for accurately identifying the key physical parameters that need to be adjusted in the initial model.
[0077] In summary, the obtained dynamic characteristic parameters are directly related to the core influencing factors of the equipment's thermo-mechanical behavior. Their accuracy can ensure the rationality of the direction of subsequent iteration and correction of key physical parameters, and help to reconstruct a digital twin model that closely matches the actual state of the equipment. This provides high-quality data support for subsequent simulation of the equipment's thermo-mechanical behavior and accurate calculation of damage accumulation, thereby improving the accuracy and reliability of the entire life prediction process from the source.
[0078] S2. Based on the dynamic characteristic parameters, the baseline output in the initial model is verified for consistency to obtain the parameter adaptability evaluation results of the reactor pressure vessel.
[0079] In this embodiment of the invention, the step of verifying the consistency of the baseline output in the initial model based on the dynamic feature parameters to obtain the parameter adaptability evaluation result of the reactor pressure vessel includes:
[0080] Based on the dynamic feature parameters and the baseline output in the initial model, the statistical characteristics of the residuals in the time series are used to determine the confidence interval for parameter fitting in the initial model.
[0081] A dynamic threshold is established based on the historical operating data of the reactor pressure vessel, and the residual is compared with the dynamic threshold.
[0082] Based on the comparison results of the confidence interval and the dynamic threshold, the parameter adaptability evaluation results of the reactor pressure vessel are generated.
[0083] Specifically, the residuals at each time point in the time series are first calculated, which is the actual value of the dynamic characteristic parameters of the reactor pressure vessel at that time point minus the baseline output of the corresponding time point in the initial model. This yields all the residuals in the time series. Then, the statistical characteristics of these residuals are analyzed, specifically the concentration distribution and fluctuation range of the residuals. Based on the concentration distribution of the residuals, the range in which most residuals are located is determined. Combined with the fluctuation range, a range that can contain the model parameters corresponding to reasonable residuals is defined. This range is the confidence interval for parameter fitting in the initial model.
[0084] Furthermore, historical operating data of the reactor pressure vessel is collected, and historical data related to residuals in different operating time periods are extracted, including dynamic characteristic parameters of each historical time period and the baseline output of the corresponding initial model. The residuals of each historical time period are calculated, and the pattern of these historical residuals changing with operating time is analyzed, such as the maximum and minimum reasonable values of historical residuals in different operating stages. Based on this pattern of change, a corresponding reasonable residual limit is set for each time point of the current operation. This limit is the dynamic threshold. Then, each residual in the current time series is compared with the dynamic threshold of the corresponding time point to determine whether the residual is within the range of the dynamic threshold.
[0085] Furthermore, first check whether the parameters of the initial model are within the previously determined confidence interval for parameter fit, and record the check result. Then check the comparison result between the residuals and the dynamic threshold, i.e., whether the residuals are within the dynamic threshold range, and record the comparison result. Then combine the two results for analysis. If the initial model parameters are within the confidence interval and the residuals are within the dynamic threshold range, the parameter fit is judged to be good. If the initial model parameters are not within the confidence interval or the residuals exceed the dynamic threshold range, the parameter fit is judged to be poor. The final judgment result is the parameter fit evaluation result of the reactor pressure vessel.
[0086] In summary, it is based on dynamic characteristic parameters that fit the real-time operating status of the equipment, combined with the residual statistical characteristics of the initial model benchmark output to determine the parameter fit confidence interval, and establishes dynamic thresholds for comparison by associating with the historical operating data of the equipment. This ensures that the evaluation results have both statistical rigor and correlation with equipment operation, avoids the bias of a single judgment logic, and ensures that the judgment of the fit of the initial model parameters is scientific and accurate.
[0087] In summary, the evaluation results can directly identify the key physical parameters that need to be adjusted in the initial model, providing a clear basis for the formulation of subsequent parameter adjustment priority sequences, avoiding the blindness of physical parameter adjustments, and significantly improving the efficiency and directional accuracy of subsequent parameter iterative corrections.
[0088] In summary, as the core link connecting dynamic feature parameters and model reconstruction, accurate parameter adaptability assessment can ensure that subsequent adjustments to the physical parameters of the model always revolve around the actual state of the equipment, laying a key foundation for building a digital twin model that closely matches the real equipment, thereby supporting the reliability of subsequent thermo-mechanical behavior simulation and life prediction.
[0089] S3. Adjust the values of the physical parameters in the initial model according to the parameter adaptability evaluation results, and reconstruct the initial model based on the adjusted physical parameters to obtain the digital twin model of the reactor pressure vessel.
[0090] In this embodiment of the invention, adjusting the values of the physical parameters in the initial model based on the parameter adaptability evaluation results, and reconstructing the initial model based on the adjusted physical parameters to obtain a digital twin model of the reactor pressure vessel includes:
[0091] Based on the parameter adaptability evaluation results, the key physical parameters that need to be adjusted in the initial model are identified, and the parameter adjustment priority sequence of the initial model is obtained.
