Method, device and storage medium for analyzing steam generator tube fatigue reliability

Abstract

The application discloses a steam generating tube fatigue reliability analysis method, equipment and storage medium, which comprises the following steps: 1, using three-dimensional modeling software to establish a digital prototype simulation model of the steam generating tube and the matching structure; 2, using ANSYS finite element software to analyze the stress and strain response of the steam generating tube under the action of temperature load, pressure load and thermal shock load, so as to determine the test position of the steam generating tube; 3, according to the probability fatigue life curve of the steam generating tube structure, a steam generating tube fatigue failure analysis model is constructed; 4, based on steps 2 and 3, the fatigue reliability of the steam generating tube is estimated by using subset simulation combined with adaptive Kriging. The method finally realizes the evaluation of the fatigue reliability of the nuclear steam generating tube, and improves the fatigue reliability analysis efficiency of the steam generating tube.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of reliability analysis, and particularly relates to a steam generating tube fatigue reliability analysis method, equipment and storage medium. BACKGROUND

[0002] The steam generator is a very important heat exchange equipment in the nuclear industry heat energy device, and is also a weak link in the whole heat energy device. The failure modes of the steam generator include corrosion, abrasion, fatigue, vibration and the like. The operation experience abroad shows that about 50% of the pressurized water reactors are affected in normal operation, reduced in power operation or even forced to shut down due to the damage of the steam generator. The steam generating tube is the smallest steam generating unit. Hundreds of steam generating tubes are connected to form the steam generator whole machine by being assembled and welded with the titanium alloy tube plate. The steam generating tube is a heat transfer interface of the primary and secondary loops, and is also a part of the main system pressure boundary. The steam generating tube is one of the weakest links of the steam generator. If the steam generating tube is broken and the safety facilities are also failed, a disastrous consequence of the reactor melting and huge economic losses will be caused. The previous fault mode and influence analysis of the steam generating tube shows that the stress fatigue failure is a typical failure mechanism of the cracking and damage of the steam generating tube. Therefore, it is of great significance to accurately and efficiently realize the fatigue reliability evaluation of the nuclear steam generating tube for the reliability prediction and management of the steam generator. SUMMARY

[0003] The steam generating tube fatigue reliability analysis method, equipment and storage medium provided by the present application can at least solve one of the technical problems in the background art. The present application provides a fatigue life reliability analysis method for the steam generating tube component, and can be applied to the efficient evaluation and fatigue reliability early warning of the steam generating tube fatigue reliability under the action of multiple field loads.

[0004] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0005] A steam generating tube fatigue reliability analysis method, comprising the following steps,

[0006] Step 1: using a three-dimensional modeling software to establish a digital prototype simulation model of the steam generating tube and the matching structure;

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

[0008] Step 3: constructing a steam generating tube fatigue failure analysis model according to the probability fatigue life curve of the steam generating tube structure; recording the steam generating tube failure state function as Y=g(X), wherein X={X1,X2,...,X n} TY represents the difference between the actual service life of the steam generating tube and the design life of the steam generating tube;

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

[0010] Further, step 1 uses UG software to establish a three-dimensional prototype simulation model of the steam generating tube, and the established steam generating tube model considers the welding process structure of the steam generating tube and the assembly connection with the tube plate of the feedwater header.

[0011] Further, step 2 specifically includes,

[0012] Step 2.1: define material properties;

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

[0014] Step 2.3: boundary condition application; since the steam generating tube is assembled by welding the transition pipe section and the steam generating tube plate, 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 pipe section and the tube plate of the steam generating tube are coupled, and the boundary condition is applied in the form of fixed non-separable contact surface;

[0015] Step 2.4: mesh division; in thermal analysis, the mesh division adopts quadratic heat transfer tetrahedral mesh; in force-thermal coupling analysis, the mesh selects 4-node thermal couple tetrahedral mesh;

[0016] Step 2.5: analysis of calculation results; load the temperature load, thermal shock load and water pressure load on the steam generating tube model to obtain the stress and strain distribution cloud diagram under different working conditions; on this basis, the dangerous point position of the steam generating tube is determined based on the maximum stress principle, and the dangerous point position is taken as the fatigue examination position;

[0017] Further, 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 plastic coefficient, and c is the fatigue plastic index.

