Fatigue reliability analysis method and equipment for steam generation pipe and storage medium

Abstract

The invention discloses a method and equipment for analyzing the fatigue reliability of a steam generation pipe and a storage medium. The method comprises the following steps of: 1, establishing a digital prototype simulation model of the steam generation pipe and a matched structure by using three-dimensional modeling software; 2, analyzing the stress and strain response of the steam generating pipe established in the step 1 under the action of temperature load, pressure load and thermal shock load by using ANSYS finite element software so as to determine an examination part of the steam generating pipe; 3, according to the probability fatigue life curve of the steam generation pipe structure, a steam generation pipe fatigue failure analysis model is constructed; and step 4, estimating the fatigue reliability of the steam generation pipe by using subset simulation in combination with adaptive Kriging based on the step 2 and the step 3. According to the method, evaluation of the fatigue reliability of the nuclear steam generation pipe is finally realized, and meanwhile, the fatigue reliability analysis efficiency of the steam generation pipe is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of reliability analysis, and in particular to a method, equipment and storage medium for analyzing fatigue reliability of a steam generating tube. Background Art

[0002] Steam generator is a very important heat exchange equipment in nuclear industry thermal energy device, and it is also the weak link in the whole thermal energy device. The failure modes of steam generator include corrosion, abrasion, fatigue, vibration, etc. Foreign operation experience shows that about 50% of pressurized water reactors are affected by normal operation, reduced power operation or even forced to shut down due to steam generator damage. Steam generator tube is the smallest steam generating unit. Hundreds of steam generator tubes are welded and connected with titanium alloy tube sheets to form a steam generator. As the heat transfer interface of the first and second circuits, it is also part of the main system pressure boundary and is one of the weakest links of the steam generator. If the rupture of the steam generator tube is accompanied by the failure of the safety facilities, it will cause catastrophic consequences such as melting of the reactor and huge economic losses. In the early stage of the failure mode and influence analysis of steam generator tube, it is shown that stress fatigue failure is the typical failure mechanism of steam generator tube cracking and damage. Accurate and efficient fatigue reliability assessment of nuclear steam generator tube is of great significance for reliability prediction and management of steam generator. Summary of the invention

[0003] A steam generator tube fatigue reliability analysis method, device and storage medium proposed in the present invention can solve at least one of the technical problems in the background technology. The present invention provides a fatigue life reliability analysis method for steam generator tube components, which can be applied to the efficient evaluation of the fatigue reliability of steam generator tubes under multi-field loads and fatigue reliability early warning.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for analyzing the fatigue reliability of a steam generating tube comprises the following steps:

[0006] Step 1: Use 3D modeling software to build a digital prototype simulation model of the steam generating tube and supporting structures;

[0007] Step 2: Use ANSYS finite element software to analyze the stress and strain response of the steam generating tube established in step 1 under the action of temperature load, pressure load, and thermal shock load, so as to determine the assessment position of the steam generating tube;

[0008] Step 3: According to the probabilistic fatigue life curve of the steam generating tube structure, a fatigue failure analysis model of the steam generating tube is constructed; the failure state function of the steam generating tube is recorded as Y = g(X), where X = {X1, X2, ..., X n} Trepresents the random input variables that affect the fatigue life of the steam generating tube, such as material properties, external loads, and uncertain parameters of the PSN curve. Y represents the difference between the actual life and the design life of the steam generating tube.

[0009] Step 4: Based on steps 2 and 3, the fatigue reliability of the steam generating tube is estimated using subset simulation combined with adaptive Kriging.

[0010] Furthermore, in step 1, UG software is used to establish a three-dimensional prototype simulation model of the steam generating tube. The established steam generating tube model takes into account the welding process structure of the steam generating tube and its assembly connection with the water supply header tube plate.

