Boiler pipeline stress monitoring system

Abstract

The invention discloses a boiler pipeline stress monitoring system, particularly relates to the field of pipeline stress analysis, and comprises a multi-physical field acquisition module, an uncertainty quantification module, a transient numerical simulation module, an evaluation prediction module and a model adaptive updating module. According to the boiler pipeline stress monitoring system, a structured first pipeline feature containing an uncertainty index is generated through an uncertainty quantification module, and uncertainty quantification of multi-physics field data is achieved; through the transient numerical simulation module, the strong time-varying coupling relation of multiple physical fields in the flexible peak regulation process is fully considered, the precision of a numerical calculation model is improved, and the defect that in the prior art, numerical simulation is insufficient in accuracy is overcome; the damage cost and the safety margin are calculated through the evaluation and prediction module, the deviation of a life prediction result is reduced, the accuracy of safety margin evaluation of unit peak regulation operation is improved, and the defect of inaccurate safety margin evaluation in the prior art is relieved.

Description

Technical Field

[0001] This invention relates to the field of pipeline stress analysis technology, and more specifically, to a boiler pipeline stress monitoring system. Background Technology

[0002] With the increasing demand for stress monitoring under strong transient conditions, the accuracy of numerical solutions to transient multiphysics coupling problems has become a core bottleneck restricting the accuracy of life prediction. Traditional techniques typically measure the physical parameters of key nodes and rely on simplified calculation models under steady-state conditions to calculate mechanical stress and estimate thermal stress. However, actual measurements show that the lack of a coupled calculation model for transient temperature gradients and dynamic alternating thermal stresses inside thick-walled components makes it difficult for simulations to accurately capture the strong time-varying characteristics of multiphysics fields. This leads to serious inaccuracies in the calculation and evaluation results of fatigue damage in stress concentration areas, making it impossible to provide reliable computational data support for safety risk early warning.

[0003] To overcome the shortcomings of traditional technologies, existing technologies use computer-aided construction of three-dimensional geometric models and loading of thermophysical property parameters. They employ computational fluid dynamics and structural mechanics coupled solution algorithms and rely on numerical simulation technology to simulate the internal transient temperature and stress field distribution caused by fluid temperature changes under transient conditions. This significantly improves the accuracy of calculation and analysis of the stress-strain evolution law at critical points from an algorithmic perspective and optimizes the adaptability and reliability of numerical simulation algorithms.

[0004] However, in practical use, it still has some shortcomings, such as the lack of measured data of multi-physics fields under high temperature environment, which leads to the lack of effective verification dataset to support the convergence and accuracy of the coupling algorithm, and the difficulty in quantifying the uncertainty of numerical solution; the numerical calculation model used to capture the strong time-varying coupling characteristics of multi-physics fields under strong transient conditions is not accurate enough, and fails to fully consider the strong time-varying coupling relationship of multi-physics fields in the flexible peak shaving process, resulting in insufficient accuracy of its numerical simulation, causing deviation in the life prediction results, and affecting the accuracy of the safety margin assessment of unit peak shaving operation. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a boiler pipeline stress monitoring system, which solves the problems mentioned in the background art through the following solutions.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A boiler piping stress monitoring system, comprising: Multiphysics acquisition module: used to acquire first pipeline data in real time in response to a sensing device deployed at a preset position on the thick-walled component, the first pipeline data including temperature, strain and pressure; Uncertainty quantification module: used to preprocess the first pipeline data to generate a first pipeline feature including an uncertainty index; Transient numerical simulation module: used to perform transient numerical simulation based on a preset three-dimensional geometric model, using the first pipeline feature as boundary condition, to calculate and output the second pipeline feature characterizing time-varying characteristics; Evaluation and prediction module: used to calculate the interaction between fatigue damage and creep damage based on the second pipeline characteristics, so as to obtain a third pipeline characteristic including damage cost and safety margin; Model adaptive update module: used to compare the first pipeline features with the second pipeline features and generate error indicators, and dynamically adjust the operating parameters of the transient numerical simulation module.

[0007] Preferably, the multi-physics acquisition module includes a thick-walled component comprising a final-stage superheater outlet header and a final-stage reheater outlet header; the preset position includes the intersection area of ​​the header pipe joint and the shell, the chamfered portion at the header opening, and the tee connection portion in the piping system.

