WO2025232121A1 - 带凹痕传热管的应力腐蚀预测方法和装置、设备及介质 - Google Patents

带凹痕传热管的应力腐蚀预测方法和装置、设备及介质

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Publication number
WO2025232121A1
WO2025232121A1 PCT/CN2024/129932 CN2024129932W WO2025232121A1 WO 2025232121 A1 WO2025232121 A1 WO 2025232121A1 CN 2024129932 W CN2024129932 W CN 2024129932W WO 2025232121 A1 WO2025232121 A1 WO 2025232121A1
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Prior art keywords
sample
stress corrosion
prediction model
stress
target
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English (en)
French (fr)
Inventor
邓小云
李学军
匡艳军
尤磊
刘彦章
张凯歌
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China Nuclear Power Engineering Co Ltd
Shenzhen China Guangdong Nuclear Engineering Design Co Ltd
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China Nuclear Power Engineering Co Ltd
Shenzhen China Guangdong Nuclear Engineering Design Co Ltd
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Publication of WO2025232121A1 publication Critical patent/WO2025232121A1/zh
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N17/00—Investigating resistance of materials to the weather, to corrosion, or to light
    • G01N17/006—Investigating resistance of materials to the weather, to corrosion, or to light of metals
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/27—Regression, e.g. linear or logistic regression

Definitions

  • This application relates to the field of stress corrosion assessment technology, and in particular to a method and apparatus for predicting stress corrosion of a heat transfer tube with dents, an electronic device, and a computer-readable storage medium.
  • Stress corrosion cracking has long been a typical failure mode for austenitic stainless steel and nickel-based alloys, and a common problem that many industries, including the nuclear power sector, urgently need to solve. Stress corrosion cracking is the result of the interaction between materials, environment, and stress. Due to the complexity of its formation mechanism, there are many technical challenges in experimental research and engineering assessment of stress corrosion.
  • the surface damage state of stainless steel or nickel-based alloy heat transfer tube components affects the mechanical properties of the metal, such as wear resistance and stress corrosion resistance.
  • various thin-walled heat transfer tubes in heat exchangers inevitably suffer surface damage defects due to improper manufacturing processes, transportation impacts, installation collisions, jamming, and foreign object impacts during operation. These damages exacerbate the risk of stress corrosion failure in heat transfer tubes, posing a threat to the safe operation of the equipment.
  • This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method and apparatus for predicting stress corrosion of a dented heat transfer tube, electronic equipment, and a computer-readable storage medium, which can improve the efficiency of stress corrosion prediction.
  • a first aspect of this application proposes a method for predicting stress corrosion of a dented heat transfer tube, the method comprising:
  • Input variables were selected from multiple candidate factors affecting stress corrosion.
  • a box plot is drawn based on the sample stress corrosion results data and the input variables, and the mapping relationship between the sample stress corrosion results data and the input variables is determined based on the distribution pattern of the quartile values in the box plot.
  • a stress corrosion prediction model is constructed.
  • the stress corrosion prediction model is used to predict the target stress corrosion results of the dented heat transfer tube.
  • sample stress corrosion result data and multiple candidate stress corrosion influencing factors that cause the sample stress corrosion results includes:
  • the target sample is placed in a preset corrosion environment, and corrosion state data of the target sample is acquired at predetermined intervals.
  • the candidate stress corrosion influencing factors include sample indentation type and sample indentation depth;
  • the step of creating a dent in the initial sample based on multiple candidate stress corrosion influencing factors to obtain the target sample includes:
  • the punch is determined based on the type of indentation in the sample.
  • the indentation depth of the sample is mapped to obtain the target energy
  • the target sample is obtained by applying the target energy to the initial sample using the impact method and the punch.
  • the first energy-dent depth mapping relationship includes candidate energy-dent depth mapping relationships corresponding to multiple candidate dent types
  • the step of mapping the sample indentation depth using the mapping relationship between the first energy and the indentation depth to obtain the target energy includes:
  • the candidate energy and dent depth mapping relationship corresponding to multiple candidate dent types is filtered to obtain the second energy and dent depth mapping relationship;
  • the sample dimple depth is mapped to obtain the target energy.
  • the input variables include sample loading stress and sample indentation depth
  • the mapping relationship includes a positive correlation between the sample stress corrosion result data and the sample loading stress, and a quadratic curve relationship between the sample stress corrosion result data and the sample indentation depth;
  • the step of constructing the stress corrosion prediction model based on the mapping relationship between the sample stress corrosion results data and the input variables includes:
  • the stress corrosion prediction model is determined based on the first formula and the second formula.
  • the step of constructing the stress corrosion prediction model based on the mapping relationship between the sample stress corrosion result data and the input variables further includes:
  • a third formula is constructed based on the sample loading stress and the sample indentation depth, which indicates the multiplicative relationship between the sample loading stress and the sample indentation depth.
  • the stress corrosion prediction model is determined based on the first integrated formula and the third formula.
  • determining the stress corrosion prediction model based on the first integration formula and the third formula includes:
  • the first integrated formula and the third formula are integrated to obtain the second integrated formula
  • the stress corrosion prediction model is obtained by multiplying the sample corrosion environment factor and the second integrated formula.
  • L is the crack length of the sample cross section
  • x is the dent depth of the sample
  • y is the loading stress of the sample
  • ⁇ is the corrosion environment factor of the sample
  • p0 , p1 , p2 , p3 , and p4 are all coefficients
  • ⁇ is the random error.
  • the input variable includes the sample dent type
  • the stress corrosion prediction model includes candidate stress corrosion prediction models corresponding to the sample dent type
  • the data predicting the target stress corrosion result of the dented heat transfer tube using the stress corrosion prediction model includes:
  • the type of the dented heat transfer tube is detected to obtain the target dent type
  • the dented heat transfer pipe is predicted using the candidate stress corrosion prediction model corresponding to the sample dent type, and the target stress corrosion result data is obtained.
  • the prediction of the target stress corrosion result data of the dented heat transfer tube using the stress corrosion prediction model includes:
  • the stress corrosion prediction model is transformed to obtain the signal stress corrosion prediction model.
  • the dented heat transfer tube was subjected to non-destructive testing to obtain the target non-destructive testing signal;
  • the target non-destructive testing signal is input into the signal stress corrosion prediction model to predict corrosion and obtain the target stress corrosion result data.
  • a stress corrosion prediction device for a dented heat transfer tube comprising:
  • the acquisition module is used to acquire sample stress corrosion result data and multiple candidate stress corrosion influencing factors that cause the sample stress corrosion results;
  • the filtering module is used to filter input variables from multiple candidate stress corrosion influencing factors
  • the determination module is used to draw a box plot based on the sample stress corrosion result data and the input variables, and to determine the mapping relationship between the sample stress corrosion result data and the input variables based on the distribution pattern of the quartile values in the box plot;
  • a construction module is used to construct a stress corrosion prediction model based on the mapping relationship between the sample stress corrosion result data and the input variables;
  • the prediction module is used to predict the target stress corrosion result data of the dented heat transfer pipe through the stress corrosion prediction model.
  • a third aspect of this application provides an electronic device, which includes a memory and a processor.
  • the memory stores a computer program
  • the processor executes the computer program to implement the stress corrosion prediction method for the dented heat transfer tube described in the first aspect.
  • a fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the stress corrosion prediction method for dented heat transfer tubes described in the first aspect.
  • input variables are first selected from multiple candidate stress corrosion influencing factors.
  • a box plot is then used to determine the mapping relationship between the sample stress corrosion results data and the input variables.
  • a stress corrosion prediction model is constructed, and predictions are then made using this model.
  • the distribution pattern of the quartile values in the box plot indicates the mapping relationship between the sample stress corrosion results data and the input variables, thus enabling the construction of the stress corrosion prediction model.
  • the constructed stress corrosion prediction model can be used to predict the target stress corrosion results data of the dented heat transfer pipe. This embodiment improves the efficiency of stress corrosion prediction.
  • Figure 1 is a flowchart of the stress corrosion prediction method for a dented heat transfer tube provided in an embodiment of this application;
  • FIG. 2 is a flowchart of step 101 in Figure 1;
  • Figure 3A is a schematic diagram of the energy-depth mapping relationship of a triangular prism-shaped indentation
  • Figure 3B is a schematic diagram of the energy-depth mapping relationship of a hemispherical indentation
  • Figures 4A to 4D are schematic diagrams illustrating the relationship between input variables and crack length in the sample cross-section
  • Figures 5A and 5B are schematic diagrams showing the relationship between the crack length of the sample cross section and the loading stress of the sample
  • Figures 6A and 6B are schematic diagrams showing the relationship between the crack length and the indentation depth of the sample cross-section
  • FIG. 7 is a flowchart of step 104 in Figure 1;
  • Figure 8 is a flowchart of step 104 in Figure 1;
  • Figure 9 is a flowchart of step 105 in Figure 1;
  • Figure 10 is a schematic diagram of the stress corrosion prediction device with a dented heat transfer tube provided in an embodiment of this application;
  • Figure 11 is a schematic diagram of the structure of the electronic device provided in an embodiment of this application.
  • Stress corrosion cracking has long been a typical failure mode for austenitic stainless steel and nickel-based alloys, and a common problem that many industries, including the nuclear power sector, urgently need to solve. Stress corrosion cracking is the result of the interaction between materials, environment, and stress. Due to the complexity of its formation mechanism, there are many technical challenges in experimental research and engineering assessment of stress corrosion.
  • the surface damage state of stainless steel or nickel-based alloy heat transfer tubes affects the mechanical properties of the metal, such as wear resistance and stress corrosion resistance.
  • Thin-walled heat transfer tubes in various heat exchangers in engineering fields, including nuclear power inevitably suffer surface damage defects due to improper manufacturing processes, transportation impacts, installation collisions, jamming, and foreign object impacts during operation.