[0011] Furthermore, step 2 specifically includes:

[0012] Step 2.1: Define material properties;

[0013] Step 2.2: Load analysis and application; Based on the given typical mission profile of the steam generator tube, the thermal analysis of the steam generator tube is carried out in the ANSYS finite element software to calculate and obtain the transient temperature field of the steam generator tube wall and the inner and outer wall temperature time history; for different working conditions of the steam generator tube wall, the corresponding inner and outer wall temperature time history is applied as a known parameter to the corresponding nodes of the inner and outer walls of the steam generator tube analysis model; considering the load group pair, in the static analysis process, the applied load also includes thermal shock load and water pressure load;

[0014] Step 2.3: Apply boundary conditions; Since the steam generating tube is assembled with the transition tube section and the tube sheet of the steam generating tube by welding, the axial displacement and radial displacement of the steam generating tube are limited by the weld, which is a rigid connection; therefore, the corresponding nodes between the transition tube section of the steam generating tube and the tube sheet are coupled, and the boundary conditions are applied by fixing the contact surface without separation;

[0015] Step 2.4: Divide the mesh; in thermal analysis, the mesh is divided using secondary heat transfer tetrahedron mesh; in force-thermal coupling analysis, the mesh is selected as 4-node thermocouple tetrahedron mesh;

[0016] Step 2.5: Analysis of calculation results; temperature load, thermal shock load and water pressure load are loaded onto the model of the steam generating tube to obtain the stress-strain distribution cloud diagram under different working conditions; on this basis, the dangerous points of the steam generating tube are determined based on the maximum stress principle, and the dangerous points are used as fatigue assessment locations;

[0017] Furthermore, step 3 specifically includes:

[0018] Step 3.1: Determine the low-cycle probabilistic fatigue analysis model of the steam generating tube;

[0019] The Manson-Coffin formula is used to predict the fatigue life of the steam generating tube, and its expression is as follows:

[0020]

[0021] In the above formula, Δε t is the total strain amplitude, Δε e is the elastic strain amplitude, Δε p is the plastic strain amplitude, σ' f is the fatigue strength coefficient, b is the fatigue strength index, ε' f is the fatigue plasticity coefficient, c is the fatigue plasticity index;

[0022] In order to describe the dispersion of low-cycle fatigue life, the Manson-Coffin formula is randomized and the probability model of the low-cycle life of the steam generator tube is established as follows:

[0023]

[0024] In the above formula, y e =y p =lg(2N f ), x e =lg(Δε e / 2), x p =lg(Δε p / 2), a e =-lg(σ' f / E) / b,a p =-lg(ε' f ) / c,b e =1 / b, b p =1 / c, let the coefficient a of the standard linear equation of elastic-plasticity be e , a p , b e , b p are all random variables, are their means respectively, μ is a standard normal random variable; finally, the low-cycle probability fatigue life model of the steam generating tube is as follows:

[0025]

[0026] In the formula, Δε t is the total strain at the dangerous point, N f is the low cycle fatigue life of the turbine blade; the parameter estimates of the elastic section and the plastic section are

[0027] Step 3.2: Construction of failure state function; For the reliability analysis of the steam generating tube, considering the uncertainty of its material properties and the uncertainty of the external load, the elastic modulus, thermal conductivity, Poisson's ratio of the steam generating tube material at 20℃, 100℃, 200℃ and 300℃ and the water pressure on the steam generating tube can be regarded as uncertainty variables that obey the normal distribution, denoted as X R In addition, the standard normal random variable μ in the probabilistic life model also affects the fatigue life of the steam generating tube, thus affecting the fatigue life N of the steam generating tube. f The input variable is represented as X = {X R ,μ} T , fatigue life N f Expressed as a function N about X f (X); Set the fatigue life design value of the steam generating tube to N D , then the fatigue failure state function of the steam generating tube is expressed as

[0028] Y=g(X)=N f -N D (5).

[0029] Furthermore, the step 4 specifically includes:

[0030] Step 4.1: Set the initial values ​​of pointer k and conditional probability value p0, k = 1, p0 = 0.1, where k is the intermediate failure event F k Serial number;

[0031] Step 4.2: According to the probability density function f of the input variable X X (x) Number of samples drawn N k , forming a candidate sample pool

[0032] Step 4.3: From S k Randomly select q0 samples from As the initial training sample, and calculate the state function value Construct training sample set T ss ={x t ,g t}. t Arrange in ascending order of value, and record the result as Take the [p0q0]th value of the sequence as the intermediate failure event F k The failure threshold b k ,Right now