[0092] The priority sequence is adjusted according to the parameters, and the values of the key physical parameters are iteratively corrected to obtain an optimized set of physical parameters.
[0093] The optimized set of physical parameters is injected into the structural framework of the initial model;
[0094] The simulation accuracy of the reconfigured model is verified, and the verified reconfigured model is output as a digital twin model of the reactor pressure vessel.
[0095] In this embodiment of the invention, the step of adjusting the priority sequence according to the parameters and iteratively correcting the values of the key physical parameters to obtain an optimized set of physical parameters includes:
[0096] The highest priority key physical parameter is selected from the parameter adjustment priority sequence to obtain the current adjustment parameters of the initial model;
[0097] The current adjustment parameter is incrementally adjusted to generate the adjusted parameter value;
[0098] The adjusted parameter values are substituted into the initial model for simulation testing to obtain the performance evaluation of the initial model parameters.
[0099] When all key physical parameters meet the convergence condition, the adjusted parameter values are integrated to obtain the optimized set of physical parameters.
[0100] Specifically, based on the parameter fit assessment results, we first examine the items marked with poor fit in the assessment results, and then match these items with the physical parameters in the initial model to determine which unreasonable physical parameters will directly cause these poor fit problems. These physical parameters are the key physical parameters that need to be adjusted. Next, we analyze the degree of influence of each key physical parameter on the poor fit problem. The key physical parameters with the greater influence are ranked higher. All key physical parameters are sorted in this way, and the resulting sequence is the parameter adjustment priority sequence of the initial model.
[0101] Furthermore, following the priority sequence of parameter adjustments, the highest priority key physical parameter is selected first. Based on the specific manifestation of poor adaptability caused by this parameter, the value of the parameter is adjusted in a direction that can improve adaptability. After adjustment, it is checked whether the model adaptability corresponding to the parameter has improved. If not, the parameter value is adjusted in the same direction or by a reasonable amount until the adaptability is improved after the parameter is adjusted. Then, the next priority key physical parameter is selected and the above adjustment process is repeated. After all key physical parameters are adjusted to meet the adaptability requirements, these adjusted key physical parameters are collected, and the resulting set is the optimized physical parameter set.
[0102] Furthermore, locate the modules or regions in the initial model's structural framework used to set each key physical parameter, extract the specific value of each key physical parameter from the optimized physical parameter set, and fill each value into the corresponding setting position in the initial model's structural framework, replacing the original parameter value. This ensures that all optimized physical parameters are accurately filled into their corresponding setting positions, thus completing the operation of injecting the optimized physical parameter set into the initial model's structural framework.
[0103] Furthermore, recent actual operating data of the reactor pressure vessel is obtained, and the input conditions of the reconfigured model are set to the input conditions corresponding to the actual operating data. The reconfigured model is run to obtain simulation data, and the simulation data is compared with the corresponding actual operating data point by point. The differences between the two are calculated. If all differences are within the preset allowable range, the simulation accuracy of the reconfigured model is deemed to have passed the verification. If there are differences that exceed the allowable range, the key physical parameters are readjusted and the above process is repeated until the simulation accuracy is verified. The verified reconfigured model is then output as a digital twin model of the reactor pressure vessel.
[0104] Specifically, from the parameter adjustment priority sequence, observe the order of the sequence. The key physical parameter that is ranked first in the sequence is the key physical parameter with the highest priority. Directly select this key physical parameter and determine it as the current adjustment parameter of the initial model.
[0105] Furthermore, first determine a fixed small adjustment amount, analyze the specific direction that caused the poor fit of the initial model before the current parameter adjustment. If the current parameter value is too small and causes fit problems, then increase the set small adjustment amount based on the existing value of the current parameter; if the current parameter value is too large and causes fit problems, then decrease the set small adjustment amount based on the existing value. The value obtained through this operation is the adjusted parameter value.
[0106] Furthermore, the adjusted parameter values replace the original parameter values in the initial model corresponding to the currently adjusted parameters. Then, the initial model is set with input conditions that are completely consistent with a certain actual operating period of the reactor pressure vessel. The initial model is run to generate simulation results for that period. The simulation results are compared point by point with the actual operating data of the reactor pressure vessel for that period. It is determined whether the difference between the simulation results and the actual operating data has narrowed. If the difference narrows, the parameter performance is determined to be improved. If the difference widens, the parameter performance is determined to be degraded. The result formed based on this judgment is the parameter performance evaluation of the initial model.
[0107] Furthermore, the convergence condition is first defined as follows: after a key physical parameter undergoes an incremental adjustment, the difference between the initial model simulation result and the actual operating data of the reactor pressure vessel is no longer changing or the change is less than a preset small value compared to the previous adjustment. Each key physical parameter is checked sequentially according to the parameter adjustment priority sequence. After confirming that all key physical parameters meet the above convergence condition, the final adjusted parameter values of each key physical parameter are collected. These values are then summarized together, and the resulting set is the optimized set of physical parameters.