[0008] Preferably, the uncertainty quantification module generates the first pipeline feature, specifically including: Within a preset time window, the statistical standard deviation of the first pipeline data is calculated using the Bessel formula, and used as the first uncertainty component. Based on the accuracy index of the sensing device, its standard uncertainty is evaluated using the normal distribution assumption, and used as the second uncertainty component. The first uncertainty component and the second uncertainty component are combined to generate an uncertainty interval.

[0009] Preferably, the transient numerical simulation module acquires the second pipeline characteristics, specifically including: Read the physical quantity values ​​and their uncertainty ranges from the first pipeline characteristics; N sets of boundary condition samples are generated within the uncertainty interval, where N≥1000; Perform multiple parallel simulations on N sets of samples; Statistical analysis was performed on the set of all simulation results, and the 5th percentile, median and 95th percentile of the results at each time point were taken to obtain the second pipeline characteristics expressed in the form of probability distribution. The second pipeline feature includes at least the time series, the median and quantiles of the equivalent stress, and the median and quantiles of the damage variable.

[0010] Preferably, the transient numerical simulation module, under isotropic damage conditions, uses the damage variable... The density of microscopic defects is quantified as follows: in, Represented as stress tensor, It represents the intrinsic elastic modulus under high-temperature conditions within the insulation space of a bulk package. It is expressed as the elastic strain tensor.

[0011] Preferably, the evaluation and prediction module obtains the third pipeline features, specifically including: Based on the time-varying stress field, strain field, and temperature field data contained in the second pipeline features, stress-time history and temperature-time history are extracted from preset monitoring points. Calculate the fatigue damage caused by cyclic loading and the creep damage caused by the combined effect of steady-state loading and high temperature during the target monitoring period, respectively. By coupling fatigue damage and creep damage, a third pipe characteristic including the remaining life is calculated.

[0012] Preferably, the evaluation and prediction module calculates the interaction between fatigue damage and creep damage, specifically including: Calculate the total fatigue damage caused by cyclic loading within a preset time period. and total creep damage caused by steady-state load holding : The total fatigue damage Rainflow counting was performed on the stress-time history of the monitoring points to obtain... The stress cycle; for the first stress cycle; One cycle, its fatigue damage is ,in This indicates that the loop has occurred once. This represents the allowable number of cycles for the material at that cyclic stress level; total fatigue damage is the sum of all cyclic damage, specifically expressed as: The total creep damage By dividing time into The steady-state load holding segment, for the first... The duration of each time period is The material creep rupture time under the corresponding temperature and stress levels within that time period is The creep damage generated during its time period is Total creep damage The sum of damage over all time periods is expressed as follows: The calculated total fatigue damage and total creep damage The envelope is compared with the envelope determined by material experiments, which is in the range of... For the horizontal axis, In a coordinate system with the vertical axis as the boundary, the boundaries between the safe and failure zones are defined.

[0013] Preferably, the evaluation and prediction module is based on the average temperature of the second pipeline characteristic under steady-state operating conditions. With average equivalent stress This provides a prediction of long-term creep life based on the interaction between fatigue damage and creep damage, specifically expressed as follows: in, Expressed as the pipe material constant, Indicated as predicted lifespan, The thermal strength parameter of the pipe material is determined by a polynomial formula obtained by fitting high-temperature creep test data of the material: in, All of these are fitting constants for the target monitoring material.

[0014] The technical effects and advantages of this invention are as follows: 1. This invention generates a structured first pipeline feature containing uncertainty index through an uncertainty quantification module, thereby realizing the uncertainty quantification of multi-physics data; 2. This invention, through a transient numerical simulation module, fully considers the strong time-varying coupling relationship of multiple physics fields during flexible peak shaving, improves the accuracy of the numerical calculation model, and alleviates the shortcomings of insufficient accuracy in numerical simulation in the prior art. 3. This invention reduces the deviation of life prediction results by calculating damage cost and safety margin through the evaluation and prediction module, improves the accuracy of safety margin assessment for unit peak operation, and alleviates the shortcomings of inaccurate safety margin assessment in the prior art. Attached Figure Description

[0015] Figure 1 This is a block diagram of a boiler pipeline stress monitoring system provided according to an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0018] Hereinafter, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first," "second," and "third" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0019] As attached Figure 1 The boiler pipeline stress monitoring system shown includes a multi-physics field acquisition module, an uncertainty quantification module, a transient numerical simulation module, an evaluation and prediction module, and a model adaptive update module.