  • the main objective of this application is to establish a fundamental predictive model for stress corrosion of surface-dented heat transfer tubes that integrates multiple factors, and to further refine the model by combining it with non-destructive testing signals from in-service inspections in nuclear power plants.
  • This model can address the service safety assessment of such components. Therefore, there is an urgent need to conduct systematic research on surface-dented defects in nickel-based alloys and austenitic stainless steel, scientifically assess the impact of surface defects of different scales on the microstructure and corrosion performance of thin-walled tubes, establish corresponding predictive assessment models, and correlate these models with non-destructive testing results of heat transfer tubes from nuclear power plants for service performance evaluation.
  • the stress corrosion prediction method, apparatus, electronic device, and storage medium for dented heat transfer tubes provided in this application are specifically illustrated through the following embodiments. First, the stress corrosion prediction method for dented heat transfer tubes in this application is described.
  • Figure 1 discloses an optional flowchart of a method for predicting stress corrosion of a dented heat transfer tube.
  • the method in Figure 1 may include, but is not limited to, steps 101 to 105.
  • Step 101 Obtain the sample stress corrosion result data and multiple candidate stress corrosion influencing factors that cause the sample stress corrosion results;
  • Step 102 Select input variables from multiple candidate stress corrosion influencing factors
  • Step 103 Draw a box plot based on the sample stress corrosion results data and input variables, and determine the mapping relationship between the sample stress corrosion results data and input variables based on the distribution pattern of the quartile values in the box plot.
  • Step 104 Based on the mapping relationship between the sample stress corrosion results data and the input variables, a stress corrosion prediction model is constructed.
  • Step 105 Predict the target stress corrosion result data of the dented heat transfer tube using the stress corrosion prediction model.
  • Steps 101 to 105 as illustrated in this embodiment, firstly, input variables are selected from multiple candidate stress corrosion influencing factors. Secondly, a box plot is used to determine the mapping relationship between the sample stress corrosion result data and the input variables. Based on this mapping relationship, a stress corrosion prediction model is constructed, and then prediction is performed using this model. Specifically, the distribution pattern of the quartile values in the box plot can indicate the mapping relationship between the sample stress corrosion result data and the input variables, thus ensuring high accuracy of the constructed stress corrosion prediction model. Finally, the constructed stress corrosion prediction model can be used to predict the target stress corrosion result data of the dented heat transfer pipe. This embodiment improves the efficiency of stress corrosion prediction. Furthermore, the stress corrosion prediction model provided in this application has high prediction accuracy and high applicability.
  • the sample stress corrosion result data refers to the data obtained by subjecting a dented pipe (such as a dented heat transfer pipe) to stress corrosion in a corrosive environment.
  • a dented pipe such as a dented heat transfer pipe
  • the sample stress corrosion result data includes the sample oxide film thickness, the sample cross-sectional crack length, and the sample surface crack length.
  • the sample oxide film thickness of the dented heat transfer pipe is 1 mm after 100 hours in a corrosive environment.
  • sample dent type sample loading stress
  • sample dent depth number of impacts
  • sample cross-dents are categorical variables
  • sample loading stress and sample dent depth are numerical variables.
  • sample dent types include hemispherical and triangular prism types.
  • the number of impacts includes one and more than one.
  • Sample cross-dents include cross-dents and non-cross-dents.
  • Sample loading stress is a positive number, for example, within [0,2], and the unit is multiples.
  • Sample dent depth is a positive number, for example, within [0,1], and the unit is mm.
  • step 101 includes:
  • Step 201 Obtain multiple candidate factors affecting stress corrosion
  • Step 202 Based on multiple candidate stress corrosion influencing factors, indentation is created on the initial sample to obtain the target sample;
  • Step 203 Place the target sample in a preset corrosion environment and acquire corrosion status data of the target sample at predetermined intervals.
  • Step 204 Perform statistical analysis on all acquired corrosion state data to obtain sample stress corrosion result data.
  • multiple candidate stress corrosion influencing factors include sample dent type, sample loading stress, sample dent depth, number of sample impacts, and sample cross-dents. These candidate stress corrosion influencing factors can be obtained by consulting relevant stress corrosion test standards.
  • the initial sample (also called the initial specimen) can be prepared according to the stress corrosion test standard, using heat transfer tube materials, including C-rings, U-bends, and reverse U-bends. Indentation defects are prepared on the outer surface of the initial sample to obtain the target sample.
  • the relevant technologies address stress corrosion testing and evaluation of thin-walled heat transfer tubes with surface defects, specifically stress corrosion testing and evaluation of tubes with surface scratches.
  • the methods and mechanisms of surface scratches differ from those of surface indentations. Compared to scratches, the pre-production of surface indentations is more difficult, typically due to the challenge in controlling the defect morphology and depth.
  • multiple stress corrosion influencing factors include sample indentation type and sample indentation depth
  • step 202 includes include:
  • the indentation depth of the sample is mapped to obtain the target energy
  • the target sample is obtained by applying the target energy to the initial sample using an impact method and a punch.
  • this embodiment primarily employs impact methods to provide impact energy for preparing dent defects.
  • Impact methods include energy control techniques such as free fall, drop hammer impact, pressing, and pendulum impact.
  • energy control techniques such as free fall, drop hammer impact, pressing, and pendulum impact.
  • the mapping relationship between energy and depth is solidified, and surface dent depths for different samples are determined based on different energies. For example, assuming the impact method is pendulum impact, the corresponding dent depths under different energies are first collected. Then, data fitting techniques (such as least squares method) can be used to determine the mapping relationship, finally obtaining the first energy-dent depth mapping relationship corresponding to the pendulum impact method.
  • the advantage of the above embodiments is that they fully demonstrate the different energy and dent depth mapping relationships under different impact methods, thereby improving the accuracy of manufacturing target samples.
  • the first energy-dimple depth mapping relationship includes candidate energy-dimple depth mapping relationships corresponding to multiple candidate dimple types; the process of mapping the sample dimple depth using the first energy-dimple depth mapping relationship to obtain the target energy may include:
  • the candidate energy and indentation depth mapping relationship corresponding to multiple candidate indentation types is filtered to obtain the second energy and indentation depth mapping relationship;
  • the indentation depth of the sample is mapped to obtain the target energy.
  • a spherical punch and a triangular prism punch are selected to prepare indentations with local morphologies of circles and stripes, respectively.
  • the punch material is stainless steel with a hardness matching that of nickel-based 690 alloy.
  • D depth in mm
  • E energy in J.
  • D depth
  • E energy in J
  • D depth
  • E energy in J
  • the advantage of the above embodiments is that they can fully reflect the different energy and dent depth mapping relationships corresponding to different dent types under the same impact mode, thereby improving the accuracy of determining the target energy.
  • the corrosive environment refers to the environment in which the target sample is located, such as the primary loop of a nuclear power plant, the secondary loop of a nuclear power plant, or the corrosion loop of a typical chemical heat exchanger.
  • the target sample will undergo stress corrosion in the corrosive environment, thus exhibiting different corrosion states.
  • Corrosion state data can be obtained by sampling and analyzing the target sample. Specifically, corrosion state data of the target sample can be acquired at predetermined intervals (e.g., every 100 hours).
  • the corrosion state data includes surface morphology, crack initiation and propagation states, crack initiation area, initiation time, propagation rate, and crack length.
  • sample stress corrosion result data includes sample oxide film thickness, sample cross-sectional crack length, and sample surface crack length. And sample propagation rate, etc.
  • sample oxide film thickness can yield the sample oxide film thickness of the target sample.
  • statistical analysis of all acquired crack lengths can yield the sample cross-sectional crack length or sample surface crack length of the target sample.
  • statistical analysis of all acquired propagation rates can yield the sample propagation rate of the target sample.
  • statistical analysis can yield other types of sample stress corrosion results data, which will not be elaborated here.
  • steps 201 to 204 by first creating the dent and then analyzing the stress corrosion results of the sample, the dent defect can be effectively created and analyzed.
  • step 102 of some embodiments through analysis and sorting of multiple candidate stress corrosion influencing factors, a total of several variables are included, such as sample indentation type, sample loading stress, sample indentation depth, number of sample impacts, and sample cross-indentations.
  • sample indentation type, sample indentation depth, and sample loading stress are key variables affecting the cross-sectional crack length.
  • step 102 includes:
  • each candidate stress corrosion influencing factor is analyzed and evaluated to obtain the influence degree. If the influence degree is greater than the influence degree threshold, the candidate stress corrosion influencing factor is determined as an input variable.
  • the sample indentation type, sample indentation depth, and sample loading stress can be selected as input variables, and the sample cross-sectional crack length can be selected as an output variable.
  • a box plot can be used to reflect the central location and distribution range of one or more sets of continuous quantitative data. This embodiment utilizes the distribution patterns of quartile values in the box plot to determine the mapping relationship between sample stress corrosion results and input variables.
  • the mapping relationship includes a positive correlation between sample stress corrosion results and sample loading stress, and a quadratic curve relationship between sample stress corrosion results and sample indentation depth.
  • the horizontal axis represents the sample loading stress (in multiples), and the vertical axis represents the crack length of the sample cross-section (in mm).
  • the box plot in Figure 5A shows that although the overall data sample is discretely distributed, the quartile values generally exhibit a linear relationship, leading to the conclusion that the crack length of the sample cross-section is positively correlated with the sample loading stress. Specifically, when the sample loading stress is 0.7 times, the quartile value of the crack length is approximately 0.1; when the sample loading stress is 0.8 times, the quartile value is approximately 1.8, and so on.
  • Figure 5B also shows the quantiles of the sample loading stress (in multiples) on the horizontal axis and the quantiles of the crack length of the sample cross-section (in mm).
  • Figure 5B shows that under the same indentation depth, the crack length of the sample cross-section continuously increases with the increase of the sample loading stress.
  • the horizontal axis represents the sample indentation depth (in mm).
  • the vertical axis represents the crack length of the sample cross-section (in mm).
