Defect Prediction Method, Device and Readable Medium for Steam Generator Heat Transfer Tubes

Abstract

The present invention provides a method, device and readable medium for predicting defects in heat transfer tubes of a steam generator. The method includes extracting characteristic data from the non-destructive testing data of the heat transfer tubes of the steam generator in previous overhauls of the nuclear power plant. The characteristic data includes the cumulative number of defects in the heat transfer tubes, the initial defect size, the defect distribution position and the defect growth rate. A defect prediction model for the heat transfer tubes of the steam generator is established by performing regression analysis and probability statistics on the characteristic data. The defect prediction model for the heat transfer tubes of the steam generator includes a development history model of the cumulative number of defects and a defect growth rate model. The defect state of the heat transfer tubes of the steam generator at a future time t is predicted through the defect prediction model for the heat transfer tubes of the steam generator. The prediction results include the number of newly added defects and the defect sizes at the positions where the defects have occurred. The present invention makes a complete prediction of the defects in the heat transfer tubes of the steam generator, and can not only predict the development of existing defects, but also predict newly added defects.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of nuclear power, and in particular to a method, device and readable medium for predicting defects in steam generator heat transfer tubes. Background Art

[0002] A steam generator is one of the core equipment of a pressurized water reactor nuclear power plant. The steam generator heat transfer tube is a key pressure boundary for isolating the primary loop and the secondary loop. The integrity of the steam generator being damaged will cause the reactor to be forced to shut down, resulting in high maintenance costs. Therefore, maintaining the integrity of the steam generator is of great significance for the safety and economy of nuclear power plant operation.

[0003] Existing solutions provide a basic method for predicting the defect growth rate using a lognormal distribution model, but it can only predict the defect growth rate at the current moment at the positions where defects have occurred in the heat transfer tubes, and does not provide methods for predicting the number of newly added defects, the distribution of newly added defect positions, the initial defect size, and the defect growth rate at future moments. Therefore, it is impossible to conduct a complete prediction and evaluation of the overall development of steam generator heat transfer tube defects. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, device and readable medium for predicting defects in steam generator heat transfer tubes, so as to solve the problem that the existing defect prediction methods cannot conduct a complete prediction and evaluation of the overall development of steam generator heat transfer tube defects.

[0005] To solve the above technical problem, the present invention provides a method for predicting defects in steam generator heat transfer tubes, including: extracting characteristic data from the non-destructive testing data of steam generator heat transfer tubes during previous overhauls of a nuclear power plant, where the characteristic data includes the cumulative number of defects, the initial defect size, the defect distribution position, and the defect growth rate of the heat transfer tubes; establishing a steam generator heat transfer tube defect prediction model by performing regression analysis and probability statistics on the characteristic data, where the steam generator heat transfer tube defect prediction model includes a cumulative number of defects development history model and a defect growth rate model; predicting the defect state of the steam generator heat transfer tubes at a future time t through the steam generator heat transfer tube defect prediction model, and the prediction results include the number of newly added defects and the defect sizes at the positions where defects have occurred.

[0006] Optionally, establishing the defect growth rate model includes: simulating the distribution of the defect growth rate measured during the most recent major overhaul using a lognormal distribution model to establish a defect growth rate probability distribution model; performing a regression analysis on the relationship between the defect growth rates measured during previous major overhauls and time using a second custom function to obtain a defect growth rate development history model; substituting a given confidence probability into the defect growth rate probability distribution model to obtain the defect growth rate at the most recent major overhaul; and establishing the defect growth rate model based on the defect growth rate at the most recent major overhaul and the defect growth rate development history model.

[0007] Optionally, the established defect growth rate model is:

[0008]

[0009] wherein, is the defect growth rate at future time t, is the defect growth rate at the most recent major overhaul, is the defect growth rate development history model, is the time of the most recent major overhaul, and t is future time t.

[0010] Optionally, the second custom function is: , where a, b, and c are coefficients obtained through regression analysis, and t is future time t.