[0020] The multiphysics acquisition module is used to respond to the sensing device deployed at a preset position on the thick-walled component to acquire first pipeline data in real time. The first pipeline data includes temperature data acquired by thermocouples, strain data acquired by high-temperature strain gauges, and pressure data acquired by pressure transmitters.

[0021] Specifically, the thick-walled component includes a final-stage superheater outlet header and a final-stage reheater outlet header; the preset position is determined by adjusting the stress concentration factor of the three-dimensional structure of the thick-walled component. Analysis revealed high-stress areas, including the intersection of the header pipe joint and the cylinder, the chamfered area at the header opening, and the tee connection in the piping system.

[0022] Furthermore, the stress concentration factor of the three-dimensional structure of the thick-walled component is determined. The analysis includes: importing the three-dimensional structures of the final-stage superheater outlet header and the final-stage reheater outlet header into a preset finite element analysis software; in the finite element analysis software, applying loads and constraints simulating the rated operating conditions of the boiler to the three-dimensional structure, specifically applying boundary conditions including: applying a uniformly distributed normal pressure equal to the boiler's design rated operating pressure on the inner wall surfaces of the header and all connecting pipes; in this embodiment, this is 25.73. Applying symmetric boundary conditions to the symmetry planes of the 3D structure significantly reduces model size and computation time; applying fixed displacement constraints at the ends of the header or designated support locations; calling the mesh generation algorithm, using SOLID185 for mesh generation, refining the mesh in stress concentration regions, and calculating the stress concentration factor at each mesh point. Specifically, it includes: in, This is represented as the local peak stress at the mesh point location, directly extracted through finite element analysis. This represents the theoretical stress of a uniform portion far from geometrically discontinuous regions; in this embodiment, its value is calculated based on the circumferential membrane stress of the header cylinder according to the standard thin-walled cylinder theory formula, and the calculated values ​​at each point are... The value is compared with a preset engineering threshold, which is generally defined as 2.0. High-stress nodes are automatically identified and marked, and finally integrated into high-stress areas.

[0023] In this embodiment, the sensing device includes: a K-type armored thermocouple, fixed to the preset position by welding, for measuring local temperature; a high-temperature strain gauge, a metal foil strain gauge with a temperature resistance of 600℃ and a resistance of 350Ω, which is attached to the chamfered part and intersection area of ​​the header opening using high-temperature welding adhesive, to measure the axial and circumferential strain at that part; a pressure transmitter connected to the main steam pipeline of the boiler through a pressure tap on the pipeline, outputting a 4-20mA standard signal; simultaneously, the signal lines of the high-temperature strain gauge and thermocouple are entirely wrapped with ceramic fiber sleeves with a temperature resistance of not less than 800℃, and IP67-rated sealing glands are installed at the points where they pass through the furnace wall for protection and sealing; all signal lines are led out from inside the boiler drum and connected to a signal conditioner and a multi-channel data acquisition instrument located outside the drum area.

[0024] Furthermore, the multi-channel data acquisition instrument transmits the packaged first pipeline data in real time via an Ethernet interface, and the first pipeline data adopts a predefined structured format, including acquisition point ID, physical quantity type, value, and high-precision timestamp.

[0025] The uncertainty quantification module is used to preprocess the first pipeline data to generate a first pipeline feature including an uncertainty index. The first pipeline feature is a structured data object, which includes at least: a unified high-precision timestamp, a collection point ID, a physical quantity value after preprocessing and unit conversion, and the corresponding uncertainty interval.

[0026] Furthermore, the preprocessing operation for the first pipeline data includes: outlier removal, whereby the data sequence of each sensor is automatically identified and removed using the Grubbs test; in this embodiment, the Grubbs test is performed at a significance level... Under the specified conditions, when the calculated statistical value exceeds the critical value at that level, the data is determined to be an outlier and is removed. Time alignment is performed by activating a time alignment algorithm, using a unified high-precision time series as a benchmark, to align data channels with slight time deviations. In this embodiment, the time alignment algorithm uses linear interpolation to uniformly interpolate the data of each channel to the time point based on the unified high-precision time series. For missing data points caused by outlier removal or alignment, cubic spline interpolation is used to fill them, ensuring that all data channels are strictly synchronized and continuous on the time axis. In this embodiment, cubic spline interpolation is performed only when the number of consecutive missing data points does not exceed 3, and there are at least 5 valid data points on both sides to support the stable construction of the interpolation function. For missing data at the beginning or end of the sequence, linear extrapolation is used to fill them. Unit conversion and physical quantity restoration are performed by batch converting the unified AD values ​​into engineering unit data through an arithmetic logic unit.