  • the quartile values exhibit a generally curvilinear relationship, leading to the conclusion that the crack length of the sample cross-section has a quadratic relationship with the sample's applied stress.
  • the horizontal axis represents the sample's indentation depth (in mm)
  • the vertical axis represents the sample's cross-section crack length (in mm).
  • Figure 6B shows that under the same applied stress, as the sample's indentation depth increases, the crack length of the sample cross-section first increases and then decreases.
  • a stress corrosion prediction model can be constructed based on the mapping relationship between sample stress corrosion result data and input variables. Referring to FIG7, in one embodiment, step 104 includes:
  • Step 701 Based on the positive correlation between the sample stress corrosion results and the sample loading stress, construct the first formula
  • Step 702 Based on the quadratic relationship between the sample stress corrosion results and the sample indentation depth, construct the second formula
  • Step 703 Determine the stress corrosion prediction model based on the first formula and the second formula.
  • k1 , k2 , k3 , and b3 need to conform to different numerical ranges at different confidence levels. For example, at a 95% confidence level, k1 should be within [1.531, 2.291], k2 within [-1.797, 0.2214], k3 within [2.165, 4.382], and b3 within [-1.005, 1.088].
  • steps 701 to 703 The advantage of the embodiments of steps 701 to 703 is that different mapping relationships first determine the corresponding formulas, and then all formulas are fused to obtain the stress corrosion prediction model, making the model construction process relatively simple and easy to implement.
  • step 104 may further include:
  • Step 801 Integrate the first formula and the second formula to obtain the first integrated formula
  • Step 802 Based on the sample loading stress and the sample indentation depth, construct a third formula.
  • the third formula is used to indicate the multiplicative relationship between the sample loading stress and the sample indentation depth.
  • Step 803 Determine the stress corrosion prediction model based on the first integrated formula and the third formula.
  • the coefficients in the stress corrosion prediction model can be estimated based on experimental data; for specific estimation methods, please refer to the examples in Table 1 below.
  • the benefit of this implementation is that it can fully consider the interaction between sample loading stress and sample indentation depth on the sample stress corrosion results data, further improving the prediction accuracy and universality of the stress corrosion prediction model.
  • step 803 includes:
  • the sample corrosion environment factor is ⁇ .
  • the benefit of this implementation is that it can fully reflect the impact of corrosive environmental factors on the stress corrosion results data of the samples, and further improve the prediction accuracy and universality of the stress corrosion prediction model.
  • the crack length of the sample cross-section is positively correlated with the sample loading stress; the crack length of the sample cross-section and the sample indentation depth exhibit a quadratic relationship; different corrosion environments (e.g., different corrosive media) are addressed by introducing a sample corrosion environment factor ⁇ ; the sample indentation type is incorporated into the model formula as a piecewise function to reflect its influence.
  • the sample loading stress corresponds to a linear function
  • the sample indentation depth corresponds to a quadratic function.
  • L is the crack length of the sample cross section
  • x is the dent depth of the sample
  • y is the loading stress of the sample
  • ⁇ is the corrosion environment factor of the sample
  • p0 , p1 , p2 , p3 , and p4 are all coefficients
  • ⁇ is the random error.
  • a multi-factor coupled stress corrosion prediction model was constructed, incorporating factors such as sample indentation depth, sample loading stress, and sample indentation type.
  • factors such as sample indentation depth, sample loading stress, and sample indentation type.
  • regression analysis was used to determine the parameters in the general formula of the prediction model.
  • a comprehensive evaluation of goodness of fit and minimum error was then performed to finalize the prediction model.
  • Table 1 shows the estimated values and confidence intervals of the coefficients of the prediction model constructed using the experimental data (including stress corrosion results data and multiple input variables) of this embodiment.
  • R2 represents the prediction accuracy of the stress corrosion prediction model
  • RMSE represents the root mean square error of the stress corrosion prediction model.
  • a larger R2 indicates higher prediction accuracy, while a smaller RMSE indicates a smaller root mean square error, thus indicating higher prediction accuracy.
  • p0 -0.827
  • p1 3.274
  • p2 1.911
  • p3 -0.7877
  • p4 -3.627
  • R2 0.82
  • RMSE 0.0782.
  • Each coefficient falls within its corresponding confidence interval for the regression coefficient and has a 95% confidence level.
  • the target stress corrosion result data of the dented heat transfer tube is predicted using a stress corrosion prediction model. Specifically, if the sample stress corrosion result data includes the sample oxide film thickness, then the target stress corrosion result data includes the target oxide film thickness; if the sample stress corrosion result data includes the sample cross-sectional crack length, then the target stress corrosion result data includes the target cross-sectional crack length; if the sample stress corrosion result data includes the sample surface crack length, then the target stress corrosion result data includes the target oxide film thickness.
  • the force corrosion results data include the crack length on the target surface.
  • step 105 includes:
  • Type detection is performed on the dented heat transfer tube to determine the target dent type
  • the target stress corrosion result data is obtained by predicting the heat transfer tube with dent using the candidate stress corrosion prediction model corresponding to the sample dent type.
  • detecting the dents on a dented heat transfer tube reveals that the target dent type is triangular prism. If the sample dent type includes triangular prisms, the heat transfer tube can be predicted using a candidate stress corrosion prediction model corresponding to the triangular prism type, ultimately yielding the target stress corrosion result data.
  • step 105 may further include:
  • Step 901 Obtain the correspondence between the sample non-destructive testing signal and multiple input variables
  • Step 902 Based on the correspondence, perform variable transformation on the stress corrosion prediction model to obtain the signal stress corrosion prediction model;
  • Step 903 Perform non-destructive testing on the dented heat transfer tube to obtain the target non-destructive testing signal
  • Step 904 Input the target non-destructive testing signal into the signal stress corrosion prediction model to predict corrosion and obtain the target stress corrosion result data.
  • the dent distribution characteristics (such as dent contour and dent depth) of the target sample (e.g., heat transfer tube sample) prepared above can be used, combined with common non-destructive testing methods currently used in the manufacturing, installation, and operation phases of nuclear power heat transfer tubes, to prepare calibration sample tubes.
  • the dent depth range of the calibration sample tubes must cover the dent defects of all samples used for corrosion testing.
  • NDT Non-destructive testing refers to the use of different NDT techniques to evaluate the quality of materials or components. These techniques may include ultrasonic testing, X-ray testing, eddy current testing, etc. These techniques can detect defects, cracks, or other undesirable conditions in materials or components.
  • eddy current testing pre-fabrication defects of all calibration sample tubes are tested, and eddy current signal impedance diagrams are recorded to obtain the correspondence between the sample eddy current signal and multiple input variables, such as the correspondence between the sample eddy current signal and the dent depth, applied stress, and dent type of the calibration sample tube.
  • step 902 based on the obtained correspondence, the input variables in the stress corrosion prediction model are equivalently transformed into sample non-destructive testing signals, thereby obtaining the signal stress corrosion prediction model.
  • the input variable is converted into a sample eddy current testing signal.
  • the signal stress corrosion prediction model during eddy current testing of heat transfer tubes in nuclear power plant steam generators, the potential stress corrosion cross-sectional crack length of the dented heat transfer tube can be quantitatively assessed directly using the actual eddy current signal. This allows for a subsequent integrity assessment to determine whether the dented heat transfer tube needs to be plugged.
  • quantitative crack data (such as oxide film thickness, cross-sectional crack length, and surface crack length) can be predicted using the stress corrosion prediction model. Based on the predicted quantitative crack data, the prediction results are used as input parameters according to specifications to conduct an integrity assessment of the defective heat transfer tube, achieving the final evaluation and analysis of the heat transfer tube's service performance.
  • the benefits of this embodiment include: 1. It proposes a method for constructing a stress corrosion prediction model, which is simple to construct, has high prediction accuracy, reduces the experimental cycle, and greatly compresses experimental costs, providing an optimized solution for stress corrosion prediction and evaluation of typical components in related industries. 2.
  • the proposed method of first converting non-destructive (eddy current testing) detection signals into input parameters for the stress corrosion prediction model, and then directly detecting and evaluating the crack initiation and propagation trend of stress corrosion online, can improve the efficiency and economy of inspection and maintenance of heat transfer tubes in nuclear power plant steam generators.
  • this application embodiment also provides a stress corrosion prediction device for a dented heat transfer tube, which can realize the above-described stress corrosion prediction method for a dented heat transfer tube.
  • Figure 10 is a block diagram of the module structure of the stress corrosion prediction device for a dented heat transfer tube provided in this application embodiment. The device includes:
  • the acquisition module 1001 is used to acquire sample stress corrosion result data and multiple candidate stress corrosion influencing factors that cause the sample stress corrosion results;
  • the filtering module 1002 is used to filter input variables from multiple candidate stress corrosion influencing factors
  • the module 1003 is used to draw a box plot based on the sample stress corrosion result data and input variables, and to determine the mapping relationship between the sample stress corrosion result data and the input variables based on the distribution pattern of the quartile values in the box plot.
  • Module 1004 is used to construct a stress corrosion prediction model based on the mapping relationship between sample stress corrosion result data and input variables.
  • Prediction module 1005 is used to predict the target stress corrosion results data of the dented heat transfer tube through the stress corrosion prediction model.
  • the specific implementation of the stress corrosion prediction device for the dented heat transfer tube is basically the same as the specific implementation of the stress corrosion prediction method for the dented heat transfer tube described above, and will not be repeated here.
  • This application also provides an electronic device, which includes: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for communication between the processor and the memory.
  • the program When the program is executed by the processor, it implements the stress corrosion prediction method for the dented heat transfer tube described above.
  • This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.
  • the electronic device includes:
  • the processor 1101 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.
  • a general-purpose CPU Central Processing Unit
  • microprocessor microprocessor
  • ASIC application-specific integrated circuit
  • the memory 1102 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM).
  • the memory 1102 can store the operating system and other application programs.