[0108] In summary, by accurately identifying the key physical parameters that need adjustment and determining the priority sequence through evaluation results, we can avoid blindly adjusting physical parameters, clarify the direction of optimization, significantly improve the efficiency of parameter iterative correction, and ensure that resources are concentrated on optimizing the core parameters that affect model accuracy.
[0109] In summary, iteratively correcting key physical parameters based on a priority sequence can gradually reduce the deviation between the parameters and the actual state of the equipment. The resulting optimized parameter set is more in line with the actual operating characteristics of the equipment. After injecting it into the initial model framework and verifying the accuracy through simulation, it can ensure that the final digital twin model is highly consistent with the actual state of the reactor pressure vessel.
[0110] In summary, the constructed digital twin model provides a reliable platform for simulating the thermo-mechanical behavior of equipment, accurately extracting damage-sensitive parameters, and calculating damage accumulation records. It ensures the data accuracy and process reliability of subsequent life prediction from the root of the model, laying a solid foundation for the final accurate mapping of remaining life.
[0111] S4. Simulate the thermo-mechanical behavior of the reactor pressure vessel under the current operating conditions based on the digital twin model to obtain the cumulative damage record of the reactor pressure vessel;
[0112] In this embodiment of the invention, the step of simulating the thermo-mechanical behavior of the reactor pressure vessel under current operating conditions based on the digital twin model to obtain the fatigue damage accumulation and creep damage accumulation of the reactor pressure vessel includes:
[0113] The temperature field and stress field under the current operating conditions are thermo-mechanically coupled using the digital twin model to obtain the thermo-mechanical response data of the reactor pressure vessel;
[0114] The thermal gradient distribution and stress fluctuation characteristics at key locations are extracted from the thermo-mechanical response data to obtain the damage-sensitive parameters of the reactor pressure vessel.
[0115] Based on the damage-sensitive parameters, the cumulative fatigue damage and cumulative creep damage of the reactor pressure vessel within a preset evaluation period are calculated respectively.
[0116] The fatigue damage accumulation and the creep damage accumulation are integrated over time to obtain the damage accumulation record of the reactor pressure vessel.
[0117] In this embodiment of the invention, the formula for calculating the cumulative fatigue damage is as follows:
[0118] ;
[0119] In the formula, Due to the accumulation of fatigue damage, The upper limit of time, To obtain the material fatigue index, To obtain the expected material fatigue strength, Time in the damage sensitivity parameter Stress amplitude at the location, As a time factor, For the temperature sensitivity coefficient to be obtained, Time in the damage sensitivity parameter The temperature at that location For reference temperature, For time differentiation, It is a natural exponential function.
[0120] The formula for calculating the accumulation of creep damage is as follows:
[0121] ;
[0122] In the formula, For the accumulation of creep damage, The upper limit of time, Time in the damage sensitivity parameter Equivalent stress at the point, To obtain the material creep strength, For the pre-obtained stress index, For the pre-acquired activation energy, For the gas constant to be obtained, Time in the damage sensitivity parameter The temperature at that location For time differentiation, It is a natural exponential function.
[0123] Specifically, the current operating conditions of the reactor pressure vessel are first obtained, including medium pressure and flow rate. These current operating conditions are accurately input into the digital twin model. The digital twin model first calculates the temperature field distribution of each part of the reactor pressure vessel under the current operating conditions. Then, based on the influence of temperature differences in different parts of the temperature field distribution on material deformation, the stress field distribution corresponding to each part is calculated. The temperature field distribution data and the stress field distribution data are correlated and fused so that the influence of temperature change on stress change is fully reflected in the fused data. The resulting fused data is the thermo-mechanical response data of the reactor pressure vessel.
[0124] Furthermore, the critical locations of the reactor pressure vessel are first identified, including vulnerable areas such as welded areas and flange connection areas. Temperature data for these critical locations are selected from the thermo-mechanical response data. The temperature difference between adjacent points at each critical location is calculated, and these temperature differences are arranged in spatial order to form the thermal gradient distribution of the critical locations. Simultaneously, stress data for the critical locations is selected from the thermo-mechanical response data. The stress data changes over time, and the maximum and minimum stress values, as well as the number of times the stress changes from maximum to minimum and back to maximum, are recorded. The stress fluctuation characteristics of the critical locations are summarized. The integrated thermal gradient distribution and stress fluctuation characteristics of the critical locations are combined to obtain the damage-sensitive parameters of the reactor pressure vessel.