[0027] In this embodiment, outlier removal is performed iteratively. After removing a set of outliers and completing interpolation, the new data sequence is subjected to Grubbs' test again until no outliers can be detected at a given significance level, thereby ensuring the thoroughness of the removal.

[0028] In one possible implementation, the uncertainty of the preprocessed data is quantified using statistical methods, including: within a preset time window, calculating the statistical standard deviation of each data point in the preprocessed first pipeline data using the Bessel formula, as the first uncertainty component. To reflect the random fluctuation characteristics of the measurement data; based on the accuracy indicators specified in the calibration certificate of the sensing device, and combined with known systematic error sources such as the sensor's installation angle deviation and thermal radiation interference at the site, its standard uncertainty is evaluated using the normal distribution assumption, and is used as the second uncertainty component. To reflect the systematic error characteristics of the measurement; the first uncertainty component and the second uncertainty component are combined to generate an uncertainty interval.

[0029] In this embodiment, the generation of the uncertainty interval includes: the preset time window is a sliding window centered on the current data point and consisting of 5 data points before and after it; the maximum permissible error of the pressure transmitter obtained from the calibration certificate is ±0.5%FS, and its error follows a normal distribution, then the second uncertainty component introduced by it... for: The maximum deviation of the strain gauge installation angle was determined by laser tracking. Based on the formulas of mechanics of materials, the maximum error in theoretical strain measurement caused by this deviation is estimated to be... Then the second uncertainty component it introduces for: , Represented as the unit symbol for microstrain; combining all second uncertainty components: The first uncertainty component is calculated using a linear formula. and the second uncertainty component The synthesis is specifically represented as follows: To represent each data point uncertainty The data in the first pipeline The uncertainty interval is expressed as , which includes factors This corresponds to a confidence level of approximately 95%.

[0030] The transient numerical simulation module is used to perform transient numerical simulation based on a preset three-dimensional geometric model, using the first pipeline feature as the boundary condition, to calculate and output the second pipeline feature that characterizes the time-varying properties.

[0031] It should be noted that using the first pipe feature as a boundary condition means: in transient thermal analysis, the measured temperature of the thermocouple in the first pipe feature is used as the fluid temperature of the convective heat transfer boundary condition; in transient structural analysis, the measured pressure of the pressure transmitter in the first pipe feature is used as a surface force load applied to the inner wall of the pipe, and the nodal temperature field obtained from the thermal analysis is used as a volume load applied to the entire structure.

[0032] Specifically, the transient numerical simulation reads the physical quantity values ​​and their uncertainty ranges from the first pipeline characteristics; uses the Latin hypercube sampling method to generate no less than 1000 sets of boundary condition samples within the uncertainty range; performs multiple parallel simulations; performs statistical analysis on the set of all simulation results; and takes the 5th percentile, median, and 95th percentile of the results at each time point to obtain the second pipeline characteristics expressed in the form of a probability distribution. The second pipeline characteristics are output as a standardized database file, which at least includes the time series, the median of the equivalent stress, the 5th percentile of the equivalent stress, the 95th percentile of the equivalent stress, the median of the damage variable, and the 95th percentile of the damage variable.

[0033] Furthermore, the execution of multiple parallel simulations is achieved through an automated script, the process of which is as follows: Latin hypercube sampling is invoked to generate N sets of boundary condition samples; these samples are then distributed to multiple computing nodes in the high-performance computing cluster; on each node, the boundary condition parameters in the master script are automatically modified, and the ANSYS solver is invoked to perform independent transient simulations; after all calculations are completed, a post-processing script automatically extracts the target time-varying data from each simulation result file and calculates its statistical quantiles to generate the probabilistic form of the second pipeline feature.