  • the relevant program code is stored in the memory 1102 and is called and executed by the processor 1101 to execute the stress corrosion prediction method for the dented heat transfer tube of this application embodiment.
  • Input/output interface 1103 is used to implement information input and output
  • Communication interface 1104 is used to enable communication and interaction between this device and other devices, and can be achieved via wired means (e.g., USB, etc.). Communication can be achieved via wired connections (such as network cables) or wireless methods (such as mobile networks, Wi-Fi, Bluetooth, etc.).
  • wired means e.g., USB, etc.
  • Communication can be achieved via wired connections (such as network cables) or wireless methods (such as mobile networks, Wi-Fi, Bluetooth, etc.).
  • Bus 1105 transmits information between various components of the device (e.g., processor 1101, memory 1102, input/output interface 1103, and communication interface 1104);
  • the processor 1101, memory 1102, input/output interface 1103 and communication interface 1104 are connected to each other within the device via bus 1105.
  • This application embodiment also provides a storage medium, which is a computer-readable storage medium for computer-readable storage.
  • the storage medium stores one or more programs, which can be executed by one or more processors to implement the above-described stress corrosion prediction method for dented heat transfer tubes.
  • Memory as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs.
  • memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device.
  • memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the device embodiments described above are merely illustrative.
  • the units described as separate components may or may not be physically separate; 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.
  • At least one item means one or more, and “more than one” means two or more.
  • “And/or” describes the relationship between related objects, indicating that three relationships can exist. For example, “A and/or B” can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character “/” generally indicates that the preceding and following related objects are in an “or” relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items.
  • At least one of a, b, or c can represent: a, b, c, "a and b", “a and c", “b and c", or "a and b and" "c", where a, b, and c can be a single number or multiple numbers.
  • the disclosed apparatus and methods can be implemented in other ways.
  • the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods.
  • multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.
  • the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
  • the units described above as separate components may or may not be physically separate.
  • the components shown as units 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 units can be selected to achieve the purpose of this embodiment according to actual needs.
  • the functional units in the various embodiments of this application 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 as a software functional unit.
  • the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.
  • the aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

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Abstract

本申请公开了一种带凹痕传热管的应力腐蚀预测方法和装置、设备及介质，涉及应力腐蚀评估技术领域。该方法包括：获取样本应力腐蚀结果数据和引起样本应力腐蚀结果的多个候选应力腐蚀影响因素；从多个候选应力腐蚀影响因素筛选出输入变量；根据所述样本应力腐蚀结果数据和所述输入变量绘制箱线图，且根据所述箱线图中的四分位数值的分布规律，确定所述样本应力腐蚀结果数据与所述输入变量的映射关系；根据所述样本应力腐蚀结果数据与所述输入变量的映射关系，构建得到应力腐蚀预测模型；通过所述应力腐蚀预测模型预测所述带凹痕传热管的目标应力腐蚀结果数据。本申请能够提高应力腐蚀预测的实现效率。

Description

带凹痕传热管的应力腐蚀预测方法和装置、设备及介质 技术领域
本申请涉及应力腐蚀评估技术领域，特别涉及一种带凹痕传热管的应力腐蚀预测方法和装置、电子设备及计算机可读存储介质。
背景技术
应力腐蚀一直以来是奥氏体不锈钢和镍基合金材料的典型失效模式，也是包括核电领域在内的诸多行业亟需解决的共性难题。应力腐蚀开裂是材料、环境和应力三者交互作用的结果，由于其产生机理的复杂性，导致针对应力腐蚀的试验研究、工程评估存在较多技术难题。不锈钢或镍基合金传热管类部件的表面损伤状态，影响着金属的机械性能，如耐磨性及耐应力腐蚀的性能。包括核电在内的工程领域内各类热交换器薄壁传热管，由于管子制造工艺不当、运输撞击、安装碰撞、卡涩以及运行期间异物撞击等原因不可避免的造成传热管表面损伤缺陷，这些损伤的存在加剧了传热管应力腐蚀失效风险，给设备安全服役带来隐患。
目前，由于影响部件应力腐蚀开裂的因素众多，对典型部件在特定服役条件下应力腐蚀(比如应力腐蚀裂纹扩展)的预测很难量化评估，一般需要开展大量试验研究，周期长。
因此，有必要解决现有技术中应力腐蚀预测技术难以构造的问题。
发明内容
本申请旨在至少解决现有技术中存在的技术问题之一。为此，本申请提出了一种带凹痕传热管的应力腐蚀预测方法和装置、电子设备及计算机可读存储介质，它能够提高应力腐蚀预测的实现效率。
为实现上述目的，本申请实施例的第一方面提出了一种带凹痕传热管的应力腐蚀预测方法，所述方法包括：
获取样本应力腐蚀结果数据和引起样本应力腐蚀结果的多个候选应力腐蚀影响因素；
从多个候选应力腐蚀影响因素筛选出输入变量；
根据所述样本应力腐蚀结果数据和所述输入变量绘制箱线图，且根据所述箱线图中的四分位数值的分布规律，确定所述样本应力腐蚀结果数据与所述输入变量的映射关系；