[0011] Optionally, establishing the cumulative defect quantity development history model includes: performing a regression analysis on the relationship between the cumulative defect quantity and time using a first custom function to establish the cumulative defect quantity development history model, where a separate cumulative defect quantity development history model is established for each steam generator.

[0012] Optionally, predicting the newly added defect quantity includes: predicting the cumulative defect quantity at time t through the cumulative defect quantity development history model; and subtracting the existing defect quantity of the current steam generator heat transfer tubes from the cumulative defect quantity at time t to obtain the newly added defect quantity at future time t.

[0013] Optionally, the steam generator heat transfer tube defect prediction model further includes a defect distribution location model, where the defect distribution location model is established through a spatial random simulation method or a Gaussian mixture model, and the spatial random simulation method includes the Kriging method and the sequential Gaussian simulation method.

[0014] Optionally, the prediction result further includes the distribution of newly added defect locations, and predicting the distribution of newly added defect locations includes: predicting the distribution of newly added defect locations for the defects of the newly added defect quantity through the defect distribution location model.

[0015] Optionally, the steam generator heat transfer tube defect prediction model further includes an initial defect size probability distribution model. Establishing the initial defect size probability distribution model includes: performing regression analysis on the actual probability density distribution of the initial defect size using a logarithmic logistic distribution function or a lognormal distribution function, thereby establishing the initial defect size probability distribution model.

[0016] Optionally, the prediction result further includes the initial defect size at each newly added defect position. Predicting the initial defect size at each newly added defect position includes: substituting a given confidence probability into the initial defect size probability distribution model to obtain the initial defect size at each newly added defect position at a future time t.

[0017] Optionally, predicting the defect size at the position where a defect has occurred includes: predicting the defect size at the position where a defect has occurred at a future time t through the defect growth rate model.

[0018] To solve the above technical problems, the present invention provides a defect prediction device for a steam generator heat transfer tube, including: a memory for storing instructions executable by a processor; and a processor for executing the instructions to implement the defect prediction method as described above.

[0019] To solve the above technical problems, the present invention provides a computer-readable medium storing computer program code, and the computer program code implements the defect prediction method as described above when executed by a processor.

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] The defect prediction method, device and readable medium for the steam generator heat transfer tube of the present invention respectively establish a cumulative defect quantity development history model, an initial defect size probability distribution model, a defect distribution position model and a defect growth rate model through regression analysis and probability statistics of characteristic data, and can perform a complete prediction and evaluation on the overall development of the steam generator heat transfer tube defects. It can not only predict the development of existing defects, but also predict the number, distribution position and size of newly added defects; this method not only covers the development of steam generator heat transfer tube defects over time, but also reflects the distribution characteristics in space; the predicted defect size can be made conservative by adjusting the model parameters, which meets the actual engineering requirements of nuclear power plants and has important significance for the operation evaluation of nuclear power plant steam generators. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are provided to provide a further understanding of the present application, and they are incorporated into and constitute a part of the present application. The accompanying drawings illustrate embodiments of the present application and, together with the description herein, serve to explain the principles of the present application. In the accompanying drawings:

[0023] Figure 1 It is a schematic diagram of a method for predicting defects in heat transfer tubes of a steam generator according to an embodiment of the present disclosure.

[0024] Figure 2 The cumulative number of defects in four steam generators was counted with respect to the number of effective full-power operation years.

[0025] Figure 3 The probability density functions of the respective steam generators are given.

[0026] Figure 4 It shows the cumulative distribution of the measured defect growth rate and the cumulative distribution of the defect growth rate simulated using the lognormal distribution.

[0027] Figure 5 It shows the change of the defect growth rate over time.

[0028] Figure 6 It is a system block diagram of a defect prediction device according to an embodiment of the present disclosure. Detailed implementation manners

[0029] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, without creative efforts, the present application can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0030] The defect growth rate is obtained from the change in the defect depth measured by two non-destructive examinations (NDEs) and is a necessary parameter for performing operation evaluations.