[0034] In one possible implementation, under the assumption of isotropic damage state, a scalar damage variable is used. The density of microscopic defects within a material is quantified, specifically expressed as: in, Represented as stress tensor, It represents the intrinsic elastic modulus under high-temperature conditions within the insulation space of a bulk package. Represented as the elastic strain tensor; effective stress coupled with damage effects. Specifically, it is expressed as: It should be noted that the stress tensor This refers to the direct response of a component to pressure and thermal loads during peak shaving; damage variables. This characterizes the degree of accumulation of micro-defect density inside the header material due to fatigue crack initiation and creep pore expansion under start-stop and high-transient conditions of flexible peak shaving. Corresponding to the initial undamaged state of the new component, This indicates a total loss state where the load-bearing capacity is completely lost; when the damage variable... Accumulated to critical damage value At that time, the failure threshold corresponding to the region of maximum stress and greatest danger; the intrinsic elastic modulus under high temperature conditions within the insulation space of the bulk packaging. Its value changes dynamically with the real-time temperature measured by the thermocouple; elastic strain tensor Data were obtained directly from high-temperature strain gauges at typical locations in the header and pipeline.

[0035] In this embodiment, the critical damage value The damage values ​​are statistically averaged by conducting high-temperature low-cycle fatigue tests or creep fracture tests on the target material at the corresponding failure cycles or fracture time. The typical value range for P91 steel is 0.2 to 0.5 at 590-620℃.

[0036] Furthermore, the damage variable The definition is transformed from micro-defect density to intrinsic elastic modulus. The decrease in its evolution rate Specifically, it is expressed as follows: in, Expressed as damage rate, Expressed as energy density release rate, and Expressed as temperature-dependent material constants, Expressed as cumulative viscoplastic strain rate; the energy density release rate Is related to damage rate The conjugate thermodynamic variable represents the energy dissipation during the irreversible process of material degradation, and its value is expressed as: in, Expressed as equivalent stress, Expressed as stress triaxiality; the cumulative viscoplastic strain rate Determined by an explicit Norton viscosity function to simulate the variation of viscoplastic strain rate with stress level: in, and It is expressed as a temperature-dependent viscous material constant.

[0037] In this embodiment, the stress triaxiality The value of is defined as: in, Expressed as Poisson's ratio, Represented as hydrostatic stress; the rate-dependent yield behavior of the material is given by the von Mises yield function with introduced effective stress. describe: in, Represented as an intrinsic variable of motion hardening. Represented as isotropic hardening internal variables, they are used to describe the movement of the yield surface center and the change in the size of the yield surface, respectively. Expressed as initial yield strength; total strain rate Decomposed into elastic strain rate Inelastic strain The sum of the inelastic strain rates Determined by the associated plastic flow law: in, The gradient tensor of the yield function corresponding to the force is represented as: The coupled damage with two kinematic hardening internal variables is represented as: in, and The material constants are temperature-dependent; the isotropic hardening model for coupled damage can be expressed as: in, and Expressed as temperature-dependent material constants, Represented as an isotropic hardening internal variable asymptotic value, This is expressed as the evolution rate.

[0038] It should be noted that the material constants required for this invention include, but are not limited to, those specified in the invention. , , , , , , , , and critical damage value All of these are temperature-related material properties, and their specific values ​​are determined through specialized high-temperature mechanical tests on the target material, combined with parameter inversion identification technology. The specific process is as follows: uniaxial isothermal low-cycle fatigue tests and creep tests are conducted at a series of temperatures to obtain cyclic stress, strain, and creep data of the material; using the test data as the fitting target, a genetic algorithm is used to perform parameter inversion to identify the material constant set at each temperature point; the material constants at any operating temperature are determined by interpolation methods; the critical damage value... The damage state of the sample at the failure time is statistically analyzed; the material constants identified through special tests and parameter inversion are organized into a data table with temperature as the independent variable and stored in the database. At each time step, the corresponding material constants are obtained in real time by linear interpolation based on the currently calculated unit average temperature. Those skilled in the art can determine all the parameters required to realize the present invention through the above method.

[0039] The evaluation and prediction module is used to calculate the interaction between fatigue damage and creep damage based on the second pipeline characteristics, so as to obtain a third pipeline characteristic that includes damage cost and safety margin.

[0040] Specifically, based on the time-varying stress field, strain field, and temperature field data contained in the second pipeline feature, stress-time history and temperature-time history are extracted from preset monitoring points; fatigue damage caused by cyclic load and creep damage caused by steady-state load and high temperature are calculated respectively during the target monitoring time period; and a third pipeline feature containing the remaining life is calculated by coupling fatigue damage and creep damage.