[0125] Furthermore, a predetermined assessment period is defined, which can be a fixed time period such as one month or one quarter. Based on the stress fluctuation characteristics in the damage-sensitive parameters, the number of times the stress completes the cycle from the initial value to the peak value and back to the initial value within the predetermined assessment period is counted. According to the degree of damage caused to the reactor pressure vessel material by each stress cycle, the degree of damage caused by all stress cycles is added together to obtain the cumulative fatigue damage within the predetermined assessment period. At the same time, based on the thermal gradient distribution in the damage-sensitive parameters, the continuous temperature level of key locations within the predetermined assessment period is determined. According to the degree of damage caused by the slow deformation of the material under stress at this temperature level, the degree of creep damage generated in each time period is added together over time to obtain the cumulative creep damage within the predetermined assessment period.
[0126] Furthermore, the time intervals of the time series are determined. The time intervals can be set to fixed intervals such as daily or hourly. At each time interval node, the accumulated fatigue damage value and creep damage value accumulated before that node time are recorded. Each time interval node is matched one-to-one with the corresponding accumulated fatigue damage value and creep damage value. These correspondences are arranged in the order of the time interval nodes to form a complete data list containing the accumulated fatigue damage value and creep damage value of the time nodes. This data list is the damage accumulation record of the reactor pressure vessel.
[0127] Specifically, The cumulative fatigue damage is calculated using this formula. It is the upper limit of the preset evaluation period, obtained from the preset evaluation period settings; It is the pre-obtained material fatigue index, which is obtained from the performance manual or material test data of the materials used in the reactor pressure vessel; It is the pre-obtained material fatigue strength, obtained from the performance manual or material test data of the materials used in the reactor pressure vessel; Time is a damage-sensitive parameter. The stress amplitude at the point was obtained directly from the damage-sensitive parameters of the reactor pressure vessel extracted previously. It is a time factor, representing the various time points involved in the calculation process, which changes with the time within the preset evaluation period; It is the pre-acquired temperature sensitivity coefficient, obtained from the thermal fatigue performance test data of the materials used in the reactor pressure vessel; Time is a damage-sensitive parameter. The temperature at that location was obtained directly from the damage-sensitive parameters of the reactor pressure vessel previously extracted; This is a reference temperature, set according to the design standards of the reactor pressure vessel or the typical temperature during normal operation. It is a time derivative, which is set as a tiny time interval within a preset evaluation period. It is used to divide the entire evaluation period into multiple consecutive tiny time intervals for cumulative calculation.
[0128] Furthermore, the fatigue damage accumulation formula is used to calculate the fatigue damage accumulation of the reactor pressure vessel within a preset evaluation period. By dividing the time within the entire evaluation period, the ratio of the stress amplitude to the material fatigue strength in each small time interval is calculated by exponentially using the material fatigue index. Then, the damage contribution is adjusted by the natural exponential function in combination with the temperature difference between the temperature and the reference temperature in the small time interval. Finally, the damage contributions in all small time intervals are summed to obtain the fatigue damage accumulation within the entire preset evaluation period.
[0129] Specifically, The cumulative creep damage is calculated using this formula. It is the upper limit of the preset evaluation period, obtained from the preset evaluation period settings; Time is a damage-sensitive parameter. The equivalent stress at the point is obtained directly from the damage-sensitive parameters of the reactor pressure vessel extracted previously; It is the pre-obtained material creep strength, obtained from the high-temperature creep performance test data or material property handbook of the materials used in the reactor pressure vessel; It is a pre-acquired stress index, obtained from the creep characteristic test data of the materials used in the reactor pressure vessel; It is the pre-acquired activation energy, obtained from thermal activation creep test data of the materials used in the reactor pressure vessel; These are pre-obtained gas constants, directly obtained from a general table of physical constants; Time is a damage-sensitive parameter. The temperature at that location was obtained directly from the damage-sensitive parameters of the reactor pressure vessel previously extracted; It is a time derivative, which is set as a tiny time interval within a preset evaluation period. It is used to divide the entire evaluation period into multiple consecutive tiny time intervals for cumulative calculation.
[0130] Furthermore, the creep damage accumulation formula is used to calculate the creep damage accumulation of the reactor pressure vessel within a preset evaluation period. By dividing the time within the entire evaluation period, the ratio of equivalent stress to material creep strength in each small time interval is calculated by exponentially using the stress exponent. Then, the damage contribution is adjusted by the natural exponential function in combination with the temperature in that small time interval. The natural exponential part reflects the influence of temperature on creep damage through the relationship between activation energy, gas constant and temperature. Finally, the damage contributions in all small time intervals are accumulated to obtain the creep damage accumulation within the entire preset evaluation period.
[0131] In summary, because digital twin models are formed after parameter adaptation and reconstruction, they can closely match the actual operating state of equipment. Their simulation of thermo-mechanical behavior can accurately realize the thermo-mechanical coupling of temperature field and stress field, generate thermo-mechanical response data that fits reality, and avoid the simulation results from deviating from the actual state of equipment.