[0033] Step 4.4: According to the training sample set T ss Establish the Kriging surrogate model of the failure state function g(x)

[0034] Step 4.5: Through the proxy model Calculate S k N k The predicted mean of the failure state function of samples and the predicted standard deviation

[0035] Step 4.6: Calculate S by the following formula k N k The improved U learning function value of samples:

[0036]

[0037] Step 4.7: When show In S k If converged, execute step 4.8; otherwise, select the sample point Calculate g(x u ), and (x u ,g(x u )) Update the training set T ss , the updated T ss T ss =T ss ∪(x u ,g(x u )) and return to step 4.4;

[0038] Step 4.8: From S k Select the failure domain F k The samples within The sample is denoted as Where Q k F represents the failure domain k The number of samples within; then, the conditional failure probability P is calculated by the following formula k Estimated value of

[0039]

[0040] Step 4.9: If b k = 0, end the SS-AK method, and the final estimated value of the failure probability is for , otherwise, execute step 4.10;

[0041] Step 4.10: Let k = k + 1, and use k-1 M k-1 Failure samples As the initial sample of the Markov chain, the MCMC method generates a conditional distribution of size Nk Sample pool

[0042] Step 4.11: Use Calculate S k N k The predicted mean of the failure state function of samples Arrange in ascending order of value, and record the result as Take the first [p0N of this sequence k ] value as the intermediate failure event F k The failure threshold b k ,Right now If b k ≤0, let b k =0, return to step 4.6; otherwise, return directly to step 4.6.

[0043] Furthermore, in step 3.1, the linear heteroskedastic regression analysis method is used to solve the above parameters.

[0044] Furthermore, in step 3.1, according to the mission profile load of the steam generating tube in step 2.2, the fatigue life N of the steam generating tube is calculated using the linear cumulative damage theory - Miner theory. f .

[0045] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0046] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0047] It can be seen from the above technical scheme that based on the above problem, for the estimation of the fatigue failure probability of steam generator tubes, the present invention proposes a fatigue reliability analysis method for steam generator tubes combining subset simulation with an adaptive Kriging model. This method establishes a digital prototype model of the steam generator tube and uses a finite element simulation method to determine its assessment location; a fatigue reliability assessment model is constructed based on the probabilistic fatigue probability life curve of the steam generator tube; on this basis, based on the principle of the subset simulation algorithm, the failure probability of the steam generator tube is expressed as the product of a series of intermediate failure probabilities, and the intermediate failure probability is obtained by calculating the Kriging model (Kriging) that is continuously iteratively updated. The method ultimately realizes the fatigue reliability assessment of nuclear steam generator tubes and improves the fatigue reliability analysis efficiency of the steam generator tubes.

[0048] Specifically, the present invention has the following advantages:

[0049] a) The present invention can quantitatively evaluate the probability of fatigue failure of steam generating tubes;

[0050] b) The present invention takes into account the random uncertainty of multi-source influencing factors during the fatigue reliability assessment of steam generating tubes, which is more in line with engineering practice;

[0051] c) The present invention establishes a probabilistic fatigue analysis model to uniformly characterize the uncertainties caused by factors such as material properties, load history, geometric dimensions and prediction models into a life distribution;

[0052] d) The present invention expresses the failure probability of the steam generating tube as the product of the intermediate failure probabilities, thereby reducing the sample size for failure probability estimation;

[0053] e) The present invention adopts the adaptive Kriging method to calculate the intermediate failure probability, thereby improving the reliability assessment efficiency of the steam generating tube.

[0054] In conclusion, the present invention can effectively calculate the fatigue failure probability of steam generating tubes, and is not only applicable to the reliability assessment of steam generating tubes in the nuclear energy field, but also applicable to the fatigue reliability assessment of steam generating tubes in the chemical, petroleum and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a diagram showing the local thermal analysis results of the steam generating tube according to an embodiment of the present invention;

[0056] Figure 2 Implementation flowchart for SS-AK algorithm;

