Defect prediction method and device for heat transfer tube of steam generator and readable medium

Abstract

The invention provides a defect prediction method and device for a heat transfer tube of a steam generator and a readable medium. The method comprises the following steps: extracting characteristic data from nondestructive testing data of a steam generator heat transfer tube of a nuclear power plant during previous overhaul, wherein the characteristic data comprises the accumulated defect number, the initial defect size, the defect distribution position and the defect growth rate of the heat transfer tube; establishing a steam generator heat transfer tube defect prediction model by performing regression analysis and probability statistics on the feature data, wherein the steam generator heat transfer tube defect prediction model comprises an accumulated defect number development process model and a defect growth rate model; the defect state of the steam generator heat transfer tube at the future t moment is predicted through the steam generator heat transfer tube defect prediction model, and the prediction result comprises the number of newly-added defects and the defect size of the defect position. According to the method, the defects of the heat transfer tube of the steam generator are completely predicted, the development condition of the existing defects can be predicted, and the newly added defects can also be predicted.

Description

Technical Field

[0001] The present invention mainly relates to the field of nuclear power technology, and in particular to a defect prediction method, device and readable medium for a steam generator heat transfer tube. Background Art

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

[0003] The existing scheme provides a basic method for predicting the defect growth rate using a log-normal distribution model. However, it can only predict the defect growth rate at the current moment where the defect has occurred in the heat transfer tube. It does not provide a prediction method for the number of new defects, the distribution of new defect locations, the initial defect size, and the defect growth rate at future moments. Therefore, it is impossible to conduct a complete predictive evaluation of the overall development of defects in the steam generator heat transfer tube. Summary of the invention

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

[0005] In order to solve the above technical problems, the present invention provides a defect prediction method for a steam generator heat transfer tube, comprising: extracting characteristic data from nondestructive testing data of steam generator heat transfer tubes that have been overhauled in nuclear power plants, the characteristic data including the cumulative defect quantity, initial defect size, defect distribution position and defect growth rate of the heat transfer tube; establishing a steam generator heat transfer tube defect prediction model by performing regression analysis and probability statistics on the characteristic data, the steam generator heat transfer tube defect prediction model including a cumulative defect quantity development history model and a defect growth rate model; predicting the defect state of the steam generator heat transfer tube at a future time t by using the steam generator heat transfer tube defect prediction model, the prediction result including the number of new defects and the defect size of the defect location that has occurred.

[0006] Optionally, establishing the defect growth rate model includes: using a log-normal distribution model to simulate the distribution of the defect growth rate actually measured in 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 in 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 based on the defect growth rate of the most recent overhaul and the defect growth rate development history model.

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

[0008]

[0009] 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.

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

[0011] Optionally, establishing the cumulative defect quantity development history model includes: using a first custom function to perform a regression analysis on the relationship between the cumulative defect quantity and time, thereby establishing the cumulative defect quantity development history model, wherein each steam generator separately establishes its own cumulative defect quantity development history model.

[0012] Optionally, predicting the number of new defects includes: predicting the cumulative number of defects at time t through the cumulative defect number development history model; subtracting the number of existing defects in the current steam generator heat transfer tube from the cumulative number of defects at time t to obtain the number of new defects at future time t.

[0013] Optionally, 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.

[0014] Optionally, the prediction result also includes a newly added defect location distribution, and predicting the newly added defect location distribution includes: predicting the newly added defect location distribution for the defects with the newly added defect quantity by using the defect distribution location model.

[0015] Optionally, the steam generator heat transfer tube defect prediction model also includes an initial defect size probability distribution model, and establishing the initial defect size probability distribution model includes: using a log-logistic distribution function or a log-normal distribution function to perform a regression analysis on the actual probability density distribution of the initial defect size, thereby establishing the initial defect size probability distribution model.

[0016] Optionally, the prediction result also includes an initial defect size at each newly added defect location, and predicting the initial defect size at each newly added defect location includes: substituting a given confidence probability into the initial defect size probability distribution model to obtain the initial defect size of each newly added defect location at the future time t.

[0017] Optionally, predicting the defect size at the defect location where the defect has occurred includes: predicting the defect size at the defect location where the defect has occurred at time t in the future by using 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, comprising: 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] In order to solve the above technical problem, the present invention provides a computer readable medium storing a computer program code, and the computer program code implements the above defect prediction method 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 of 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 by performing regression analysis and probability statistics on characteristic data, and can perform a complete prediction and evaluation on the overall development of defects in the steam generator heat transfer tube, and can predict not only the development of existing defects, but also the number, distribution location and size of new defects; the method covers the development of steam generator heat transfer tube defects over time and reflects the spatial distribution characteristics; the predicted defect size can be made conservative by adjusting the model parameters, which meets the actual engineering needs of nuclear power plants and is of great significance to the operation evaluation of nuclear power plant steam generators. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are included to provide a further understanding of the present application. They are included and constitute a part of the present application. The accompanying drawings illustrate embodiments of the present application and together with the present specification serve to explain the principles of the present application. In the accompanying drawings:

[0023] Figure 1 It is a schematic diagram of a defect prediction method for a steam generator heat transfer tube according to an embodiment of the present disclosure.