[0022] In order to describe the dispersion of low-cycle fatigue life, the Manson-Coffin formula is randomized to establish a probability model of low-cycle life of the steam generating tube 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, and the coefficients a e , a p , b e , b p of the elastic-plastic standard linear equation are all random variables, are their mean values, 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:

[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; wherein the parameter estimates of the elastic segment and the plastic segment are respectively

[0027] Step 3.2: Failure State Function Construction; For the reliability analysis of the steam generator pipe, considering the uncertainties in its material properties and external loads, the elastic modulus, thermal conductivity, Poisson's ratio of the steam generator pipe material at 20℃, 100℃, 200℃, and 300℃, as well as the water pressure borne by the steam generator pipe, can be regarded as uncertain variables following a normal distribution, denoted as X. R Furthermore, the standard normal random variable μ in the probabilistic life model also affects the fatigue life of the steam generator tube, thus influencing the fatigue life N of the steam generator tube. f The input variables are represented as X = {X R ,μ} T Fatigue life N f Represented as a function N of X f (X); The design value for the fatigue life of the steam generator pipe is set to N. D Then the fatigue failure state function of the steam generator pipe is expressed as:

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

[0029] Furthermore, step 4 specifically includes,

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

[0031] Step 4.2: Based on the probability density function f of the input variable X X (x) Number of samples N k To form a pool of candidate samples

[0032] Step 4.3: From S k q0 samples are randomly selected from the middle As initial training samples, the state function values ​​are calculated. Construct training sample set T ss ={x t ,g t}. G t Sort the values ​​in ascending order, and denote the sorted result as follows: The [p0q0]th value in the sequence is taken as the intermediate failure event F. k Failure threshold b k ,Right now

[0033] Step 4.4: Based on the training sample set T ss Establish a Kriging surrogate model for the failure state function g(x)

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

[0035] Step 4.6: Calculate S using the following formula k N in k Improved U-learning function values ​​for each sample:

[0036]

[0037] Step 4.7: When show In S k If convergence has been achieved, proceed to step 4.8; otherwise, select sample points. Calculate g(x) u ), and with (x u ,g(x u Update training set T ss The updated T ss For T ss =T ss ∪(x u ,g(x u Then return to step 4.4;

[0038] Step 4.8: From S k Select the failure domain F k The samples inside, i.e. The sample is denoted as Q k Indicates the failure domain F k The number of samples within; then, the conditional failure probability P is calculated using the following formula. k The estimated value

[0039]

[0040] Step 4.9: If b k =0, ending the SS-AK method, the final estimated failure probability is obtained. for Otherwise, proceed to step 4.10;

[0041] Step 4.10: Let k = k + 1, and apply it in the failure domain F k-1 M in k-1 One failed sample As the initial sample for a Markov chain, a conditional distribution of size Nk is generated using the MCMC method. sample pool

[0042] Step 4.11: Use Calculate S k N in k The predicted mean of the failure state function for each sample Sort the values ​​in ascending order, and denote the sorted result as follows: With the [p0N]th of this sequence k ] values ​​as intermediate failure events F k 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 above parameters are solved using linear heteroscedasticity regression analysis.

[0044] Furthermore, in step 3.1, based on the task profile load of the steam generator tube in step 2.2, the fatigue life N of the steam generator tube is calculated using the linear cumulative damage theory—Miner's theory. f .

[0045] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0046] In another aspect, the present invention also 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 performs the steps of the method described above.

[0047] As can be seen from the above technical solutions, based on the aforementioned problems, this invention proposes a fatigue reliability analysis method for steam generator pipes that combines subset simulation with an adaptive Kriging model for estimating the fatigue failure probability. This method establishes a digital prototype model of the steam generator pipe and uses finite element simulation to determine the test components. A fatigue reliability assessment model is constructed based on the probabilistic fatigue life curve of the steam generator pipe. Furthermore, based on the subset simulation algorithm principle, the failure probability of the steam generator pipe is expressed as a product of a series of intermediate failure probabilities. An iteratively updated Kriging model is used to calculate and obtain the intermediate failure probabilities. Ultimately, this method achieves the assessment of the fatigue reliability of nuclear steam generator pipes while improving the efficiency of fatigue reliability analysis.