[0057] Figure 3 This is the overall technical roadmap for fatigue reliability analysis of the steam generating tube according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0059] Aiming at the reliability assessment of steam generating tubes, the present invention proposes a method for calculating the probabilistic fatigue life of steam generating tubes based on the combination of subset simulation algorithm and adaptive Kriging algorithm. The method expresses the failure probability of steam generating tubes as the product of a series of intermediate failure probabilities, and uses the Kriging model (Kriging) that is continuously iteratively updated to calculate the intermediate failure probability, and finally calculates the fatigue failure probability of the steam generating tube. The algorithm proposed by the present invention is as follows:

[0060] Step 1: Use 3D modeling software to build a digital prototype simulation model of the steam generating tube and supporting structures;

[0061] Step 2: Use ANSYS finite element software to analyze the stress and strain response of the steam generating tube established in step 1 under the action of temperature load, pressure load, thermal shock load, etc., so as to determine the assessment position of the steam generating tube;

[0062] Step 3: According to the probabilistic fatigue life curve of the steam generating tube structure, a fatigue failure analysis model of the steam generating tube is constructed. The failure state function of the steam generating tube is Y = g(X), where X = {X1, X2, ..., X n} T represents the random input variables that affect the fatigue life of the steam generating tube, such as material properties, external force loads, and uncertain parameters of the PSN curve. Y represents the difference between the actual life and the design life of the steam generating tube.

[0063] Step 4: Estimate the fatigue reliability of the steam generating tube using subset simulation combined with adaptive Kriging (SS-AK).

[0064] The following are specific instructions:

[0065] The fatigue reliability analysis of the steam generating tube of the present invention follows Figure 3 The overall technical route shown. Based on this, the present invention, in conjunction with the accompanying drawings, further describes in detail the fatigue reliability analysis method of the steam generator tube combining subset simulation with adaptive Kriging proposed in the present invention through a specific implementation case. However, the example implementation can be implemented in various forms and should not be understood as being limited to the examples set forth herein; on the contrary, this embodiment is provided to make the present invention more comprehensive and complete, and to fully convey the concept of the example implementation to engineers and technicians in the field. The described features, structures, or characteristics may be in one or more embodiments in any suitable manner. In the following description, many specific details are provided to give a full understanding of the embodiments of the present disclosure.

[0066] Step 1: Use UG software to establish a three-dimensional prototype simulation model of the steam generating tube. The steam generating tube model established in the present invention takes into account the welding process structure of the steam generating tube and its assembly connection with the water header tube plate;

[0067] Step 2: Use Ansys finite element software to analyze the stress and strain of the steam generating tube model established in step 1 under the action of temperature load, pressure load, impact load, etc., and determine the fatigue analysis assessment position. For the present embodiment, the finite element simulation analysis includes the following steps:

[0068] Step 2.1: Define material properties. The steam generator in the present invention uses titanium alloy, which has the physical properties of high thermal conductivity and low expansion coefficient. Its physical properties are strongly anisotropic. The linear expansion coefficient, strength limit, conditional yield strength, elastic modulus, shear modulus, Poisson's ratio, thermal conductivity and specific heat capacity at different temperatures in the range of 20-350°C are shown in the following table.

[0069] Table 1 Mechanical properties of titanium alloy materials

[0070] Temperature(℃) <![CDATA[R m (MPa)]]> <![CDATA[R P0.2 (MPa)]]> <![CDATA[S m (MPa)]]> 20 480～667 ≥372 160 350 ≥235 ≥176 78

[0071] Table 2 Physical properties of titanium alloy materials

[0072]

[0073] Step 2.2: Load analysis and application. According to the given typical mission profile of the steam generator tube, the thermal analysis of the steam generator tube is carried out in ANSYS to calculate the transient temperature field of the steam generator tube wall and the inner and outer wall temperature time history. According to the different working conditions of the steam generator tube wall, the corresponding inner and outer wall temperature time history is applied as a known parameter to the corresponding nodes of the inner and outer walls of the steam generator tube analysis model. Considering the load group pair, during the static analysis process, the applied loads also include thermal shock loads and water pressure loads.

[0074] Step 2.3: Apply boundary conditions. The steam generating tube of the present invention is assembled by welding the transition tube section and the tube sheet of the steam generating tube. The axial displacement and radial displacement of the steam generating tube are limited by the weld, which is a rigid connection. Therefore, the present invention couples the corresponding nodes between the transition tube section of the steam generating tube and the tube sheet, and applies boundary conditions by fixing the contact surface without separation.