[0031] Existing methods for predicting defects in heat transfer tubes of steam generators can only predict the defect growth rate at the current moment at the positions where defects have already occurred in the heat transfer tubes, and do not provide methods for predicting the number of new defects, the distribution of new defect positions, the initial defect size, and the defect growth rate at future moments. Therefore, a complete prediction and evaluation of the overall development of defects in heat transfer tubes of steam generators cannot be performed.

[0032] To overcome the limitations of the above-mentioned defect prediction method for steam generator heat transfer tubes, the present invention proposes a defect prediction method for steam generator heat transfer tubes, which can predict the defect growth rate at future times. In some embodiments, the defect prediction method can also predict the number of newly added defects, the distribution location, and / or the size, so as to be able to conduct a complete prediction and evaluation of the overall development of the defects of the steam generator heat transfer tubes. In some embodiments, the method not only covers the development of the defects of the steam generator heat transfer tubes over time but also reflects the spatial distribution characteristics; the predicted defect size can be made more conservative by adjusting the model parameters, meeting the actual engineering requirements of nuclear power plants, and having important significance for the operation evaluation of steam generators in nuclear power plants.

[0033] Figure 1 is a schematic diagram of a defect prediction method for a steam generator heat transfer tube according to an embodiment of the present disclosure. As Figure 1 shown, the defect prediction method 100 for the steam generator heat transfer tube includes the following steps:

[0034] Step 1: Extract characteristic data from the non-destructive testing data of the steam generator heat transfer tubes during previous major overhauls of the nuclear power plant. The characteristic data includes the defect growth rate of the heat transfer tubes and may also include any one or more of the cumulative number of defects, the initial defect size, and the defect distribution location.

[0035] Step 2: Establish a complete defect prediction model for the steam generator heat transfer tubes by performing regression analysis and probability statistics on the characteristic data obtained in Step 1. The defect prediction model for the steam generator heat transfer tubes includes a defect growth rate model and may also include any one or more of a cumulative number of defects development history model, an initial defect size probability distribution model, and a defect distribution location model.

[0036] It should be noted that there is currently no relevant literature studying the spatio-temporal distribution laws of the cumulative number of defects, the initial defect size, and the defect distribution location of the heat transfer tubes. The existing solutions for the defect growth rate obtained in Step 1 have given specific prediction models, and the logarithmic normal distribution model is used to predict the defect growth rate. However, the prediction methods given in the existing solutions do not reflect the variation relationship of the defect growth rate over time, while the present invention takes into account the variation of the defect growth rate over time. Therefore, establishing the defect growth rate model, the cumulative number of defects development history model, the initial defect size probability distribution model, and the defect distribution location model in Step 2 belongs to original research results. The methods for establishing the above models are as follows:

[0037] (1) Establish a cumulative number of defects development history model

[0038] The relationship between the cumulative number of defects and time is analyzed by a first custom function to establish a development history model of the cumulative number of defects. For each steam generator, a respective development history model of the cumulative number of defects is established.

[0039] The specific example is as follows:

[0040] Figure 2 The changes in the cumulative number of defects of four steam generators with the effective full power operation years (EFPY) are statistically analyzed. As Figure 2 shown, the cumulative number of defects of the four steam generators basically shows a linear change trend with time. Therefore, a linear regression model can be used to fit it, and the cumulative number of defects at future times can be predicted by extrapolation. That is, the first custom function is n = f1(t), where the independent variable t is time and the dependent variable n represents the cumulative number of defects.

[0041] The linear regression model obtained by fitting with the least squares method and its comparison with the measured results are shown in Table 1. In addition, from Figure 2 the slopes of the fitting formulas in Table 1, it can be seen that there are obvious differences in the increasing speed of the cumulative number of defects of each steam generator (1a < 1b < 2a < 2b). Therefore, when predicting the cumulative number of defects, different prediction models are used for different steam generator units. For the steam generator numbered 1a, the established development history model of the cumulative number of defects is N = 39.4×EFPY - 37.0.

[0042] It should be noted that the establishment of the above example model only uses the data corresponding to four overhauls for statistical analysis. Therefore, strictly speaking, it is only applicable to the initial stage of the operation of the steam generator. The prediction model for long-term development needs to be further corrected through long-term operation data.