根据所述样本应力腐蚀结果数据与所述输入变量的映射关系，构建得到应力腐蚀预测模型；
通过所述应力腐蚀预测模型预测所述带凹痕传热管的目标应力腐蚀结果数据。
可选地，所述获取样本应力腐蚀结果数据和引起样本应力腐蚀结果的多个候选应力腐蚀影响因素，包括：
获取多个候选应力腐蚀影响因素；
根据多个所述候选应力腐蚀影响因素对初始样件进行凹痕制作，得到目标样件；
将所述目标样件放入预设的腐蚀环境，每隔预定周期获取所述目标样件的腐蚀状态数据；
对所有获取到的所述腐蚀状态数据进行统计分析，得到所述样本应力腐蚀结果数据。
可选地，多个所述候选应力腐蚀影响因素包括样本凹痕类型和样本凹痕深度；
所述根据多个所述候选应力腐蚀影响因素对初始样件进行凹痕制作，得到目标样件，包括：
根据所述样本凹痕类型确定冲头；
获取冲击方式，且获取与所述冲击方式对应的第一能量与凹痕深度映射关系；
利用所述第一能量与凹痕深度映射关系，对所述样本凹痕深度进行映射，得到目标能量；
利用所述冲击方式和所述冲头对所述初始样件施加所述目标能量的冲击，得到所述目标样件。
可选地，所述第一能量与凹痕深度映射关系包括与多个候选凹痕类型对应的候选能量与凹痕深度映射关系；
所述利用所述第一能量与凹痕深度映射关系，对所述样本凹痕深度进行映射，得到目标能量，包括：
根据所述样本凹痕类型，对多个所述候选凹痕类型对应的候选能量与凹痕深度映射关系进行筛选，得到第二能量与凹痕深度映射关系；
利用所述第二能量与凹痕深度映射关系，对所述样本凹痕深度进行映射，得到所述目标能量。
可选地，所述输入变量包括样本加载应力、和样本凹痕深度，所述映射关系包括所述样本应力腐蚀结果数据与所述样本加载应力呈正相关关系、和所述样本应力腐蚀结果数据与所述样本凹痕深度呈二次曲线关系；
所述根据所述样本应力腐蚀结果数据与输入变量的映射关系，构建得到所述应力腐蚀预测模型，包括：
根据所述样本应力腐蚀结果数据与所述样本加载应力呈正相关关系，构建第一公式；
根据所述样本应力腐蚀结果数据与所述样本凹痕深度呈二次曲线关系，构建第二公式；
根据所述第一公式和所述第二公式，确定所述应力腐蚀预测模型。
可选地，所述根据所述样本应力腐蚀结果数据与输入变量的映射关系，构建得到所述应力腐蚀预测模型，还包括：
将所述第一公式和所述第二公式进行整合，得到第一整合公式；
根据所述样本加载应力与所述样本凹痕深度，构建第三公式，所述第三公式用于指示所述样本加载应力与所述样本凹痕深度的乘法关系；
根据所述第一整合公式和所述第三公式，确定所述应力腐蚀预测模型。
可选地，所述根据所述第一整合公式和所述第三公式，确定所述应力腐蚀预测模型，包括：
将所述第一整合公式和所述第三公式进行整合，得到第二整合公式；
获取样本腐蚀环境因子；
将所述样本腐蚀环境因子和所述第二整合公式进行相乘，得到所述应力腐蚀预测模型。
可选地，所述样本应力腐蚀结果数据包括样本截面裂纹长度，所述应力腐蚀预测模型如下公式所示：
L＝β*(p0+p1x+p2y+p3x2+p4xy+ε)；
其中，L为所述样本截面裂纹长度、x为所述样本凹痕深度，y为所述样本加载应力，β为所述样本腐蚀环境因子，p0、p1、p2、p3、和p4均为系数，ε为随机误差。
可选地，所述输入变量包括样本凹痕类型，所述应力腐蚀预测模型包括与所述样本凹痕类型对应的候选应力腐蚀预测模型；
所述通过所述应力腐蚀预测模型预测所述带凹痕传热管的目标应力腐蚀结果数据，包括：
对所述带凹痕传热管进行类型检测，得到目标凹痕类型；
若所述目标凹痕类型与所述样本凹痕类型一致，则通过与所述样本凹痕类型对应的候选应力腐蚀预测模型对所述带凹痕传热管进行预测，得到所述目标应力腐蚀结果数据。
可选地，所述通过所述应力腐蚀预测模型预测所述带凹痕传热管的目标应力腐蚀结果数据，包括：
获取样本无损检测信号与多个所述输入变量的对应关系；
根据所述对应关系，对所述应力腐蚀预测模型进行变量转化，得到信号应力腐蚀预测模型；
对所述带凹痕传热管进行无损检测，得到目标无损检测信号；
将所述目标无损检测信号输入所述信号应力腐蚀预测模型进行腐蚀预测，得到所述目标应力腐蚀结果数据。
为实现上述目的，本申请的第二方面提出了一种带凹痕传热管的应力腐蚀预测装置，所述装置包括：
获取模块，用于获取样本应力腐蚀结果数据和引起样本应力腐蚀结果的多个候选应力腐蚀影响因素；
筛选模块，用于从多个候选应力腐蚀影响因素筛选出输入变量；
确定模块，用于根据所述样本应力腐蚀结果数据和所述输入变量绘制箱线图，且根据所述箱线图中的四分位数值的分布规律，确定所述样本应力腐蚀结果数据与所述输入变量的映射关系；
构建模块，用于根据所述样本应力腐蚀结果数据与所述输入变量的映射关系，构建得到应力腐蚀预测模型；
预测模块，用于通过所述应力腐蚀预测模型预测所述带凹痕传热管的目标应力腐蚀结果数据。
为实现上述目的，本申请实施例的第三方面提出了一种电子设备，所述电子设备包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现上述第一方面所述的带凹痕传热管的应力腐蚀预测方法。
为实现上述目的，本申请的第四方面提出了一种计算机可读存储介质，所述存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述第一方面所述的带凹痕传热管的应力腐蚀预测方法。
本申请实施例中，先从多个候选应力腐蚀影响因素筛选出输入变量，利用箱线图确定样本应力腐蚀结果数据与输入变量的映射关系，从而基于映射关系构建出应力腐蚀预测模型，进而通过应力腐蚀预测模型进行预测。具体地，箱线图中的四分位数值的分布规律可以指示样本应力腐蚀结果数据与输入变量的映射关系，从而基于映射关系构建出应力腐蚀预测模型。最后，可以通过构建的应力腐蚀预测模型预测带凹痕传热管的目标应力腐蚀结果数据。本申请实施例能够提高应力腐蚀预测的实现效率。
本申请的附加方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本申请的实践了解到。
附图说明
图1是本申请实施例提供的带凹痕传热管的应力腐蚀预测方法的流程图；
图2是图1中的步骤101的流程图；
图3A是三棱柱形凹痕的能量与深度映射关系的示意图；
图3B是半球形凹痕的能量与深度映射关系的示意图；
图4A至图4D是输入变量与样本截面裂纹长度之间的规律的示意图；
图5A至图5B是样本截面裂纹长度与样本加载应力的关系的示意图；
图6A至图6B是样本截面裂纹长度与样本凹痕深度的关系的示意图；
图7是图1中的步骤104的流程图；
图8是图1中的步骤104的流程图；
图9是图1中的步骤105的流程图；
图10是本申请实施例提供的带凹痕传热管的应力腐蚀预测装置的结构示意图；
图11是本申请实施例提供的电子设备的结构示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。
需要说明的是，虽然在装置示意图中进行了功能模块划分，在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于装置中的模块划分，或流程图中的顺序执行所示出或描述的步骤。说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。
除非另有定义，本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人 员通常理解的含义相同。本文中所使用的术语只是为了描述本申请实施例的目的，不是旨在限制本申请。
应力腐蚀一直以来是奥氏体不锈钢和镍基合金材料的典型失效模式，也是包括核电领域在内的诸多行业亟需解决的共性难题。应力腐蚀开裂是材料、环境和应力三者交互作用的结果，由于其产生机理的复杂性，导致针对应力腐蚀的试验研究、工程评估存在较多技术难题。不锈钢或镍基合金传热管类部件的表面损伤状态，影响着金属的机械性能，如耐磨性及耐应力腐蚀的性能。包括核电在内的工程领域内各类热交换器薄壁传热管，由于管子制造工艺不当、运输撞击、安装碰撞、卡涩以及运行期间异物撞击等原因不可避免的造成传热管表面损伤缺陷，这些损伤的存在加剧了传热管应力腐蚀失效风险，给设备安全服役带来隐患。同时，由于影响部件应力腐蚀开裂的因素众多，对典型部件在特定服役条件下应力腐蚀敏感性及应力腐蚀裂纹扩展的预测很难量化评估，需要开展大量试验研究，周期长，成本高。至今业界仍缺乏针对带有表面缺陷的典型镍基合金(如690、600等)和奥氏体不锈钢材料应力腐蚀裂纹萌生和开裂的预测评估模型。
目前表面带凹痕损伤的应力腐蚀数据极其缺乏，严重制约了带凹痕类表面缺陷传热管应力腐蚀评估预测。同时，由于应力腐蚀影响因素的复杂性以及服役环境的多样性，各影响因素之间具有高度非线性和相互强耦合的关系，当前部分薄壁管应力腐蚀裂纹萌生及扩展预测评估中，一般是通过数据参数关联或机理建模的方法来实现的，很难准确表征裂纹萌生及开裂与各影响参数之间的映射关系，也同样无法支撑行业内对带表面损伤镍基合金和奥氏体不锈钢薄壁传热管的应力腐蚀预测评估。
基于此，本申请主要目的是建立融合多因素的表面带凹痕传热管应力腐蚀基础预测模型，并进一步结合核电厂在役检查无损检测信号，修正模型，可解决此类部件服役安全评估。因此，急需针对镍基合金和奥氏体不锈钢凹痕类表开展系统研究，科学地评估不同尺度凹痕表面缺陷对薄壁管表面微观组织及腐蚀性能的影响，建立对应的预测评估模型，并关联核电厂传热管无损检测结果进行服役性能评估。
本申请提供的带凹痕传热管的应力腐蚀预测方法和装置、电子设备及存储介质，具体通过如下实施例进行说明，首先描述本申请中的带凹痕传热管的应力腐蚀预测方法。
请参照图1，图1公开了一种带凹痕传热管的应力腐蚀预测方法的一个可选的流程图，图1中的方法可以包括但不限于包括步骤101至步骤105。
步骤101，获取样本应力腐蚀结果数据和引起样本应力腐蚀结果的多个候选应力腐蚀影响因素；
步骤102，从多个候选应力腐蚀影响因素筛选出输入变量；
步骤103，根据样本应力腐蚀结果数据和输入变量绘制箱线图，且根据箱线图中的四分位数值的分布规律，确定样本应力腐蚀结果数据与输入变量的映射关系；
步骤104，根据样本应力腐蚀结果数据与输入变量的映射关系，构建得到应力腐蚀预测模型；
步骤105，通过应力腐蚀预测模型预测带凹痕传热管的目标应力腐蚀结果数据。