[0075] Step 2.4: Mesh division. In thermal analysis, the mesh division adopts secondary heat transfer tetrahedron mesh; in force-thermal coupling analysis, the mesh selection is 4-node thermocouple tetrahedron mesh.

[0076] Step 2.5: Analysis of calculation results: Temperature load, thermal shock load and water pressure load are applied to the model of the steam generating tube to obtain the stress-strain distribution cloud diagram under different working conditions. Figure 1 The local thermal analysis stress cloud diagram of the steam generating tube is given. On this basis, the dangerous points of the steam generating tube are determined based on the maximum stress principle, and the dangerous points are used as fatigue assessment sites. Considering that under the action of temperature load, impact load and water pressure load, the stress at the assessment point is often large, and it is easy to enter the plastic zone and thus cause low-cycle fatigue failure, the example of the present invention mainly considers the low-cycle fatigue failure mode.

[0077] Step 3: According to the PSN curve of the steam generating tube structure, the fatigue reliability analysis model is established to obtain the failure state function of the steam generating tube Y = g(X), where X = {X1, X2, ..., X n} T represents the random input variables that affect the fatigue life of the steam generating tube, such as material properties, external force loads, and uncertain parameters of the PSN curve. Y represents the difference between the actual life and the design life of the steam generating tube. The model construction and analysis calculation process are as follows:

[0078] Step 3.1: Determine the low-cycle probabilistic fatigue analysis model of the steam generating tube. The Manson-Coffin formula is a commonly used low-cycle fatigue life prediction model. The Manson-Coffin formula is used in the present invention to predict the fatigue life of the steam generating tube. The expression is as follows:

[0079]

[0080] In the above formula, Δε t is the total strain amplitude, Δε e is the elastic strain amplitude, Δε p is the plastic strain amplitude, σ' f is the fatigue strength coefficient, b is the fatigue strength index, ε' f is the fatigue plasticity coefficient, and c is the fatigue plasticity index.

[0081] In order to describe the dispersion of low-cycle fatigue life, the Manson-Coffin formula is randomized and the probability model of the low-cycle life of the steam generator tube is established as follows:

[0082]

[0083] In the above formula, y e =y p =lg(2N f ), x e =lg(Δε e / 2), x p =lg(Δε p / 2), a e =-lg(σ' f / E) / b,a p =-lg(ε' f ) / c,b e =1 / b, b p =1 / c, let the coefficient a of the standard linear equation of elastic-plasticity be e , a p , b e , b p are all random variables, are their means respectively, and μ is a standard normal random variable. Finally, the low-cycle probability fatigue life model of the steam generating tube is obtained as follows:

[0084]

[0085] In the formula, Δε t is the total strain at the dangerous point, N f is the low cycle fatigue life of the turbine blade. Among them, the parameter estimates of the elastic section and the plastic section are The above parameters can be solved by linear heteroskedastic regression analysis method.

[0086] According to the mission profile load of the steam generating tube in step 2.2, the fatigue life N of the steam generating tube is calculated using the linear cumulative damage theory - Miner theory. f .

[0087] Step 3.2: Construction of failure state function. For the reliability analysis of the steam generator tube, considering the uncertainty of its material properties and the uncertainty of the external load, the elastic modulus, thermal conductivity, Poisson's ratio of the steam generator tube material at 20℃, 100℃, 200℃ and 300℃ and the water pressure on the steam generator tube can be regarded as uncertainty variables that obey the normal distribution, denoted as X R In addition, the standard normal random variable μ in the probabilistic life model also affects the fatigue life of the steam generating tube, thus affecting the fatigue life N of the steam generating tube. f The input variable can be expressed as X = {X R ,μ} T , fatigue life N f It can be expressed as a function N about X f (X). Set the fatigue life design value of the steam generating tube to N D , then the fatigue failure state function of the steam generating tube can be expressed as

[0088] Y=g(X)=N f -N D (5)