[0043] Table 1 Prediction of the cumulative number of defects

[0044]

[0045] (2) Establish an initial defect size probability distribution model

[0046] The actual probability density distribution of the initial defect size is analyzed by a logarithmic logistic distribution function or a lognormal distribution function to establish the initial defect size probability distribution model.

[0047] Figure 3The probability density functions of the steam generators are given, and it can be seen that their distributions are asymmetric. Coupled with the fact that the initial defect sizes are all positive values, four distribution models, namely the lognormal distribution, the loglogistic distribution, the Weibull distribution, and the extreme value distribution, are used to perform regression analysis on the actual probability density distribution of the initial defect sizes respectively. The results of the regression analysis are as Figure 3 shown. It can be seen that both the loglogistic distribution and the lognormal distribution can accurately fit the initial defect size distribution.

[0048] (3) Establish a defect distribution position model

[0049] Optionally, a defect distribution position model is established by a spatial random simulation method, and the spatial random simulation method includes, but is not limited to, the Kriging method and the sequential Gaussian simulation method.

[0050] Optionally, the defect distribution position model is established by a Gaussian mixture model.

[0051] (4) Establish a defect growth rate model

[0052] The steps for establishing the defect growth rate model include:

[0053] Step a. Use the lognormal distribution model to simulate the distribution of the defect growth rate measured during the most recent overhaul, and establish a defect growth rate probability distribution model.

[0054] Figure 4 shows the cumulative distribution of the measured defect growth rate and the cumulative distribution of the defect growth rate simulated using the lognormal distribution. From Figure 4 it can be seen that the lognormal distribution model can accurately simulate the distribution of the defect growth rate.

[0055] As an example, the steps for creating the defect growth rate probability distribution model in step a include:

[0056] a1) Calculate the average or median value of the defect growth rate using the defect growth rate data measured during the most recent overhaul ;

[0057] a2) The standard deviation of the defect growth rate traverses and takes values within the range of [0.5, 0.9], i = 1, 2, 3... n; randomly generate 100,000 sample points of the defect growth rate that satisfy the lognormal distribution lnX~ ;

[0058] a3) Generate 100,000 sample points of the measurement error that satisfy Y~ the normal distribution, and the standard deviation of the measurement error Traverse and take values within the range of [0, 2], where j = 1, 2, 3, …… m;

[0059] a4) Superimpose the 100,000 defect growth rate sample points generated in step a2) and the 100,000 measurement error sample points generated in step a3) to obtain 100,000 simulated defect growth rate sample points;

[0060] a5) Calculate the goodness of fit R between the cumulative distribution curve of the simulated defect growth rate sample points in step a4) and the cumulative distribution curve of the measured defect growth rate i 2 ;

[0061] a6) Repeat steps a2) to a5) to obtain n * m goodness of fit R ij 2 values, and find the maximum R ij 2 value corresponding to the and The obtained cumulative distribution curve of the simulated defect growth rate is the defect growth rate probability distribution model described in step a).

[0062] Step b. Use the second custom function to perform a regression analysis on the relationship between the defect growth rate measured during each major overhaul and time to obtain the defect growth rate development history model.

[0063] Figure 5 shows the change of the defect growth rate over time. From Figure 5 it can be seen that the defect growth rate data corresponding to each major overhaul has a certain degree of discreteness, but the average level of the defect growth rate shows a decreasing trend over time. The present disclosure uses the second custom exponential function to perform a regression analysis on the relationship between the defect growth rate and time.

[0064] In one embodiment, the second custom exponential function , where a, b, and c are coefficients that need to be determined through regression analysis, and t is the future time t.

[0065] After regression analysis, the coefficients a, b, and c are determined, and the obtained defect growth rate development history model is as follows:

[0066]

[0067] Among them, the unit of t is generally effective full power operation years (EFPY).

[0068] Figure 5 The goodness of fit R between the defect growth rate development history model obtained by regression analysis in 2 = 0.998, so it can accurately reflect the change law of the average level of the defect growth rate over time.