[0041] In one possible implementation, calculating the interaction between fatigue damage and creep damage includes: calculating the total fatigue damage caused by cyclic loading over a preset time period. and total creep damage caused by steady-state load holding The total fatigue damage mentioned above Rainflow counting was performed on the stress-time history of the monitoring points to obtain... The stress cycle; for the first stress cycle; One cycle, its fatigue damage is ,in This indicates that the loop has occurred once. This is expressed as the allowable number of cycles for the material at this cyclic stress level. Obtained by querying the design fatigue curve; total fatigue damage is the sum of all cyclic damage, specifically expressed as: The total creep damage By dividing time into The steady-state load holding segment, for the first... The duration of each time period is The material creep rupture time under the corresponding temperature and stress levels within that time period is The creep rupture time of the material The creep damage generated within the specified time period was obtained by extrapolating from the endurance strength data provided in existing material reports. Total creep damage The sum of damage over all time periods is expressed as follows: The calculated total fatigue damage and total creep damage The envelope of fatigue-creep damage interaction, determined by materials experiments, is compared with that of the envelope in terms of... For the horizontal axis, In a coordinate system with the vertical axis as the boundary, the boundaries between the safe and failure zones are defined; when the coordinate point ( , If the value is located below the envelope, it is considered safe.

[0042] It should be noted that the damage cost refers to the fraction of lifetime consumed during the target monitoring period, and its value is the total damage degree. Specifically, it is expressed as: in, and From the fatigue-creep damage interaction envelope, based on the current point ( , The allowable damage limit is determined by the positional relationship of the coordinates; the safety margin is defined as the current coordinate point ( , The shortest Euclidean distance to the envelope is used to characterize the safe boundary distance in the damage space.

[0043] In a preferred embodiment, the fatigue-creep damage interaction envelope, determined by materials experiments, is defined by the following equation: when When ≤0.3, + / 0.3≤1; when When ≤0.3, / 0.3+ ≤1; when >0.3 and When >0.3, + ≤1.3; in, and That is, based on the current damage point ( , The allowable value is determined based on the region within the aforementioned envelope.

[0044] In one possible implementation, the average temperature under steady-state operating conditions in the second pipeline characteristic is used as a basis. With average equivalent stress This provides a prediction of long-term creep life based on the interaction between fatigue damage and creep damage, specifically expressed as follows: in, Expressed as a pipe material constant, in this embodiment, for P91 steel, 20, Indicated as predicted lifespan, The thermal strength parameter of the pipe material is determined by a polynomial formula obtained by fitting high-temperature creep test data of the material: in, All of these are fitting constants for the target monitoring material; in this embodiment, , .

[0045] The model adaptive update module is used to compare the first pipeline features with the second pipeline features and generate error indices, and dynamically adjust the operating parameters of the transient numerical simulation module.

[0046] Specifically, the dynamic adjustment is to minimize the weighted root mean square error between the second pipeline feature and the first pipeline feature. The generation of the error index includes: extracting the measured strain-time series of the monitoring points from the first pipeline feature; extracting the simulated strain-time series of the same location and time period from the second pipeline feature; and calculating the weighted root mean square error between the two series as the core error index. The weighting coefficient is determined by the reciprocal of the uncertainty index of the corresponding data point in the first pipeline feature, that is, the smaller the uncertainty of the data point, the higher its weight in the error calculation.

[0047] It should be noted that the dynamic adjustment process is based on the error index, using a gradient descent optimization algorithm or a Bayesian optimization framework, with the goal of minimizing the weighted root mean square error, to iteratively update the above material parameters. Specifically, this involves: initializing a set of model parameters; running a transient numerical simulation to obtain the simulation results and calculating the error index compared with the measured data; calculating the gradient of the error with respect to the parameters; updating the parameters according to the gradient along the direction that reduces the error; and repeating the iteration until the preset maximum number of iterations is reached.

[0048] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A boiler piping stress monitoring system, characterized in that, include: Multiphysics acquisition module: used to acquire first pipeline data in real time in response to a sensing device deployed at a preset position on the thick-walled component, the first pipeline data including temperature, strain and pressure; Uncertainty quantification module: used to preprocess the first pipeline data to generate a first pipeline feature including an uncertainty index; Transient numerical simulation module: used to perform transient numerical simulation based on a preset three-dimensional geometric model, using the first pipeline feature as boundary condition, to calculate and output the second pipeline feature characterizing time-varying characteristics; Evaluation and prediction module: used to calculate the interaction between fatigue damage and creep damage based on the second pipeline characteristics, so as to obtain a third pipeline characteristic including damage cost and safety margin; Model adaptive update module: used to compare the first pipeline features with the second pipeline features and generate error indicators, and dynamically adjust the operating parameters of the transient numerical simulation module.