[0132] In summary, extracting damage-sensitive parameters such as thermal gradient distribution and stress fluctuation characteristics from key locations from response data can ensure that the parameters are directly related to the core influencing factors of equipment damage, providing accurate input for subsequent fatigue and creep damage accumulation calculations and avoiding inaccurate damage assessments due to parameter deviations.
[0133] In summary, by calculating the cumulative fatigue and creep damage within a preset period and integrating them into a time series record, the development process of the two main damage types of equipment can be captured comprehensively and continuously. This avoids the one-sidedness of single damage assessment and provides a complete and reliable data foundation for subsequent damage integration and total damage index calculation, directly supporting the accuracy of subsequent remaining life prediction.
[0134] S5. Based on the container fatigue damage accumulation and creep damage accumulation in the damage accumulation record, the reactor pressure vessel is damaged and integrated to obtain the total damage index of the reactor pressure vessel.
[0135] In this embodiment of the invention, the step of integrating the damage of the reactor pressure vessel based on the vessel fatigue damage accumulation and the creep damage accumulation in the damage accumulation record to obtain the total damage index of the reactor pressure vessel includes:
[0136] Based on the operating condition data of the reactor pressure vessel, the damage mechanism weights of the fatigue damage accumulation and the creep damage accumulation are determined, and a dynamic weight allocation scheme for the reactor pressure vessel is obtained.
[0137] According to the dynamic weight allocation scheme, the fatigue damage accumulation and the creep damage accumulation are weighted to obtain the weighted fatigue damage value and the weighted creep damage value.
[0138] The total damage index of the reactor pressure vessel is obtained by linearly superimposing the weighted fatigue damage value and the weighted creep damage value.
[0139] In this embodiment of the invention, the step of linearly superimposing the weighted fatigue damage value and the weighted creep damage value to obtain the total damage index of the reactor pressure vessel includes:
[0140] The weighted fatigue damage value and the weighted creep damage value are interactively corrected based on the damage coupling relationship between the weighted fatigue damage value and the weighted creep damage value to obtain the fatigue damage value and creep damage value of the reactor pressure vessel.
[0141] The fatigue damage value and the creep damage value are weighted and fused to obtain the initial total damage index of the reactor pressure vessel.
[0142] The initial total damage index is normalized to obtain the total damage index of the reactor pressure vessel.
[0143] Specifically, indicators related to damage mechanisms are extracted from the operating data of the reactor pressure vessel, including the frequency of temperature fluctuations and the duration of sustained high temperatures. The frequency of temperature fluctuations reflects the frequency of stress cycles in the operating conditions, and the duration of sustained high temperatures reflects the time that the material is in a high-temperature environment for a long period of time. If the frequency of temperature fluctuations is high, it indicates that fatigue damage has a more significant impact on the reactor pressure vessel. In this case, the weight of the damage mechanism accumulated by fatigue damage is set to a larger value. If the duration of sustained high temperatures indicates that creep damage has a more significant impact on the reactor pressure vessel, the weight of the damage mechanism accumulated by creep damage is set to a larger value. At the same time, it is ensured that the sum of the weight of the damage mechanism accumulated by fatigue damage and the weight of the damage mechanism accumulated by creep damage is 1. The specific allocation method that includes the two weights is the dynamic weight allocation scheme of the reactor pressure vessel.
[0144] Furthermore, based on the weight corresponding to the fatigue damage accumulation determined in the dynamic weight allocation scheme, the weight value is taken out and multiplied with the previously calculated fatigue damage accumulation of the reactor pressure vessel. The result of the operation is the weighted fatigue damage value. Then, based on the weight corresponding to the creep damage accumulation determined in the dynamic weight allocation scheme, the weight value is taken out and multiplied with the previously calculated creep damage accumulation of the reactor pressure vessel. The result of the operation is the weighted creep damage value.
[0145] Furthermore, the calculated weighted fatigue damage value and weighted creep damage value are extracted, and these two values are added together. The weighted fatigue damage value is directly added to the weighted creep damage value without introducing any other additional calculation steps. The result obtained after the addition operation is the total damage index of the reactor pressure vessel.
[0146] Specifically, the damage coupling relationship between the weighted fatigue damage value and the weighted creep damage value is first determined. This relationship is that the weighted fatigue damage value enhances the effect of the weighted creep damage value, and vice versa. Then, based on the fatigue-creep coupling test data of the materials used in the reactor pressure vessel, a fixed interactive correction ratio is determined. That is, 10% of the value is extracted from the weighted fatigue damage value as an interactive increment and added to the weighted creep damage value. At the same time, 10% of the value is extracted from the weighted creep damage value as an interactive increment and added to the weighted fatigue damage value. The weighted fatigue damage value is added to the corresponding interactive increment to obtain the fatigue damage value of the reactor pressure vessel. The weighted creep damage value is added to the corresponding interactive increment to obtain the creep damage value of the reactor pressure vessel.