[0069] Step c. Substitute the given confidence probability into the defect growth rate probability distribution model to obtain the defect growth rate at the most recent major overhaul.

[0070] Reference Figure 4 , after a given confidence probability value ( Figure 4 ordinate value), the corresponding defect growth rate can be directly obtained through Figure 4 . Here, by setting a relatively high confidence probability value, the conservativeness of the defect growth rate prediction value can be adjusted. The greater the confidence probability, the greater the defect growth rate, and thus the more conservative.

[0071] Step d. Establish the defect growth rate model based on the defect growth rate at the most recent major overhaul and the defect growth rate development history model.

[0072] Optionally, establish the defect growth rate model through the following formula:

[0073]

[0074] where is the defect growth rate at future time t, is the defect growth rate at the most recent major overhaul, is the defect growth rate development history model, is the time of the most recent major overhaul, and t is future time t.

[0075] Step 3: Predict the defect state of the steam generator heat transfer tubes at future time t through the steam generator heat transfer tube defect prediction model obtained in Step 2, including:

[0076] Step 3.1: Use the cumulative defect quantity development history model obtained in Step 2 to predict the cumulative defect quantity at time t;

[0077] Step 3.2: Subtract the existing defect quantity of the current steam generator heat transfer tubes from the cumulative defect quantity obtained in Step 3.1 to get the new defect quantity at future time t.

[0078] Step 3.3: Use the defect distribution position model obtained in Step 2 to predict the position distribution of the corresponding quantity of new defects obtained in Step 3.2.

[0079] Step 3.4: Use the initial defect size probability distribution model obtained in Step 2 to predict the initial defect size at each new defect position obtained in Step 3.3 at future time t.

[0080] Step 3.4.1: Substitute the given confidence probability into the initial defect size probability distribution model obtained in Step 2 to get the initial defect size Size_Initail;

[0081] Step 3.4.2: Assign an initial defect size of Size_Initail at future time t to each newly added defect location obtained in Step 3.3.

[0082] The present disclosure can adjust the conservativeness of the predicted value of the initial defect size through a confidence probability. The greater the confidence probability, the greater the defect growth rate, and thus the more conservative.

[0083] Optionally, an alternative to Step 3.4 is: Use the random size values generated by the initial defect size probability distribution model obtained in Step 2 to predict the initial defect size at each newly added defect location obtained in Step 3.3 at future time t.

[0084] Step 3.5: Use the defect growth rate model obtained in Step 2 to predict the defect size at the defect locations that have occurred at future time t.

[0085] This application also includes a defect prediction device for a steam generator heat transfer tube, including a memory and a processor. Among them, the memory is used to store instructions executable by the processor; the processor is used to execute the instructions to implement the defect prediction method described above.

[0086] Figure 6 is a system block diagram of the defect prediction device according to an embodiment of the present disclosure. Refer to Figure 6 As shown, the defect prediction device 600 may include an internal communication bus 601, a processor 602, a read-only memory (ROM) 603, a random access memory (RAM) 604, and a communication port 605. When applied to a personal computer, the defect prediction device 600 may further include a hard disk 606. The internal communication bus 601 can enable data communication between the components of the defect prediction device 600. The processor 602 can make judgments and issue prompts. In some embodiments, the processor 602 may be composed of one or more processors. The communication port 605 can enable data communication between the defect prediction device 600 and the outside. In some embodiments, the defect prediction device 600 can send and receive information and data from the network through the communication port 605. The defect prediction device 600 may also include different forms of program storage units and data storage units, such as the hard disk 606, the read-only memory (ROM) 603, and the random access memory (RAM) 604, which can store various data files used for computer processing and / or communication, as well as possible program instructions executed by the processor 602. The processor executes these instructions to implement the main part of the method. The results processed by the processor are transmitted to the user device through the communication port and displayed on the user interface.

[0087] The above operation method can be implemented as a computer program, stored in the hard disk 606, and loaded into the processor 602 for execution to implement the defect prediction method of this application.