本申请实施例所示意的步骤101至步骤105，先从多个候选应力腐蚀影响因素筛选出输入变量，其次利用箱线图确定样本应力腐蚀结果数据与输入变量的映射关系，从而基于映射关系构建出应力腐蚀预测模型，进而通过应力腐蚀预测模型进行预测。具体地，箱线图中的四分位数值的分布规律可以指示样本应力腐蚀结果数据与输入变量的映射关系，从而基于构建出的应力腐蚀预测模型能够准确性较高。最后，可以通过构建的应力腐蚀预测模型预测带凹痕传热管的目标应力腐蚀结果数据。本申请实施例能够提高应力腐蚀预测的实现效率。此外，本申请提供的应力腐蚀预测模型具有较高的预测准确性，且适用性较高。
在一些实施例的步骤101中，样本应力腐蚀结果数据是指某个带凹痕的管件(比如带凹痕传热管)在腐蚀环境下经受应力腐蚀得到的数据。比如，样本应力腐蚀结果数据包括样本氧化膜厚度、样本截面裂纹长度和样本表面裂纹长度。以样本氧化膜厚度为例，带凹痕传热管在腐蚀环境下，100小时后样本氧化膜厚度为1mm。
带凹痕的管件经受的应力腐蚀程度不同，则引起的样本应力腐蚀结果不同。应力腐蚀程度不同体现在候选应力腐蚀影响因素上不同，即候选应力腐蚀影响因素表征影响应力腐蚀程度的因子或因素。多个候选应力腐蚀影响因素包括样本凹痕类型、样本加载应力、样本凹痕深度、样本冲击次数、样本交叉凹痕等。其中，样本凹痕类型、样本冲击次数、和样本交叉凹痕属于分类变量，样本加载应力、和样本凹痕深度属于数值变量。例如，样本凹痕类型包括半球型和三棱柱型等。样本冲击次数包括1次和大于1次。样本交叉凹痕包括交叉和非交叉。样本加载应力的数值正数，比如在[0,2]内，单位为倍。样本凹痕深度的数值为正数，比如在[0,1]内，单位为mm。
为此，在一实施例中，参照图2，步骤101包括：
步骤201，获取多个候选应力腐蚀影响因素；
步骤202，根据多个候选应力腐蚀影响因素对初始样件进行凹痕制作，得到目标样件；
步骤203，将目标样件放入预设的腐蚀环境，每隔预定周期获取目标样件的腐蚀状态数据；
步骤204，对所有获取到的腐蚀状态数据进行统计分析，得到样本应力腐蚀结果数据。
在步骤201，一般对于传热管带有凹痕缺陷的应力腐蚀试验，多个候选应力腐蚀影响因素包括样本凹痕类型、样本加载应力、样本凹痕深度、样本冲击次数、样本交叉凹痕等。可以通过查找相关应力腐蚀试验标准获取到多个候选应力腐蚀影响因素。
在步骤202，初始样件(也称初始试样)可以按照应力腐蚀试验标准，选取传热管材料制备包括C型环，U弯、反U弯样件。在初始样件的外表面制备凹痕缺陷，得到目标样件。
相关技术针对薄壁传热管带有表面缺陷的应力腐蚀试验及评估，具体针对的是带有表面划伤的应力腐蚀试验及评估。然而表面划伤的方法与机理不同于表面凹痕缺陷。相比划伤，表面凹痕缺陷的预制更难实现，通常是缺陷形貌和深度较难控制。也缺乏不同类型表面损伤对镍基和不锈钢部件材料应力腐蚀裂纹萌生及开裂的预测模型，用于指导薄壁管应力腐蚀损伤的预测及评估。
在一实施例中，多个应力腐蚀影响因素包括样本凹痕类型和样本凹痕深度，步骤202包 括：
根据样本凹痕类型确定冲头；
获取冲击方式，且获取与冲击方式对应的第一能量与凹痕深度映射关系；
利用第一能量与凹痕深度映射关系，对样本凹痕深度进行映射，得到目标能量；
利用冲击方式和冲头对初始样件施加目标能量的冲击，得到目标样件。
具体地，本实施例制备凹痕缺陷主要采用的是冲击方式提供冲击能量的手段。冲击方式包括能量控制法，包括自由落体、落锤冲击、压制及摆锤冲击等方式。通过摸索不同冲击方式在传热管外表面处的能量与深度的关系，固化能量与深度的映射关系，进而根据不同的能量制定不同样本凹痕深度的表面凹痕。例如，假定冲击方式为摆锤冲击方式，先收集不同能量下对应的凹痕深度，然后可以利用数据拟合技术(比如最小二乘法)确定映射关系，最后得到摆锤冲击方式对应的第一能量与凹痕深度映射关系。
上述实施例的益处在于，充分体现不同冲击方式下对应有不同的能量与凹痕深度映射关系，提高了制作目标样件的准确性。
在一实施例中，第一能量与凹痕深度映射关系包括与多个候选凹痕类型对应的候选能量与凹痕深度映射关系；上述利用第一能量与凹痕深度映射关系，对样本凹痕深度进行映射，得到目标能量的过程，可以包括：
根据样本凹痕类型，对多个候选凹痕类型对应的候选能量与凹痕深度映射关系进行筛选，得到第二能量与凹痕深度映射关系；
利用第二能量与凹痕深度映射关系，对样本凹痕深度进行映射，得到目标能量。
在一例子中，以采用摆锤冲击方式以制备半球形和三棱柱形缺陷为例，选择球形冲头和三棱柱形冲头，分别制备局部形貌为圆形和条形的凹痕。其中，冲头的材质为硬度与镍基690合金匹配的不锈钢材质。冲头后端尺寸可变，用以控制质量。材料选择：较硬材料，避免撞击磨损的影响。质量范围：10至50g；冲头角度：α＝60°；尺寸设计：L(长度)≤2mm，H(高度)≤1mm。如图3A所示，若凹痕为三棱柱形，则能量与深度映射关系为D＝92E+48。其中，D表示深度，单位mm，E表示能量，单位J。如图3B所示，若凹痕为半球形，则深度与能量映射关系为D＝2.7E2+58E+51。其中，D表示深度，E表示能量。比如，样本凹痕类型为三棱柱型、且样本凹痕深度为200mm，则利用D＝92E+48，计算得到目标能量为1.65(J)。
上述实施例的益处在于，可以充分体现同一冲击方式下不同凹痕类型对应不同的能量与凹痕深度映射关系，提高了确定目标能量的准确性。
在步骤203中，腐蚀环境是指目标样件所处的环境，比如核电厂一回路、核电厂二回路、典型化工换热器腐蚀回路等。目标样件在腐蚀环境中会经受应力腐蚀，从而呈现不同的腐蚀状态，进而通过对目标样件进行取样分析可以得到腐蚀状态数据。具体地，可以每隔预定周期(比如每隔100小时)获取目标样件的腐蚀状态数据。腐蚀状态数据包括表面形貌，裂纹萌生及扩展状态，裂纹起裂区域、起裂时间、扩展速率及裂纹长度等。
在步骤204中，对所有获取到的腐蚀状态数据进行统计分析，得到样本应力腐蚀结果数据。样本应力腐蚀结果数据包括样本氧化膜厚度、样本截面裂纹长度、样本表面裂纹长度、 和样本扩展速率等。比如，对所有获取到的表面形貌进行统计分析，可以得到目标样件的样本氧化膜厚度。再比如，对所有获取到的裂纹长度进行统计分析，可以得到目标样件的样本截面裂纹长度或样本表面裂纹长度。又比如，对所有获取到的扩展速率进行统计分析，可以得到目标样件的样本扩展速率。同理，基于裂纹萌生及扩展状态，裂纹起裂区域、起裂时间等进行统计分析，可以得到其它类型的样本应力腐蚀结果数据，此处不再赘述。
在步骤201至步骤204中，利用先制作凹痕再分析出样本应力腐蚀结果数据的方式，可以有效对凹痕缺陷进行制作和分析。
在一些实施例的步骤102中，通过对多个候选应力腐蚀影响因素的分析梳理，总计包括样本凹痕类型、样本加载应力、样本凹痕深度、样本冲击次数、样本交叉凹痕等多个变量。经过梳理，表明样本凹痕类型、样本凹痕深度、和样本加载应力是影响截面裂纹长度的关键变量。
在一实施例中，步骤102包括：
评估每个候选应力腐蚀影响因素对样本应力腐蚀结果数据的影响度；
根据影响度从多个应力腐蚀影响因素筛选出多个输入变量。
比如对每个候选应力腐蚀影响因素的重要性及属性进行分析评估，得到影响度，若影响度大于影响度阈值，则将候选应力腐蚀影响因素确定为输入变量。在一实施例中，可以选择样本凹痕类型、样本凹痕深度、样本加载应力作为输入变量，样本截面裂纹长度作为输出变量。
需要说明的是，通过对所有应力腐蚀截面裂纹结果数据的分析发现，由于应力腐蚀影响因素的多样性、实验过程控制的复杂性等原因，无法获知多个输入变量与样本截面裂纹长度之间的规律。比如，参照图4A，样本加载应力一定时，样本凹痕深度与样本截面裂纹长度的关系不明显。参照图4B，样本凹痕深度一定时，样本加载应力与样本截面裂纹长度的关系不明显。参照图4C，样本加载应力/样本凹痕深度，与样本截面裂纹长度的关系不明显。参照图4D，样本加载应力/样本凹痕深度，与样本截面裂纹长度的关系不明显。
在一些实施例的步骤103中，箱线图可以用来反映一组或多组连续型定量数据分布的中心位置和散布范围。本实施例利用箱线图中的四分位数值的分布规律，确定样本应力腐蚀结果数据与输入变量的映射关系。映射关系包括样本应力腐蚀结果数据与样本加载应力呈正相关关系、和样本应力腐蚀结果数据与样本凹痕深度呈二次曲线关系。
以样本截面裂纹长度为例，如图5A所示，横坐标为样本加载应力(单位为倍)，纵坐标为样本截面裂纹长度(单位为mm)。由图5A示出的箱线图可知虽然总体数据样本呈离散分布，但四分位数值整体呈线性规律，可以得到样本截面裂纹长度与样本加载应力呈正相关关系的结论。具体地，在样本加载应力为0.7倍时，样本截面裂纹长度的四分位数值约为0.1，在样本加载应力为0.8倍时，样本截面裂纹长度的四分位数值约为1.8等。还可以如图5B所示，横坐标为样本加载应力的分位数(单位为倍)，纵坐标为样本截面裂纹长度的分位数(单位为mm)。由图5B可知，同一凹痕深度条件下，随着样本加载应力的增加，样本截面裂纹长度不断增加。同一样本加载应力下，如图6A所示，横坐标为样本凹痕深度(单位为mm)， 纵坐标为样本截面裂纹长度(单位为mm)，由图6A示出的箱线图可知虽然总体数据样本呈离散分布，但四分位数值整体呈曲线规律，可以得到样本截面裂纹长度与样本加载应力呈二次曲线关系的结论。还可以如图6B所示，横坐标为样本凹痕深度(单位为mm)，纵坐标为样本截面裂纹长度(单位为mm)。由图6B可知，同一加载应力条件下，随着样本凹痕深度的增加，样本截面裂纹长度先增加再下降。
在一些实施例的步骤104中，可以根据样本应力腐蚀结果数据与输入变量的映射关系，构建得到应力腐蚀预测模型。参照图7，在一实施例中，步骤104包括：
步骤701，根据样本应力腐蚀结果数据与样本加载应力呈正相关关系，构建第一公式；
步骤702，根据样本应力腐蚀结果数据与样本凹痕深度呈二次曲线关系，构建第二公式；
步骤703，根据第一公式和第二公式，确定应力腐蚀预测模型。
例如，第一公式为G＝k1*y+b1，G表示样本应力腐蚀结果数据，y表示样本加载应力，k1，b1均为系数。第二公式为G＝k2*x2+k3*x+b2，G表示样本应力腐蚀结果数据，x表示样本凹痕深度，k2，k3，b2均为系数。