[0089] Step 4: Estimate the fatigue reliability of the steam generating tube using the SS-AK method. The SS-AK algorithm implementation flow chart is as follows: Figure 2 As shown, the specific steps include:

[0090] Step 4.1: Set the initial values ​​of pointer k and conditional probability value p0, k = 1, p0 = 0.1, where k is the intermediate failure event F k The serial number of the

[0091] Step 4.2: According to the probability density function f of the input variable X X (x) Number of samples drawn N k, forming a candidate sample pool

[0092] Step 4.3: From S k Randomly select q0 samples from As the initial training sample, and calculate the state function value Construct training sample set T ss ={x t ,g t}. t Arrange in ascending order of value, and record the result as Take the [p0q0]th value of the sequence as the intermediate failure event F k The failure threshold b k ,Right now

[0093] Step 4.4: According to the training sample set T ss Establish the Kriging surrogate model of the failure state function g(x)

[0094] Step 4.5: Through the proxy model Calculate S k N k The predicted mean of the failure state function of samples and the predicted standard deviation

[0095] Step 4.6: Calculate S by the following formula k N k The improved U learning function value of samples:

[0096]

[0097] Step 4.7: When show In S k If converged, execute step 4.8; otherwise, select the sample point Calculate g(x u ), and (x u ,g(x u )) Update the training set T ss , the updated T ss T ss =T ss ∪(x u ,g(x u )) and return to step 4.4.

[0098] Step 4.8: From S k Select the failure domain F k The samples within The sample is denoted as Where Q k F represents the failure domain k The number of samples within. Then, the conditional failure probability P is calculated by the following formula k Estimated value of

[0099]

[0100] Step 4.9: If b k = 0, end the SS-AK method, and the final estimated value of the failure probability is for Otherwise, proceed to step 4.10.

[0101] Step 4.10: Let k = k + 1, and use k-1 M k-1 Failure samples As the initial sample of the Markov chain, the MCMC method generates a conditional distribution of size Nk Sample pool

[0102] Step 4.11: Use Calculate S k N k The predicted mean of the failure state function of samples Arrange in ascending order of value, and record the result as Take the first [p0N of this sequence k ] value as the intermediate failure event F k The failure threshold b k ,Right now If b k ≤0, let b k =0, return to step 4.6; otherwise, return directly to step 4.6.

[0103] According to the above steps, the failure probability of the fatigue reliability of the steam generating tube in this paper is calculated using the SS-AK method to be 4.19×10 -4 , the number of finite element model calls is 10 3 In the related technology, the Monte Carlo digital simulation method can also estimate the fatigue reliability of the steam generating tube. Under the same calculation accuracy, the Monte Carlo simulation method can call the finite element model of the steam generating tube 10 times. 6 This shows that the method proposed in this paper has a much smaller amount of calculation than the Monte Carlo method under the same calculation accuracy, which greatly improves the efficiency of fatigue reliability analysis of steam generator tubes.

[0104] In summary, the purpose of the present invention is to solve the problem of quantitative analysis of fatigue reliability of nuclear steam generator tubes and to improve the efficiency of fatigue reliability analysis of steam generators. The method solves the problem of single and low efficiency of fatigue reliability assessment of steam generator tubes and is suitable for reliability assessment of steam generator tubes in engineering practice. In addition, the present invention can be used not only in the field of nuclear energy devices, but also in other industrial fields such as chemical industry, petroleum and petrochemical industry.

[0105] Aiming at estimating the probability of fatigue failure of steam generator tubes, the present invention proposes a method for fatigue failure reliability analysis of nuclear steam generator tubes. The method establishes a digital prototype model of the steam generator tubes and determines the assessment position thereof by using a finite element simulation method; a fatigue reliability assessment model is constructed based on the probabilistic fatigue probability life curve of the steam generator tubes; on this basis, based on the principle of subset simulation algorithm, the failure probability of the steam generator tubes is expressed as the product of a series of intermediate failure probabilities, and the intermediate failure probabilities are obtained by calculating the continuously iteratively updated Kriging model (Kriging), thereby finally realizing the fatigue reliability assessment of nuclear steam generator tubes and improving the fatigue reliability analysis efficiency of the steam generator tubes.