[0088] The present application also includes a computer-readable medium storing computer program code, which implements the foregoing defect prediction method when executed by a processor.

[0089] When the defect prediction method is implemented as a computer program, it can also be stored in a computer-readable storage medium as an article of manufacture. For example, the computer-readable storage medium may include, but is not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips), optical disks (e.g., compact disks (CDs), digital versatile disks (DVDs)), smart cards, and flash memory devices (e.g., electrically erasable programmable read-only memory (EEPROM), cards, sticks, key drives). In addition, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media (and / or storage media) that can store, contain, and / or carry code and / or instructions and / or data.

[0090] Flowcharts are used in the present application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the operations above or below do not necessarily have to be executed precisely in order. Instead, the various steps can be processed in reverse order or simultaneously. Also, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0091] The basic concepts have been described above. Obviously, for those skilled in the art, the above invention disclosure is only an example and does not constitute a limitation to the present application. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to the present application. Such modifications, improvements, and corrections are proposed in the present application, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of the present application.

[0092] Meanwhile, the present application uses specific terms to describe the embodiments of the present application. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the present application can be appropriately combined.

[0093] Some aspects of the present application may be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above-mentioned hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". The processor may be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. In addition, aspects of the present application may be embodied as a computer product located on one or more computer-readable media, which product includes computer-readable program code. For example, the computer-readable media may include, but is not limited to, magnetic storage devices (such as hard disks, floppy disks, magnetic tapes...), optical disks (such as compact disks CD, digital versatile disks DVD...), smart cards, and flash memory devices (such as cards, sticks, key drives...).

[0094] The computer-readable media may include a propagated data signal having computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take many forms, including electromagnetic, optical, or the like, or any suitable combination thereof. The computer-readable media may be any computer-readable media other than a computer-readable storage media, which can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The program code located on the computer-readable media may be propagated through any appropriate medium, including radio, cable, fiber optic cable, radio frequency signal, or similar media, or any combination of the above media.

[0095] Similarly, it should be noted that, for the sake of simplicity of the presentation of the disclosure of the present application, and thus to assist in the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of the present application, sometimes multiple features are grouped into one embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the features required by the subject matter of the present application are more than those mentioned. In fact, the features of the embodiments are fewer than all the features of the single embodiment disclosed above.

[0096] As shown in the present application, unless the context clearly indicates an exception, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list, and the method or device may also include other steps or elements.

[0097] Unless otherwise specifically stated, the relative arrangements, numerical expressions, and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for the sake of convenience of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationships. Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods, and devices should be regarded as part of the specification. In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, further discussion thereof in subsequent drawings is not required.

[0098] In addition, it should be noted that the use of terms such as "first" and "second" to limit components is only for the convenience of distinguishing the corresponding components. Without otherwise stating, the above terms have no special meanings, and thus should not be construed as limiting the scope of protection of the present application. In addition, although the terms used in the present application are selected from well-known and commonly used terms, some of the terms mentioned in the specification of the present application may be selected by the applicant according to his or her judgment, and their detailed meanings are described in the relevant parts of the description herein. In addition, it is required to understand the present application not only through the actual terms used, but also through the meanings implied by each term.

[0099] In some embodiments, numbers are used to describe components and attribute quantities. It should be understood that such numbers used for the description of embodiments are modified by the modifiers "about", "approximately", or "substantially" in some examples. Unless otherwise stated, "about", "approximately", or "substantially" indicate that the said numbers allow a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification are all approximate values, and such approximate values may change according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining general digits. Although the numerical ranges and parameters used to confirm the breadth of the scope in some embodiments of the present application are approximate values, in specific embodiments, such numerical settings are made as precise as possible within the feasible range.

[0100] Although the present application has been described with reference to the current specific embodiments, those of ordinary skill in the art of the present technology should recognize that the above embodiments are only used to illustrate the present application, and various equivalent changes or substitutions can be made without departing from the spirit of the present application. Therefore, as long as the changes and modifications to the above embodiments are within the scope of the substantial spirit of the present application, they will fall within the scope of the present application.