在步骤703中，可以通过将第一公式和第二公式进行整合，得到应力腐蚀预测模型，例如G＝k1*y+k2*x2+k3*x+b3。其中，b3为系数。需要说明的是，k1，k2，k3，b3在不同的置信度内需要符合不同的数值范围。例如，在95％置信度时，k1在[1.531，2.291]内，k2在[-1.797，0.2214]内，k3在[2.165，4.382]内，b3在[-1.005，1.088]内。
步骤701至步骤703的实施例的益处是，不同的映射关系先分别确定对应的公式，再将所有公式进行融合得到应力腐蚀预测模型，模型构建过程比较简单易实现。
在另一实施例中，还需考虑样本加载应力和样本凹痕深度之间的交互对样本应力腐蚀结果数据的影响。在该实施例具体实现时，参照图8，步骤104还可以包括：
步骤801，将第一公式和第二公式进行整合，得到第一整合公式；
步骤802，根据样本加载应力与样本凹痕深度，构建第三公式，第三公式用于指示样本加载应力与样本凹痕深度的乘法关系；
步骤803，根据第一整合公式和第三公式，确定应力腐蚀预测模型。
例如，第一整合公式为G＝k1*y+k2*x2+k3*x+b3，且第三公式为G＝k4*x*y，则将第一整合公式和第三公式整合得到的应力腐蚀预测模型为G＝k1*y+k2*x2+k3*x+k4*x*y+b4。其中，b4为系数。又例如，第三公式为G＝k4*x2*y，则将第一整合公式和第三公式相加得到的应力腐蚀预测模型为G＝k1*y+k2*x2+k3*x+k4*x2*y+b4。应力腐蚀预测模型中的系数可以根据试验数据估计得到，具体如何估计可以参照下文中表1的例子。
该实施的益处是，可以充分考虑样本加载应力和样本凹痕深度的交互对样本应力腐蚀结果数据的影响，进一步提高应力腐蚀预测模型的预测准确性，和普适性。
在一实施例中，步骤803包括：
将第一整合公式和第三公式进行整合，得到第二整合公式；
获取样本腐蚀环境因子；
将样本腐蚀环境因子和第二整合公式进行相乘，得到应力腐蚀预测模型。
例如，第二整合公式为G＝k1*y+k2*x2+k3*x+k4*x*y+b4，且样本腐蚀环境因子为β，则得到的应力腐蚀预测模型为G＝β*(k1*y+k2*x2+k3*x+k4*x*y+b4)。
该实施的益处是，可以充分体现腐蚀环境因子对样本应力腐蚀结果数据的影响，进一步提高应力腐蚀预测模型的预测准确性，和普适性。
在一实施例中，样本截面裂纹长度与样本加载应力呈正相关关系；样本截面裂纹长度与样本凹痕深度呈二次曲线关系；不同的腐蚀环境(比如腐蚀介质不同)引入样本腐蚀环境因子β来修正；样本凹痕类型以分段函数形式融入模型公式中体现凹痕类型的影响。样本加载应力对应一次函数，样本凹痕深度对应二次函数，引入两个变量交互影响项，应力腐蚀预测模型如下公式所示：
L＝β*(p0+p1x+p2y+p3x2+p4xy+ε)。
其中，L为样本截面裂纹长度、x为样本凹痕深度，y为样本加载应力，β为样本腐蚀环境因子，p0、p1、p2、p3、和p4均为系数，ε为随机误差。
最终实现包含样本凹痕深度、样本加载应力、样本凹痕类型等多因素耦合应力腐蚀预测模型的构建。整体而言，通过应力腐蚀结果数据和多个输入变量，通过归回分析确定预测模型通式中参数，并进行拟合优度和最小误差综合评估，最终确定预测模型。以本实施例的试验数据(包括应力腐蚀结果数据和多个输入变量)构建预测模型的系数的估计值及置信区间如表1所示。
表1
在表1中，R2表示应力腐蚀预测模型的预测精度，RMSE表示应力腐蚀预测模型的均方根误差。R2越大，则说明应力腐蚀预测模型的预测精度越高。RMSE越小，则说明应力腐蚀预测模型的均方根误差越小，从而说明应力腐蚀预测模型的预测精度越高。在p0＝-0.827、p1＝3.274、p2＝1.911、p3＝-0.7877、和p4＝-3.627时，R2＝0.82；RMSE＝0.0782。每个系数均在对应的回归系数的置信区间，且具有95％置信度。
在一些实施例的步骤105中，通过应力腐蚀预测模型预测带凹痕传热管的目标应力腐蚀结果数据。其中，若样本应力腐蚀结果数据包括样本氧化膜厚度，则目标应力腐蚀结果数据包括目标氧化膜厚度，若样本应力腐蚀结果数据包括样本截面裂纹长度，则目标应力腐蚀结果数据包括目标截面裂纹长度，若样本应力腐蚀结果数据包括样本表面裂纹长度，则目标应 力腐蚀结果数据包括目标表面裂纹长度。
在一实施例中，若输入变量包括样本凹痕类型，则应力腐蚀预测模型包括与样本凹痕类型对应的候选应力腐蚀预测模型。该实施例中，步骤105包括：
对带凹痕传热管进行类型检测，得到目标凹痕类型；
若目标凹痕类型与样本凹痕类型一致，则通过与样本凹痕类型对应的候选应力腐蚀预测模型对带凹痕传热管进行预测，得到目标应力腐蚀结果数据。
在一例子中，对带凹痕传热管的凹痕进行检测，可以得到目标凹痕类型为三棱柱型。若样本凹痕类型包括三棱柱型，则可以通过与三棱柱型对应的候选应力腐蚀预测模型对带传热管进行预测，最终得到目标应力腐蚀结果数据。
上述实施例的益处在于，充分体现不同凹痕类型在应力腐蚀预测是产生的不同影响，进一步提高了预测准确性。
在一实施例中，参照图9，步骤105还可以包括：
步骤901，获取样本无损检测信号与多个输入变量的对应关系；
步骤902，根据对应关系，对应力腐蚀预测模型进行变量转化，得到信号应力腐蚀预测模型；
步骤903，对带凹痕传热管进行无损检测，得到目标无损检测信号；
步骤904，将目标无损检测信号输入信号应力腐蚀预测模型进行腐蚀预测，得到目标应力腐蚀结果数据。
在步骤901中，可以利用上文制备的目标样件(比如传热管样件)的凹痕分布特征(比如凹痕轮廓及凹痕深度)，结合目前核电传热管制造及安装运行阶段的常见无损检测方法，制备标定样管。其中，标定样管的凹痕深度范围需覆盖用于腐蚀试验的所有试样的凹痕缺陷。无损检测：是指通常会使用不同的无损检测技术来评估材料或构件的质量。这些技术可能包括超声波检测、X射线检测、涡流检测等。通过这些技术，可以检测出材料或构件中的缺陷、裂纹或其他不良情况。以涡流检测为例，针对所有标定样管的预制缺陷进行检测，并记录涡流信号阻抗图，获得样本涡流信号与多个输入变量的对应关系，比如是样本涡流信号与标定样管的凹痕深度、加载应力及凹痕类型的对应关系。
在步骤902，基于获取的对应关系，将应力腐蚀预测模型中的输入变量等效转化为样本无损检测信号，从而得到信号应力腐蚀预测模型。
在步骤903至步骤904中，比如无损检测具体是涡流检测，则输入变量具体是转化为样本涡流检测信号，则通过信号应力腐蚀预测模型，可以实现在核电厂蒸汽发生器传热管涡流检测时，直接通过实际涡流信号定量评估带凹痕传热管可能的应力腐蚀截面裂纹长度，进而通过下一步完整性评估，判断带凹痕传热管是否需要堵管。此外，可以根据传热管制造阶段及运行阶段的无损检测结果异常结果信号特征，基于应力腐蚀预测模型，开展裂纹定量数据(比如氧化膜厚度、截面裂纹长度及表面裂纹长度)的预测。根据预测的裂纹定量数据，按照规范要求将预测结果作为输入参数，进行带缺陷传热管的完整性评估,实现传热管的服役性能最终评估分析。
本实施例的益处包括：1、提出了应力腐蚀预测模型的构建方法，构建流程简单，预测精度高，还降低了试验周期，大大压缩了实验成本，为相关行业的典型部件的应力腐蚀预测评估提供了优化解决方案。2、提出的先将无损(涡流检测)检测信号转化为应力腐蚀预测模型的输入参数，进而直接在线检测并评估应力腐蚀的裂纹萌生扩展趋势的方式，可提高核电厂蒸汽发生器传热管检测运维效率和经济性。
以上实施方式中的各种技术特征可以任意进行组合，只要特征之间的组合不存在冲突或矛盾，但是限于篇幅，未进行一一描述，因此上述实施方式中的各种技术特征的任意进行组合也属于本说明书公开的范围。
请参阅图10，本申请实施例还提供一种带凹痕传热管的应力腐蚀预测装置，可以实现上述的带凹痕传热管的应力腐蚀预测方法。图10为本申请实施例提供的带凹痕传热管的应力腐蚀预测装置的模块结构框图，该装置包括：
获取模块1001，用于获取样本应力腐蚀结果数据和引起样本应力腐蚀结果的多个候选应力腐蚀影响因素；
筛选模块1002，用于从多个候选应力腐蚀影响因素筛选出输入变量；
确定模块1003，用于根据样本应力腐蚀结果数据和输入变量绘制箱线图，且根据箱线图中的四分位数值的分布规律，确定样本应力腐蚀结果数据与输入变量的映射关系；
构建模块1004，用于根据样本应力腐蚀结果数据与输入变量的映射关系，构建得到应力腐蚀预测模型；
预测模块1005，用于通过应力腐蚀预测模型预测带凹痕传热管的目标应力腐蚀结果数据。
需要说明的是，该带凹痕传热管的应力腐蚀预测装置的具体实施方式与上述带凹痕传热管的应力腐蚀预测方法的具体实施例基本相同，在此不再赘述。
本申请实施例还提供了电子设备，电子设备包括：存储器、处理器、存储在存储器上并可在处理器上运行的程序以及用于实现处理器和存储器之间的连接通信的数据总线，程序被处理器执行时实现上述带凹痕传热管的应力腐蚀预测方法。该电子设备可以为包括平板电脑、车载电脑等任意智能终端。
请参阅图11，图11示意了另一实施例的电子设备的硬件结构，电子设备包括：
处理器1101，可以采用通用的CPU(Central Processing Unit，中央处理器)、微处理器、应用专用集成电路(Application Specific Integrated Circuit，ASIC)、或者一个或多个集成电路等方式实现，用于执行相关程序，以实现本申请实施例所提供的技术方案；
存储器1102，可以采用只读存储器(Read Only Memory，ROM)、静态存储设备、动态存储设备或者随机存取存储器(Random Access Memory，RAM)等形式实现。存储器1102可以存储操作系统和其他应用程序，在通过软件或者固件来实现本说明书实施例所提供的技术方案时，相关的程序代码保存在存储器1102中，并由处理器1101来调用执行本申请实施例的带凹痕传热管的应力腐蚀预测方法；
输入/输出接口1103，用于实现信息输入及输出；
通信接口1104，用于实现本设备与其他设备的通信交互，可以通过有线方式(例如USB、 网线等)实现通信，也可以通过无线方式(例如移动网络、WIFI、蓝牙等)实现通信；
总线1105，在设备的各个组件(例如处理器1101、存储器1102、输入/输出接口1103和通信接口1104)之间传输信息；
其中处理器1101、存储器1102、输入/输出接口1103和通信接口1104通过总线1105实现彼此之间在设备内部的通信连接。