[0106] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0107] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0108] In another embodiment provided in the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute any of the steam generating tube fatigue reliability analysis methods in the above embodiments.

[0109] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0110] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0111] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0112] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0113] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing the fatigue reliability of a steam generating tube, characterized in that: The following steps are included: Step 1: Use 3D modeling software to build a digital prototype simulation model of the steam generating tube and supporting structures; Step 2: Use ANSYS finite element software to analyze the stress and strain response of the steam generating tube established in step 1 under the action of temperature load, pressure load, and thermal shock load, so as to determine the assessment position of the steam generating tube; Step 3: According to the probabilistic fatigue life curve of the steam generating tube structure, a fatigue failure analysis model of the steam generating tube is constructed; the failure state function of the steam generating tube is recorded as Y = g(X), where X = {X1, X2, ..., X n } T represents the random input variables that affect the fatigue life of the steam generating tube, such as material properties, external loads, and uncertain parameters of the PSN curve. Y represents the difference between the actual life and the design life of the steam generating tube. Step 4: Based on steps 2 and 3, the fatigue reliability of the steam generating tube is estimated using subset simulation combined with adaptive Kriging.

2. The method for analyzing the fatigue reliability of a steam generating tube according to claim 1, characterized in that: Step 1: Use UG software to establish a three-dimensional prototype simulation model of the steam generating tube. The established steam generating tube model takes into account the welding process structure of the steam generating tube and its assembly connection with the water supply header tube plate.

3. The method for analyzing the fatigue reliability of a steam generating tube according to claim 2, characterized in that: Step 2 specifically includes: Step 2.1: Define material properties; Step 2.2: Load analysis and application; Step 2.3: Apply boundary conditions; Step 2.4: Divide the grid; Step 2.5: Analysis of calculation results; add temperature load, thermal shock load and water pressure load to the model of the steam generating tube to obtain the stress-strain distribution cloud diagram under different working conditions; on this basis, determine the dangerous points of the steam generating tube based on the maximum stress principle, and use the dangerous points as fatigue assessment locations.

4. The method for analyzing the fatigue reliability of a steam generating tube according to claim 3, characterized in that: Step 2.2 specifically includes: According to the given typical mission profile of the steam generating tube, the thermal analysis of the steam generating tube is carried out in the ANSYS finite element software, and the transient temperature field of the tube wall and the temperature time history of the inner and outer walls of the steam generating tube are calculated. According to the different working conditions of the steam generating tube wall, the corresponding temperature time history of the inner and outer walls is applied as a known parameter to the corresponding nodes of the inner and outer walls of the steam generating tube analysis model. Considering the load group pair, in the static analysis process, the applied loads also include thermal shock load and water pressure load.

5. The method for analyzing the fatigue reliability of a steam generating tube according to claim 3, characterized in that: Step 2.3 specifically includes: Since the steam generating tube is assembled by welding the transition tube section and the tube sheet of the steam generating tube, the axial displacement and radial displacement of the steam generating tube are limited by the weld, which is a rigid connection; therefore, the corresponding nodes between the transition tube section of the steam generating tube and the tube sheet are coupled, and the boundary conditions are applied by fixing the contact surface without separation.

6. The method for analyzing the fatigue reliability of a steam generating tube according to claim 3, characterized in that: In step 2.4, in thermal analysis, the mesh is divided into secondary heat transfer tetrahedral mesh; in force-thermal coupling analysis, the mesh is selected as 4-node thermocouple tetrahedral mesh.