Claims

1. A method for predicting defects of a steam generator heat transfer tube, characterized in that: include: Extracting characteristic data from nondestructive testing data of heat transfer tubes of steam generators in nuclear power plants after previous overhauls, wherein the characteristic data includes the cumulative number of defects, initial defect size, defect distribution location and defect growth rate of the heat transfer tubes; Establishing a steam generator heat transfer tube defect prediction model by performing regression analysis and probability statistics on the characteristic data, wherein the steam generator heat transfer tube defect prediction model includes a cumulative defect quantity development history model and a defect growth rate model; The defect state of the steam generator heat transfer tube at the future time t is predicted by the steam generator heat transfer tube defect prediction model, and the prediction results include the number of new defects and the defect size of the defect location; Wherein, establishing the defect growth rate model comprises: using a log-normal distribution model to simulate the distribution of the defect growth rate actually measured at the most recent overhaul, and establishing a defect growth rate probability distribution model; using a second custom function to perform a regression analysis on the relationship between the defect growth rate actually measured at all previous overhauls and time, and obtaining a defect growth rate development history model; substituting a given confidence probability into the defect growth rate probability distribution model to obtain the defect growth rate of the most recent overhaul; and establishing the defect growth rate model according to the defect growth rate of the most recent overhaul and the defect growth rate development history model; The defect growth rate model is established by the following formula: in, is the defect growth rate at time t in the future, is the defect growth rate of the most recent overhaul, is the defect growth rate development history model, is the time of the most recent overhaul, and t is the time t in the future.

2. The defect prediction method according to claim 1, characterized in that: The second custom function is: , where a, b, c are the coefficients obtained through regression analysis, and t is the future time t.

3. The defect prediction method according to claim 1, characterized in that: Establishing the cumulative defect quantity development history model includes: A first user-defined function is used to perform regression analysis on the relationship between the cumulative number of defects and time, so as to establish the cumulative number of defects development history model, wherein each steam generator establishes its own cumulative number of defects development history model.

4. The defect prediction method according to claim 3, characterized in that: The predicted number of new defects includes: Predicting the cumulative defect quantity at time t by using the cumulative defect quantity development history model; The number of defects added at the future time t is obtained by subtracting the number of defects already existing in the steam generator heat transfer tube from the cumulative number of defects at the time t.

5. The defect prediction method according to claim 4, characterized in that: The steam generator heat transfer tube defect prediction model also includes a defect distribution location model, wherein the defect distribution location model is established by a spatial random simulation method or a Gaussian mixture model, and the spatial random simulation method includes a Kriging method and a sequential Gaussian simulation method.

6. The defect prediction method according to claim 5, characterized in that: The prediction result also includes the newly added defect location distribution, and the predicted newly added defect location distribution includes: The defect distribution location model is used to predict the new defect location distribution of the defects with the new defect quantity.

7. The defect prediction method according to claim 6, characterized in that: The steam generator heat transfer tube defect prediction model also includes an initial defect size probability distribution model. Establishing the initial defect size probability distribution model includes: A log-logistic distribution function or a log-normal distribution function is used to perform regression analysis on the actual probability density distribution of the initial defect size, thereby establishing the initial defect size probability distribution model.

8. The defect prediction method according to claim 7, characterized in that: The prediction result also includes the initial defect size at each newly added defect position. The prediction of the initial defect size at each newly added defect position includes: Substitute the given confidence probability into the initial defect size probability distribution model to obtain the initial defect size of each newly added defect position at the future time t.

9. The defect prediction method according to claim 1, characterized in that: The defect sizes that predict the location of the defect that has occurred include: The defect growth rate model is used to predict the defect size at the defect location at time t in the future.

10. A defect prediction device for a steam generator heat transfer tube, characterized in that: include: a memory for storing instructions executable by a processor; A processor, configured to execute the instructions to implement the defect prediction method according to any one of claims 1 to 9.

11. A computer readable medium storing computer program code, characterized in that: When the computer program code is executed by a processor, the defect prediction method according to any one of claims 1 to 9 is implemented.

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