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

[0049] a) The present application can quantitatively evaluate the steam generating tube fatigue failure probability;

[0050] b) In the process of evaluating the steam generating tube fatigue reliability, the present application considers the random uncertainty of multiple source influencing factors, which is more in line with engineering practice;

[0051] c) The present application unifies the uncertainty caused by material properties, load history, geometric size and prediction model into the distribution of life by establishing a probabilistic fatigue analysis model;

[0052] d) The present application expresses the steam generating tube failure probability as the product of intermediate failure probability, which reduces the sample size of failure probability estimation;

[0053] e) The present application uses adaptive Kriging method to calculate the intermediate failure probability, which improves the efficiency of steam generating tube reliability evaluation.

[0054] In summary, the present application can effectively calculate the fatigue failure probability of the steam generating tube, and is not only suitable for the reliability evaluation of the steam generating tube in the field of nuclear energy, but also suitable for the fatigue reliability evaluation of the steam generating tube in the fields of chemical industry, petroleum and the like. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is a local thermal analysis result diagram of the steam generating tube of the embodiment of the present application;

[0056] Figure 2 is a flowchart of the SS-AK algorithm;

[0057] Figure 3 is a general technical route diagram of the steam generating tube fatigue reliability analysis of the embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical scheme and advantages of the embodiment of the present application more clear, the technical scheme in the embodiment of the present application will be described clearly and completely below in combination with the drawings of the embodiment of the present application. Obviously, the described embodiment is a part of the embodiments of the present application, rather than all the embodiments of the present application.

[0059] For the reliability evaluation of the steam generating tube, the present application proposes a calculation method of the steam generating tube probabilistic fatigue life on the basis of the combination of the subset simulation algorithm and the adaptive Kriging algorithm. The method expresses the failure probability of the steam generating tube as the product of a series of intermediate failure probabilities, and uses the Kriging model (Kriging) updated by iteration to calculate and obtain the intermediate failure probability, and finally calculates the fatigue failure probability of the steam generating tube. The algorithm content of the present application is as follows:

[0060] Step 1: Use 3D modeling software to create a digital prototype simulation model of the steam generator pipe and its supporting structures;

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

[0062] Step 3: Based on the probabilistic fatigue life curve of the steam generator pipe structure, construct a fatigue failure analysis model for the steam generator pipe. Let the failure state function of the steam generator pipe be Y = g(X), where X = {X1, X2, ..., X...} n} T Y represents the random input variables that affect the fatigue life of the steam generator pipe, such as material properties, external loads, and uncertain parameters of the PSN curve.

[0063] Step 4: Use subset simulation combined with adaptive Kriging (SS-AK) to estimate the fatigue reliability of the steam generator tube.

[0064] The following is a detailed explanation:

[0065] The fatigue reliability analysis of the steam generating pipe in this invention follows Figure 3 The overall technical roadmap is shown. Based on this, the present invention, in conjunction with the accompanying drawings, provides a detailed description of the steam generator tube fatigue reliability analysis method combining subset simulation and adaptive Kriging through a specific implementation example. However, the exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make the invention more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics can be presented in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure.

[0066] Step 1: Use UG software to create a 3D prototype simulation model of the steam generator pipe. The steam generator pipe model established in this invention takes into account the welding process structure of the steam generator pipe and its assembly connection with the feedwater header tube sheet;

[0067] Step 2: Use Ansys finite element software to analyze the stress and strain of the steam generator pipe model established in Step 1 under temperature load, pressure load, and impact load, and determine the fatigue analysis test areas. For this invention example, the finite element simulation analysis includes the following steps:

[0068] Step 2.1: Define material properties. The steam generator in the present application adopts titanium alloy, which has the physical properties of high thermal conductivity and low expansion coefficient, and the 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℃ are as shown in the following table.

[0069] Table 1 Table of mechanical properties of titanium alloy materials

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

[0071] Table 2 Table of physical properties of titanium alloy materials

[0072]

[0073] Step 2.2: Load analysis and application. According to the given typical task profile of the steam generator tube, thermal analysis of the steam generator tube is carried out in ANSYS, and the transient temperature field and inner and outer wall temperature history of the steam generator tube wall are calculated. For different working conditions of the steam generator tube wall, the corresponding inner and outer wall temperature 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, in the process of static force analysis, the applied load also includes thermal shock load and water pressure load.