[0024] Figure 2 The changes of the cumulative number of defects of the four steam generators with the effective full-power operation years were statistically analyzed.

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

[0026] Figure 4 The measured cumulative distribution of defect growth rate and the cumulative distribution of defect growth rate simulated using log-normal distribution are shown.

[0027] Figure 5 Shows the defect growth rate over time.

[0028] Figure 6 4 is a system block diagram of a defect prediction device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0030] The defect growth rate is obtained by measuring the change in defect depth between two nondestructive examinations (NDE) and is an essential parameter for performing operational evaluation.

[0031] The existing defect prediction method for steam generator heat transfer tubes can only predict the defect growth rate at the defect location of the heat transfer tube at the current moment, and does not provide a prediction method for the number of new defects, the distribution of new defect locations, 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.

[0032] In order to overcome the limitations of the above-mentioned steam generator heat transfer tube defect prediction method, the present invention proposes a steam generator heat transfer tube defect prediction method, which can predict the defect growth rate at a future time. In some embodiments, the defect prediction method can also predict the number, distribution location and / or size of new defects, so as to be able to make a complete prediction and evaluation of the overall development of steam generator heat transfer tube defects. In some embodiments, the method covers both the development of steam generator heat transfer tube defects over time and the spatial distribution characteristics; the model parameters can be adjusted to make the predicted defect size conservative, which meets the actual engineering needs of nuclear power plants and is of great significance to the operation evaluation of nuclear power plant steam generators.

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

[0034] Step 1: Extract characteristic data from the nondestructive testing data of the steam generator heat transfer tubes of the nuclear power plant after each overhaul, wherein the characteristic data includes the defect growth rate of the heat transfer tubes, and may also include any one or more of the cumulative defect number, initial defect size and defect distribution location.

[0035] Step 2: Establish a complete steam generator heat transfer tube defect prediction model by performing regression analysis and probability statistics on the characteristic data obtained in step 1. The steam generator heat transfer tube defect prediction model includes a defect growth rate model, and may also include any one or more of a cumulative defect quantity 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 that studies the temporal and spatial distribution of the cumulative number of defects, initial defect size, and defect distribution location of heat transfer tubes. The existing scheme for the defect growth rate obtained in step 1 has given a specific prediction model, and the log-normal distribution model is used to predict the defect growth rate. However, the prediction method given by the existing scheme does not reflect the relationship between the defect growth rate and time, while the present invention takes into account the change of the defect growth rate over time. Therefore, step 2 to establish a defect growth rate model, a cumulative defect number development history model, an initial defect size probability distribution model, and a defect distribution location model are original research results, and the method for establishing the above models is as follows:

[0037] (1) Establishing a cumulative defect quantity development history model

[0038] 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.

[0039] The following are some examples:

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

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

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

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

[0044]

[0045] (2) Establishing the initial defect size probability distribution model

[0046] 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.

[0047] Figure 3The probability density functions of the steam generators are given, and it can be seen that their distribution is asymmetric. In addition, the initial defect sizes are all positive values. Therefore, four distribution models, namely lognormal distribution, loglogistic distribution, Weibull distribution, and extreme value distribution, are used to perform regression analysis on the actual probability density distribution of the initial defect sizes. The results of the regression analysis are shown in Figure 2. Figure 3 As shown in Figure 2, it can be seen that both the log-logistic distribution and the log-normal distribution can accurately fit the initial defect size distribution.

[0048] (3) Establish defect distribution location model

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

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

[0051] (4) Establishing defect growth rate model

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

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

[0054] Figure 4 The cumulative distribution of the measured defect growth rate and the cumulative distribution of the defect growth rate simulated by the log-normal distribution are shown. Figure 4 It can be seen that the log-normal distribution model can accurately simulate the distribution of 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 defect growth rate using the defect growth rate data measured during the most recent overhaul ;

[0057] a2) Standard deviation of defect growth rate Traverse the range of [0.5,0.9] to select values, i=1,2,3...n; randomly generate lnX~ that satisfies the log-normal distribution The defect growth rate is 100,000 sample points;

[0058] a3) Generate 100,000 numbers that satisfy Y~ Normally distributed measurement error sample points, standard deviation of measurement error Traverse the range of [0,2] to obtain values, j=1,2,3,...m;

[0059] a4) superimposing 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 value, find the maximum R ij 2 The value corresponds to and The obtained simulated defect growth rate cumulative distribution curve is the defect growth rate probability distribution model described in step a).

[0062] Step b. Use the second user-defined function to perform regression analysis on the relationship between the defect growth rate and time measured in previous overhauls to obtain a defect growth rate development history model.

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

[0064] In one embodiment, the second custom exponential function , where a, b, 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 were determined, and the defect growth rate development model was obtained as follows:

[0066]

[0067] The unit of t is generally effective full power year (EFPY).

[0068] Figure 5 The goodness of fit R of the defect growth rate development history model relative to the curve of the mean defect growth rate changing with time is obtained by regression analysis. 2 =0.998, so it can accurately reflect the time-varying pattern of the average level of defect growth rate.