2. The boiler pipeline stress monitoring system according to claim 1, characterized in that: The multi-physics acquisition module includes a thick-walled component comprising a final-stage superheater outlet header and a final-stage reheater outlet header; the preset positions include the intersection area of ​​the header pipe joint and the shell, the chamfered portion at the header opening, and the tee connection portion in the piping system.

3. The boiler pipeline stress monitoring system according to claim 1, characterized in that: The uncertainty quantification module generates the first pipeline feature, specifically including: Within a preset time window, the statistical standard deviation of the first pipeline data is calculated using the Bessel formula, and used as the first uncertainty component. Based on the accuracy index of the sensing device, its standard uncertainty is evaluated using the normal distribution assumption, and used as the second uncertainty component. The first uncertainty component and the second uncertainty component are combined to generate an uncertainty interval.

4. The boiler pipeline stress monitoring system according to claim 1, characterized in that: The transient numerical simulation module acquires the second pipeline characteristics, specifically including: Read the physical quantity values ​​and their uncertainty ranges from the first pipeline characteristics; N sets of boundary condition samples are generated within the uncertainty interval, where N≥1000; Perform multiple parallel simulations on N sets of samples; Statistical analysis was performed on the set of all simulation results, and the 5th percentile, median and 95th percentile of the results at each time point were taken to obtain the second pipeline characteristics expressed in the form of probability distribution. The second pipeline feature includes at least the time series, the median and quantiles of the equivalent stress, and the median and quantiles of the damage variable.

5. A boiler pipeline stress monitoring system according to claim 4, characterized in that: The transient numerical simulation module, under isotropic damage conditions, uses the damage variables... The density of microscopic defects is quantified as follows: in, Represented as stress tensor, It represents the intrinsic elastic modulus under high-temperature conditions within the insulation space of a bulk package. It is expressed as the elastic strain tensor.

6. The boiler pipeline stress monitoring system according to claim 1, characterized in that: The evaluation and prediction module obtains the features of the third pipeline, specifically including: Based on the time-varying stress field, strain field, and temperature field data contained in the second pipeline features, stress-time history and temperature-time history are extracted from preset monitoring points. Calculate the fatigue damage caused by cyclic loading and the creep damage caused by the combined effect of steady-state loading and high temperature during the target monitoring period, respectively. By coupling fatigue damage and creep damage, a third pipe characteristic including the remaining life is calculated.

7. A boiler pipeline stress monitoring system according to claim 6, characterized in that: The evaluation and prediction module calculates the interaction between fatigue damage and creep damage, specifically including: Calculate the total fatigue damage caused by cyclic loading within a preset time period. and total creep damage caused by steady-state load holding : The total fatigue damage Rainflow counting was performed on the stress-time history of the monitoring points to obtain... The stress cycle; for the first stress cycle; One cycle, its fatigue damage is ,in This indicates that the loop has occurred once. This represents the allowable number of cycles for the material at that cyclic stress level; total fatigue damage is the sum of all cyclic damage, specifically expressed as: The total creep damage By dividing time into The steady-state load holding segment, for the first... The duration of each time period is The material creep rupture time under the corresponding temperature and stress levels within that time period is The creep damage generated during its time period is Total creep damage The sum of damage over all time periods is expressed as follows: The calculated total fatigue damage and total creep damage The envelope is compared with the envelope determined by material experiments, which is in the range of... For the horizontal axis, In a coordinate system with the vertical axis as the boundary, the boundaries between the safe and failure zones are defined.

8. A boiler pipeline stress monitoring system according to claim 6, characterized in that: The evaluation and prediction module is based on the average temperature under steady-state operating conditions in the second pipeline characteristic. With average equivalent stress This provides a prediction of long-term creep life based on the interaction between fatigue damage and creep damage, specifically expressed as follows: in, Expressed as the pipe material constant, Indicated as predicted lifespan, The thermal strength parameter of the pipe material is determined by a polynomial formula obtained by fitting high-temperature creep test data of the material: in, All of these are fitting constants for the target monitoring material.

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