[0147] Furthermore, the previously determined dynamic weight allocation scheme for the reactor pressure vessel is retrieved, and the weights corresponding to the cumulative fatigue damage and the cumulative creep damage are extracted from it. The obtained fatigue damage value of the reactor pressure vessel is multiplied by the weight corresponding to the cumulative fatigue damage to obtain the fatigue-weighted contribution value. The creep damage value of the reactor pressure vessel is multiplied by the weight corresponding to the cumulative creep damage to obtain the creep-weighted contribution value. The fatigue-weighted contribution value and the creep-weighted contribution value are added together to obtain the initial total damage index of the reactor pressure vessel.
[0148] Furthermore, the theoretical maximum value of the initial total damage index of the reactor pressure vessel is first determined. This maximum value is determined based on the ultimate damage tolerance of the materials used in the reactor pressure vessel, that is, the initial total damage index value corresponding to the complete failure of the materials. Then, the calculated initial total damage index of the reactor pressure vessel is divided by this theoretical maximum value, and the result is the total damage index of the reactor pressure vessel. This result is between 0 and 1, and the normalization process is completed.
[0149] In summary, a dynamic weighting scheme for the two types of damage is determined based on reactor pressure vessel operating condition data. This enables the weighted processing of fatigue and creep damage to adapt to the differences in damage impact under different operating scenarios, avoids weighting bias caused by fixed weights, and improves the adaptability of damage integration to the actual operating conditions of the equipment.
[0150] In summary, by considering the coupling relationship between the two types of damage and performing interactive correction, the one-sidedness of using fatigue or creep damage data alone can be compensated for. The corrected damage values are closer to the actual damage superposition effect of the equipment, ensuring the authenticity of the damage data.
[0151] In summary, normalizing the initial total damage index after weighted fusion unifies the index dimensions and range, avoids index distortion caused by differences in data volume, and makes the total damage index more valuable for reference. Ultimately, the total damage index, which accurately reflects the overall damage level of the equipment, provides a high-quality core basis for subsequent mapping of remaining lifespan, directly supports the reliability of lifespan prediction results, and ensures the scientific nature of equipment operation and maintenance decisions.
[0152] S6. Map the total damage index to the remaining life of the reactor pressure vessel.
[0153] In this embodiment of the invention, the total design life of the equipment is first extracted from the design technical documents of the reactor pressure vessel. The total design life is the expected total safe operating time of the equipment determined in the design stage, and the specific value of this time is defined as the basis for life calculation.
[0154] Furthermore, according to the meaning of the total damage index, the value of the total damage index represents the proportion of damage that has occurred to the reactor pressure vessel to the total allowable damage. This proportion is equivalent to the proportion of the equipment's consumed life to the total design life. Therefore, the value of the total damage index is directly determined as the proportion of consumed life.
[0155] Furthermore, by multiplying the extracted total design lifespan by the proportion of lifespan already consumed, the result is the lifespan of the reactor pressure vessel that has been consumed from commissioning to the present moment, and the specific value of this duration is recorded.
[0156] Furthermore, by subtracting the actual duration of the lifespan already consumed from the actual total design lifespan, the result obtained through this subtraction is the remaining lifespan of the reactor pressure vessel.
[0157] In summary, the total damage index is obtained after dynamic weight allocation, damage coupling correction and normalization. It can accurately reflect the overall damage level of the equipment. The mapping based on this index can avoid the deviation in life prediction caused by the distortion of core indexes, ensure that the remaining life result is highly matched with the actual wear and tear of the equipment, and improve the accuracy of prediction.
[0158] In summary, this mapping process transforms abstract damage quantification indicators into intuitive remaining life data, overcoming the limitation that damage indicators are difficult to directly guide operation and maintenance decisions. It provides staff with a clear and practical reference for equipment life, facilitating advance planning of maintenance actions such as inspection and replacement.
[0159] In summary, as the final output of the entire life prediction process, this mapping creates a practical closed loop for the entire process from dynamic feature extraction and model reconstruction to damage integration. It also enables the technical solution to form a complete link from data processing to result application, directly supporting safe operation and maintenance decisions for reactor pressure vessels and ensuring long-term stable operation of the equipment.
[0160] like Figure 2 The diagram shown is a functional block diagram of a reactor pressure vessel lifetime prediction system based on digital twins provided in an embodiment of the present invention.
[0161] The reactor pressure vessel lifetime prediction system 100 based on digital twins described in this invention can be installed in an electronic device. Depending on the functions implemented, the reactor pressure vessel lifetime prediction system 100 based on digital twins may include a feature acquisition module 101, a model parameter adjustment module 102, a model parameter configuration module 103, a thermal simulation module 104, a loss determination module 105, and a remaining lifetime generation module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.