7. The method for analyzing the fatigue reliability of a steam generating tube according to claim 1, characterized in that: Step 3 specifically includes: Step 3.1: Determine the low-cycle probabilistic fatigue analysis model of the steam generating tube; The Manson-Coffin formula is used to predict the fatigue life of the steam generating tube, and its expression is as follows: In the above formula, Δε t is the total strain amplitude, Δε e is the elastic strain amplitude, Δε p is the plastic strain amplitude, σ' f is the fatigue strength coefficient, b is the fatigue strength index, ε' f is the fatigue plasticity coefficient, c is the fatigue plasticity index; In order to describe the dispersion of low-cycle fatigue life, the Manson-Coffin formula is randomized and the probability model of the low-cycle life of the steam generator tube is established as follows: In the above formula, y e =y p =lg(2N f ), x e =lg(Δε e / 2), x p =lg(Δε p / 2), a e =-lg(σ' f / E) / b,a p =-lg(ε' f ) / c,b e =1 / b, b p =1 / c, let the coefficient a of the standard linear equation of elastic-plasticity be e , a p , b e , b p are all random variables, are their means respectively, μ is a standard normal random variable; finally, the low-cycle probability fatigue life model of the steam generating tube is as follows: In the formula, Δε t is the total strain at the dangerous point, N f is the low cycle fatigue life of the turbine blade; the parameter estimates of the elastic section and the plastic section are Step 3.2: Construction of failure state function; For the reliability analysis of the steam generating tube, considering the uncertainty of its material properties and the uncertainty of the external load, the elastic modulus, thermal conductivity, Poisson's ratio of the steam generating tube material at 20℃, 100℃, 200℃ and 300℃ and the water pressure on the steam generating tube can be regarded as uncertainty variables that obey the normal distribution, denoted as X R In addition, the standard normal random variable μ in the probabilistic life model also affects the fatigue life of the steam generating tube, thus affecting the fatigue life N of the steam generating tube. f The input variable is represented as X = {X R ,μ} T , fatigue life N f Expressed as a function N about X f (X); Set the fatigue life design value of the steam generating tube to N D , then the fatigue failure state function of the steam generating tube is expressed as Y=g(X)=N f -N D (5)。 8. The method for analyzing the fatigue reliability of a steam generating tube according to claim 1, characterized in that: The step 4 specifically includes: Step 4.1: Set the initial values ​​of pointer k and conditional probability value p0, k = 1, p0 = 0.1, where k is the intermediate failure event F k Serial number; Step 4.2: According to the probability density function f of the input variable X X (x) Number of samples drawn N k , forming a candidate sample pool Step 4.3: From S k Randomly select q0 samples from As the initial training sample, and calculate the state function value Construct training sample set T ss ={x t ,g t }; t Arrange in ascending order of value, and record the result as Take the [p0q0]th value of the sequence as the intermediate failure event F k The failure threshold b k ,Right now Step 4.4: According to the training sample set T ss Establish the Kriging surrogate model of the failure state function g(x) Step 4.5: Through the proxy model Calculate S k N k The predicted mean of the failure state function of samples and the predicted standard deviation Step 4.6: Calculate S by the following formula k N k The improved U learning function value of samples: Step 4.7: When show In S k If converged, execute step 4.8; otherwise, select the sample point Calculate g(x u ), and (x u ,g(x u )) Update the training set T ss , the updated T ss T ss =T ss ∪(x u ,g(x u )) and return to step 4.4; Step 4.8: From S k Select the failure domain F k The samples within The sample is denoted as Where Q k F represents the failure domain k The number of samples within; then, the conditional failure probability P is calculated by the following formula k Estimated value of Step 4.9: If b k = 0, end the SS-AK method, and the final estimated value of the failure probability is for Otherwise, proceed to step 4.10; Step 4.10: Let k = k + 1, and use k-1 M k-1 Failure samples As the initial sample of the Markov chain, the MCMC method generates a size of N k The conditional distribution q X (x|Fk-1)=I Fk-1 (x)f X (x) / P{Fk-1} sample pool Step 4.11: Use Calculate S k N k The predicted mean of the failure state function of samples Arrange in ascending order of value, and record the result as Take the first [p0N of this sequence k ] value as the intermediate failure event F k The failure threshold b k ,Right now If b k ≤0, let b k =0, return to step 4.6; Otherwise, return directly to step 4.

6.

9. The method for analyzing the fatigue reliability of a steam generating tube according to claim 7, characterized in that: In step 3.1, the linear heteroskedastic regression analysis method is used to solve the above parameters.

10. The method for analyzing the fatigue reliability of a steam generating tube according to claim 7, characterized in that: In step 3.1, according to the mission profile load of the steam generating tube in step 2.2, the fatigue life N of the steam generating tube is calculated using the linear cumulative damage theory - Miner theory. f .

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 10.

12. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 10.

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