本申请实施例还提供了存储介质，存储介质为计算机可读存储介质，用于计算机可读存储，存储介质存储有一个或者多个程序，一个或者多个程序可被一个或者多个处理器执行，以实现上述带凹痕传热管的应力腐蚀预测方法。
存储器作为一种非暂态计算机可读存储介质，可用于存储非暂态软件程序以及非暂态性计算机可执行程序。此外，存储器可以包括高速随机存取存储器，还可以包括非暂态存储器，例如至少一个磁盘存储器件、闪存器件、或其他非暂态固态存储器件。在一些实施方式中，存储器可选包括相对于处理器远程设置的存储器，这些远程存储器可以通过网络连接至该处理器。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
本申请实施例描述的实施例是为了更加清楚的说明本申请实施例的技术方案，并不构成对于本申请实施例提供的技术方案的限定，本领域技术人员可知，随着技术的演变和新应用场景的出现，本申请实施例提供的技术方案对于类似的技术问题，同样适用。
本领域技术人员可以理解的是，图中示出的技术方案并不构成对本申请实施例的限定，可以包括比图示更多或更少的步骤，或者组合某些步骤，或者不同的步骤。
以上所描述的装置实施例仅仅是示意性的，其中作为分离部件说明的单元可以是或者也可以不是物理上分开的，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
本领域普通技术人员可以理解，上文中所公开方法中的全部或某些步骤、系统、设备中的功能模块/单元可以被实施为软件、固件、硬件及其适当的组合。
本申请的说明书及上述附图中的术语“第一”、“第二”、“第三”、“第四”等(如果存在)是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
应当理解，在本申请中，“至少一个(项)”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，用于描述关联对象的关联关系，表示可以存在三种关系，例如，“A和/或B”可以表示：只存在A，只存在B以及同时存在A和B三种情况，其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项(个)”或其类似表达，是指这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a，b或c中的至少一项(个)，可以表示：a，b，c，“a和b”，“a和c”，“b和c”，或“a和b和 c”，其中a，b，c可以是单个，也可以是多个。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，上述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
上述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括多指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例的方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(Read-Only Memory，简称ROM)、随机存取存储器(Random Access Memory，简称RAM)、磁碟或者光盘等各种可以存储程序的介质。
以上参照附图说明了本申请实施例的优选实施例，并非因此局限本申请实施例的权利范围。本领域技术人员不脱离本申请实施例的范围和实质内所作的任何修改、等同替换和改进，均应在本申请实施例的权利范围之内。

Claims (13)

  1. 一种带凹痕传热管的应力腐蚀预测方法，其特征在于，所述方法包括：
    获取样本应力腐蚀结果数据和引起样本应力腐蚀结果的多个候选应力腐蚀影响因素；
    从多个候选应力腐蚀影响因素筛选出输入变量；
    根据所述样本应力腐蚀结果数据和所述输入变量绘制箱线图，且根据所述箱线图中的四分位数值的分布规律，确定所述样本应力腐蚀结果数据与所述输入变量的映射关系；
    根据所述样本应力腐蚀结果数据与所述输入变量的映射关系，构建得到应力腐蚀预测模型；
    通过所述应力腐蚀预测模型预测所述带凹痕传热管的目标应力腐蚀结果数据。
  2. 根据权利要求1所述的方法，其特征在于，所述获取样本应力腐蚀结果数据和引起样本应力腐蚀结果的多个候选应力腐蚀影响因素，包括：
    获取多个候选应力腐蚀影响因素；
    根据多个所述候选应力腐蚀影响因素对初始样件进行凹痕制作，得到目标样件；
    将所述目标样件放入预设的腐蚀环境，每隔预定周期获取所述目标样件的腐蚀状态数据；
    对所有获取到的所述腐蚀状态数据进行统计分析，得到所述样本应力腐蚀结果数据。
  3. 根据权利要求2所述的方法，其特征在于，多个所述候选应力腐蚀影响因素包括样本凹痕类型和样本凹痕深度；
    所述根据多个所述候选应力腐蚀影响因素对初始样件进行凹痕制作，得到目标样件，包括：
    根据所述样本凹痕类型确定冲头；
    获取冲击方式，且获取与所述冲击方式对应的第一能量与凹痕深度映射关系；
    利用所述第一能量与凹痕深度映射关系，对所述样本凹痕深度进行映射，得到目标能量；
    利用所述冲击方式和所述冲头对所述初始样件施加所述目标能量的冲击，得到所述目标样件。
  4. 根据权利要求3所述的方法，其特征在于，所述第一能量与凹痕深度映射关系包括与多个候选凹痕类型对应的候选能量与凹痕深度映射关系；
    所述利用所述第一能量与凹痕深度映射关系，对所述样本凹痕深度进行映射，得到目标能量，包括：
    根据所述样本凹痕类型，对多个所述候选凹痕类型对应的候选能量与凹痕深度映射关系进行筛选，得到第二能量与凹痕深度映射关系；
    利用所述第二能量与凹痕深度映射关系，对所述样本凹痕深度进行映射，得到所述目标能量。
  5. 根据权利要求1至4任一项所述的方法，其特征在于，所述输入变量包括样本加载应力、和样本凹痕深度，所述映射关系包括所述样本应力腐蚀结果数据与所述样本加载应力呈正相关关系、和所述样本应力腐蚀结果数据与所述样本凹痕深度呈二次曲线关系；
    所述根据所述样本应力腐蚀结果数据与输入变量的映射关系，构建得到所述应力腐蚀预测模型，包括：
    根据所述样本应力腐蚀结果数据与所述样本加载应力呈正相关关系，构建第一公式；
    根据所述样本应力腐蚀结果数据与所述样本凹痕深度呈二次曲线关系，构建第二公式；
    根据所述第一公式和所述第二公式，确定所述应力腐蚀预测模型。
  6. 根据权利要求5所述的方法，其特征在于，所述根据所述样本应力腐蚀结果数据与输入变量的映射关系，构建得到所述应力腐蚀预测模型，还包括：
    将所述第一公式和所述第二公式进行整合，得到第一整合公式；
    根据所述样本加载应力与所述样本凹痕深度，构建第三公式，所述第三公式用于指示所述样本加载应力与所述样本凹痕深度的乘法关系；
    根据所述第一整合公式和所述第三公式，确定所述应力腐蚀预测模型。
  7. 根据权利要求6所述的方法，其特征在于，所述根据所述第一整合公式和所述第三公式，确定所述应力腐蚀预测模型，包括：
    将所述第一整合公式和所述第三公式进行整合，得到第二整合公式；
    获取样本腐蚀环境因子；
    将所述样本腐蚀环境因子和所述第二整合公式进行相乘，得到所述应力腐蚀预测模型。
  8. 根据权利要求7所述的方法，其特征在于，所述样本应力腐蚀结果数据包括样本截面裂纹长度，所述应力腐蚀预测模型如下公式所示：
    L＝β*(p0+p1x+p2y+p3x2+p4xy+ε)；
    其中，L为所述样本截面裂纹长度、x为所述样本凹痕深度，y为所述样本加载应力，β为所述样本腐蚀环境因子，p0、p1、p2、p3、和p4均为系数，ε为随机误差。
  9. 根据权利要求1至4任一项所述的方法，其特征在于，所述输入变量包括样本凹痕类型，所述应力腐蚀预测模型包括与所述样本凹痕类型对应的候选应力腐蚀预测模型；
    所述通过所述应力腐蚀预测模型预测所述带凹痕传热管的目标应力腐蚀结果数据，包括：
    对所述带凹痕传热管进行类型检测，得到目标凹痕类型；
    若所述目标凹痕类型与所述样本凹痕类型一致，则通过与所述样本凹痕类型对应的候选应力腐蚀预测模型对所述带凹痕传热管进行预测，得到所述目标应力腐蚀结果数据。
  10. 根据权利要求1至4任一项所述的方法，其特征在于，所述通过所述应力腐蚀预测模型预测所述带凹痕传热管的目标应力腐蚀结果数据，包括：
    获取样本无损检测信号与多个所述输入变量的对应关系；
    根据所述对应关系，对所述应力腐蚀预测模型进行变量转化，得到信号应力腐蚀预测模型；
    对所述带凹痕传热管进行无损检测，得到目标无损检测信号；
    将所述目标无损检测信号输入所述信号应力腐蚀预测模型进行腐蚀预测，得到所述目标应力腐蚀结果数据。
  11. 一种带凹痕传热管的应力腐蚀预测装置，其特征在于，所述装置包括：
    获取模块，用于获取样本应力腐蚀结果数据和引起样本应力腐蚀结果的多个候选应力腐蚀影响因素；
    筛选模块，用于从多个候选应力腐蚀影响因素筛选出输入变量；
    确定模块，用于根据所述样本应力腐蚀结果数据和所述输入变量绘制箱线图，且根据所述箱线图中的四分位数值的分布规律，确定所述样本应力腐蚀结果数据与所述输入变量的映射关系；
    构建模块，用于根据所述样本应力腐蚀结果数据与所述输入变量的映射关系，构建得到应力腐蚀预测模型；
    预测模块，用于通过所述应力腐蚀预测模型预测所述带凹痕传热管的目标应力腐蚀结果数据。
  12. 一种电子设备，其特征在于，所述电子设备包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现权利要求1至10任一项所述的方法。
  13. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求1至10中任一项所述的方法。
PCT/CN2024/129932 2024-05-07 2024-11-05 带凹痕传热管的应力腐蚀预测方法和装置、设备及介质 Pending WO2025232121A1 (zh)

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