[0162] In this embodiment, the functions of each module / unit are as follows:
[0163] The feature acquisition module 101 is used to extract the temperature change rate and stress distribution from the real-time monitoring data of the reactor pressure vessel to obtain the dynamic feature parameters of the reactor pressure vessel.
[0164] The model parameter adjustment module 102 is used to perform consistency verification on the baseline output in the initial model based on the dynamic feature parameters, and obtain the parameter adaptability evaluation result of the reactor pressure vessel.
[0165] The model parameter configuration module 103 is used to adjust the values of physical parameters in the initial model according to the parameter adaptability evaluation results, and reconstruct the initial model based on the adjusted physical parameters to obtain a digital twin model of the reactor pressure vessel.
[0166] The thermal simulation module 104 is used to simulate the thermo-mechanical behavior of the reactor pressure vessel under current operating conditions based on the digital twin model, and obtain the cumulative damage record of the reactor pressure vessel.
[0167] The loss determination module 105 is used to perform damage integration on the reactor pressure vessel based on the container fatigue damage accumulation and creep damage accumulation in the damage accumulation record, and obtain the total damage index of the reactor pressure vessel.
[0168] The remaining lifetime generation module 106 is used to map the total damage index to the remaining lifetime of the reactor pressure vessel.
[0169] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.
[0170] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0171] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.
[0172] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0173] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.
[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. A method for predicting the lifespan of a reactor pressure vessel based on digital twins, characterized in that, The method includes: S1. Extract the temperature change rate and stress distribution from the real-time monitoring data of the reactor pressure vessel to obtain the dynamic characteristic parameters of the reactor pressure vessel. S2. Based on the dynamic characteristic parameters, the baseline output in the initial model is verified for consistency to obtain the parameter adaptability evaluation results of the reactor pressure vessel. S3. Adjust the values of the physical parameters in the initial model according to the parameter adaptability evaluation results, and reconstruct the initial model based on the adjusted physical parameters to obtain the digital twin model of the reactor pressure vessel. S4. Simulate the thermo-mechanical behavior of the reactor pressure vessel under the current operating conditions based on the digital twin model to obtain the cumulative damage record of the reactor pressure vessel; S5. Based on the container fatigue damage accumulation and creep damage accumulation in the damage accumulation record, the reactor pressure vessel is damaged and integrated to obtain the total damage index of the reactor pressure vessel. S6. Map the total damage index to the remaining life of the reactor pressure vessel.
2. The reactor pressure vessel lifetime prediction method based on digital twin as described in claim 1, characterized in that, The real-time monitoring data of the reactor pressure vessel is used to extract the temperature change rate and stress distribution to obtain the dynamic characteristic parameters of the reactor pressure vessel, including: Temperature time series data and strain space data were separated from real-time monitoring data of the reactor pressure vessel; The temperature time series data is differentially processed to obtain the temperature change rate of the reactor pressure vessel; The stress distribution of the reactor pressure vessel is obtained by interpolating the strain space data. The temperature change rate and the stress distribution are combined to form the dynamic characteristic parameters of the reactor pressure vessel.
3. The reactor pressure vessel lifetime prediction method based on digital twin as described in claim 1, characterized in that, The process of verifying the consistency of the baseline output in the initial model based on the dynamic characteristic parameters to obtain the parameter adaptability evaluation results of the reactor pressure vessel includes: Based on the dynamic feature parameters and the baseline output in the initial model, the statistical characteristics of the residuals in the time series are used to determine the confidence interval for parameter fitting in the initial model. A dynamic threshold is established based on the historical operating data of the reactor pressure vessel, and the residual is compared with the dynamic threshold. Based on the comparison results of the confidence interval and the dynamic threshold, the parameter adaptability evaluation results of the reactor pressure vessel are generated.
4. The reactor pressure vessel lifetime prediction method based on digital twin as described in claim 3, characterized in that, The step of adjusting the values of the physical parameters in the initial model based on the parameter adaptability evaluation results, and reconstructing the initial model based on the adjusted physical parameters to obtain a digital twin model of the reactor pressure vessel includes: Based on the parameter adaptability evaluation results, the key physical parameters that need to be adjusted in the initial model are identified, and the parameter adjustment priority sequence of the initial model is obtained. The priority sequence is adjusted according to the parameters, and the values of the key physical parameters are iteratively corrected to obtain an optimized set of physical parameters. The optimized set of physical parameters is injected into the structural framework of the initial model; The simulation accuracy of the reconfigured model is verified, and the verified reconfigured model is output as a digital twin model of the reactor pressure vessel.