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

[0070] refer to Figure 4 , after a given confidence probability value ( Figure 4 The vertical axis value) can be directly Figure 4 The corresponding defect growth rate is obtained. Here, the conservativeness of the defect growth rate prediction value can be adjusted by giving a higher confidence probability value. The larger the confidence probability, the larger the defect growth rate, and thus the more conservative it is.

[0071] Step d. 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.

[0072] Optionally, the defect growth rate model is established by the following formula:

[0073]

[0074] 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.

[0075] Step 3: 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 obtained in step 2, including:

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

[0077] Step 3.2: The cumulative number of defects obtained in step 3.1 minus the number of existing defects in the current steam generator heat transfer tubes is used to obtain the number of new defects at time t in the future.

[0078] Step 3.3: Use the defect distribution location model obtained in step 2 to predict the location distribution of the corresponding number of newly added 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 of each newly added defect location obtained in step 3.3 at the 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 obtain the initial defect size Size_Initail;

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

[0082] The present disclosure can adjust the conservativeness of the initial defect size prediction value by confidence probability. The greater the confidence probability, the greater the defect growth rate, and thus the more conservative it is.

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

[0085] The present application also includes a defect prediction device for a steam generator heat transfer tube, comprising a memory and a processor, wherein the memory is used to store instructions executable by the processor; and 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 a defect prediction device according to an embodiment of the present disclosure. 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 on a personal computer, the defect prediction device 600 may also include a hard disk 606. The internal communication bus 601 can realize 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 can be composed of one or more processors. The communication port 605 can realize 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 a hard disk 606, a read-only memory (ROM) 603 and a random access memory (RAM) 604, which can store various data files used for computer processing and / or communication, and possible program instructions executed by the processor 602. The processor executes these instructions to implement the main part of the method. The processing result of the processor is transmitted to the user device through the communication port and displayed on the user interface.

[0087] The above-mentioned 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 the present application.

[0088] The present application also includes a computer-readable medium storing a computer program code, which implements the above 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, a 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 memories (EPROMs), 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" can 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 system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously. At the same time, other operations may be added to these processes, or one or more operations may 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 used as an example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0092] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some 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 performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may 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, various aspects of the present application may be represented as computer products located in one or more computer-readable media, which include computer-readable program codes. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, tapes ...), optical disks (e.g., compact disks CDs, digital versatile disks DVDs ...), smart cards, and flash memory devices (e.g., cards, sticks, key drives ...).

[0094] A computer-readable medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may have a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium, which may be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit a program for use. The program code on the computer-readable medium may be propagated via any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or any combination of the above mediums.

[0095] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of the present application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the subject of the present application requires more features than the features mentioned. In fact, the features of the embodiment are less than all the features of the single embodiment disclosed above.

[0096] As shown in this application, unless the context clearly indicates an exception, the words "a", "an", "a kind" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" 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. The method or device may also include other steps or elements.

[0097] Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments do not limit the scope of the application. Meanwhile, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to actual proportional relationships. The technology, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be considered as a part of the specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0098] In addition, it should be noted that the use of words such as "first" and "second" to define components is only for the convenience of distinguishing the corresponding components. If not otherwise stated, the above words have no special meaning and cannot be understood as limiting the scope of protection of this application. In addition, although the terms used in this application are selected from well-known and commonly used terms, some terms mentioned in the specification of this application may be selected by the applicant at his or her discretion, and their detailed meanings are explained in the relevant parts of the description of this article. In addition, it is required to understand this application not only by the actual terms used, but also by the meaning implied by each term.

[0099] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used for the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification are approximate values, which can be changed according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the method of general digit retention. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0100] Although the present application has been described with reference to the current specific embodiments, ordinary technicians in this technical field should recognize that the above embodiments are only used to illustrate the present application, and various equivalent changes or substitutions may 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 essential 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 prediction model for the steam generator heat transfer tube is used to predict the defect state of the steam generator heat transfer tube at the future time t, and the prediction results include the number of new defects and the defect size of the defect location where the defect has occurred.

2. The defect prediction method according to claim 1, characterized in that: Establishing the defect growth rate model includes: The log-normal distribution model is used to simulate the distribution of the defect growth rate measured in the most recent overhaul, and a probability distribution model of the defect growth rate is established; The second custom function is used to conduct regression analysis on the relationship between the defect growth rate and time measured in previous overhauls, and the defect growth rate development history model is obtained; Substituting the given confidence probability into the defect growth rate probability distribution model to obtain the defect growth rate of the most recent overhaul; The defect growth rate model is established according to the defect growth rate of the most recent overhaul and the defect growth rate development history model.

3. The defect prediction method according to claim 2, characterized in that: The defect growth rate model established is: 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.

4. The defect prediction method according to claim 3, 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.

5. 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.

6. The defect prediction method according to claim 5, 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.

7. The defect prediction method according to claim 6, 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.

8. The defect prediction method according to claim 7, characterized in that: The prediction result also includes the new defect location distribution, and the prediction of the new 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.

9. The defect prediction method according to claim 8, 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.

10. The defect prediction method according to claim 9, 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.

11. 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.

12. 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 11.

13. 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 11 is implemented.

Images