5. The reactor pressure vessel lifetime prediction method based on digital twin as described in claim 4, characterized in that, The step of adjusting the priority sequence according to the parameters and iteratively correcting the values of the key physical parameters to obtain an optimized set of physical parameters includes: The highest priority key physical parameter is selected from the parameter adjustment priority sequence to obtain the current adjustment parameters of the initial model; The current adjustment parameter is incrementally adjusted to generate the adjusted parameter value; The adjusted parameter values are substituted into the initial model for simulation testing to obtain the performance evaluation of the initial model parameters. When all key physical parameters meet the convergence condition, the adjusted parameter values are integrated to obtain the optimized set of physical parameters.
6. The reactor pressure vessel lifetime prediction method based on digital twin as described in claim 1, characterized in that, The simulation of the reactor pressure vessel's thermo-mechanical behavior under current operating conditions based on the digital twin model yields the accumulated fatigue damage and creep damage of the reactor pressure vessel, including: The temperature field and stress field under the current operating conditions are thermo-mechanically coupled using the digital twin model to obtain the thermo-mechanical response data of the reactor pressure vessel; The thermal gradient distribution and stress fluctuation characteristics at key locations are extracted from the thermo-mechanical response data to obtain the damage-sensitive parameters of the reactor pressure vessel. Based on the damage-sensitive parameters, the cumulative fatigue damage and cumulative creep damage of the reactor pressure vessel within a preset evaluation period are calculated respectively. The fatigue damage accumulation and the creep damage accumulation are integrated over time to obtain the damage accumulation record of the reactor pressure vessel.
7. The reactor pressure vessel lifetime prediction method based on digital twin as described in claim 6, characterized in that, The formula for calculating the cumulative fatigue damage is as follows: ； In the formula, Due to the accumulation of fatigue damage, The upper limit of time, To obtain the material fatigue index, To obtain the expected material fatigue strength, Time in the damage sensitivity parameter Stress amplitude at the location, As a time factor, For the temperature sensitivity coefficient to be obtained, Time in the damage sensitivity parameter The temperature at that location For reference temperature, For time differentiation, It is a natural exponential function; The formula for calculating the accumulation of creep damage is as follows: ； In the formula, For the accumulation of creep damage, The upper limit of time, Time in the damage sensitivity parameter Equivalent stress at the point, To obtain the material creep strength, For the pre-obtained stress index, For the pre-acquired activation energy, For the gas constant to be obtained, Time in the damage sensitivity parameter The temperature at that location For time differentiation, It is a natural exponential function.
8. The reactor pressure vessel lifetime prediction method based on digital twin as described in claim 1, characterized in that, The damage integration of the reactor pressure vessel based on the vessel fatigue damage accumulation and creep damage accumulation in the damage accumulation record to obtain the total damage index of the reactor pressure vessel includes: Based on the operating condition data of the reactor pressure vessel, the damage mechanism weights of the fatigue damage accumulation and the creep damage accumulation are determined, and a dynamic weight allocation scheme for the reactor pressure vessel is obtained. According to the dynamic weight allocation scheme, the fatigue damage accumulation and the creep damage accumulation are weighted to obtain the weighted fatigue damage value and the weighted creep damage value. The total damage index of the reactor pressure vessel is obtained by linearly superimposing the weighted fatigue damage value and the weighted creep damage value.
9. The reactor pressure vessel lifetime prediction method based on digital twin as described in claim 8, characterized in that, The method of linearly superimposing the weighted fatigue damage value and the weighted creep damage value to obtain the total damage index of the reactor pressure vessel includes: The weighted fatigue damage value and the weighted creep damage value are interactively corrected based on the damage coupling relationship between the weighted fatigue damage value and the weighted creep damage value to obtain the fatigue damage value and creep damage value of the reactor pressure vessel. The fatigue damage value and the creep damage value are weighted and fused to obtain the initial total damage index of the reactor pressure vessel. The initial total damage index is normalized to obtain the total damage index of the reactor pressure vessel.
10. A reactor pressure vessel lifetime prediction system based on digital twins, characterized in that, The system includes: The feature acquisition module is used to extract the temperature change rate and stress distribution from the real-time monitoring data of the reactor pressure vessel to obtain the dynamic feature parameters of the reactor pressure vessel. The model parameter adjustment module is used to perform consistency verification on the baseline output in the initial model based on the dynamic feature parameters, and obtain the parameter adaptability evaluation result of the reactor pressure vessel. The model parameter configuration module is used to adjust the values of physical parameters in the initial model according to the parameter adaptability evaluation results, and to reconstruct the initial model based on the adjusted physical parameters to obtain a digital twin model of the reactor pressure vessel. The thermal simulation module is used to simulate the thermo-mechanical behavior of the reactor pressure vessel under current operating conditions based on the digital twin model, and obtain the cumulative damage record of the reactor pressure vessel. The loss determination module is used to perform damage integration on the reactor pressure vessel based on the container fatigue damage accumulation and creep damage accumulation in the damage accumulation record, and obtain the total damage index of the reactor pressure vessel. The remaining lifetime generation module is used to map the total damage index to the remaining lifetime of the reactor pressure vessel.