[0074] Step 2.3: Apply boundary conditions. The steam generator tube of the present application is assembled by welding the transition pipe section and the steam generator tube plate, which restricts the axial displacement and radial displacement of the steam generator tube through the weld, and belongs to rigid connection. Therefore, the present application couples the corresponding nodes between the transition pipe section and the tube plate of the steam generator tube, and applies the boundary conditions in the form of non-separable contact surface fixation.

[0075] Step 2.4: Mesh division. In thermal analysis, the mesh division adopts quadratic heat transfer tetrahedral mesh; in force-thermal coupling analysis, the mesh selects 4-node thermal couple tetrahedral mesh.

[0076] Step 2.5: Analysis of calculation results. Load the temperature load, thermal shock load and water pressure load on the model of the steam generator tube to obtain the stress and strain distribution cloud diagram under different working conditions. Figure 1 The local thermal analysis stress cloud diagram of the steam generator tube is given. On this basis, the dangerous point position of the steam generator tube is determined based on the stress maximum principle, and the dangerous point position is taken as the fatigue assessment position. Considering that under the action of temperature load, impact load and water pressure load, the stress at the assessment point is often large, which is easy to enter the plastic zone and cause low cycle fatigue failure, therefore, the present application mainly considers the low cycle fatigue failure mode.

[0077] Step 3: According to the P-S-N curve of the steam generating tube structure, a fatigue reliability analysis model is established, and a steam generating tube failure state function Y=g(X) is obtained, wherein X={X1, X2,..., X n} T represents the random input variables such as material properties, external force load, P-S-N curve, and the like affecting the fatigue life of the steam generating tube, Y represents the difference between the actual life and the design life of the steam generating tube, and the model construction and analysis calculation process is 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, and the Manson-Coffin formula is used in the present application to predict the fatigue life of the steam generating tube, and 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 the low-cycle fatigue life, the Manson-Coffin formula is randomized to establish a probabilistic model of the low-cycle life of the steam generating tube 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, and the coefficients a e , a p , b e , and b p of the elastic-plastic standard linear equation are all random variables, are their mean values, μ is a standard normal random variable. The low cycle probability fatigue life model of steam generator tube can be obtained as follows:

[0084]

[0085] where Δε is the total strain at the critical point, N t is the low cycle fatigue life of the turbine blade. The parameter estimates of the elastic and plastic segments are f The above parameters can be solved by using linear heteroscedastic regression analysis method.

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

[0087] Step 3.2: Failure state function construction. For the reliability analysis of the steam generator tube, considering the uncertainty of the material properties and the uncertainty of the external load, the elastic modulus, the thermal conductivity, the Poisson's ratio of the steam generator tube material at 20℃, 100℃, 200℃ and 300℃, and the water pressure borne by the steam generator tube can be regarded as uncertainty variables subject to normal distribution, denoted as X R . In addition, the standard normal random variable μ in the probability life model also affects the fatigue life of the steam generator tube, so the input variables affecting the fatigue life N f of the steam generator tube can be expressed as X={X R , μ} T , and the fatigue life N f can be expressed as a function of X, N f (X). Assuming that the design value of the fatigue life of the steam generator tube is N D , the fatigue failure state function of the steam generator tube can be expressed as

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

[0089] Step 4: Estimate the fatigue reliability of the steam generator tube by using the SS-AK method. The implementation flowchart of the SS-AK algorithm is shown in Figure 2 , and the specific steps include:

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

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

[0092] Step 4.3: From S k q0 samples are randomly selected from the middle As initial training samples, the state function values ​​are calculated. Construct training sample set T ss ={x t ,g t}. G t Sort the values ​​in ascending order, and denote the sorted result as follows: The [p0q0]th value in the sequence is taken as the intermediate failure event F. k Failure threshold b k ,Right now

[0093] Step 4.4: Based on the training sample set T ss Establish a Kriging surrogate model for the failure state function g(x)

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

[0095] Step 4.6: Calculate S using the following formula k N in k Improved U-learning function values ​​for each sample:

[0096]

[0097] Step 4.7: When show In S k If convergence has been achieved, proceed to step 4.8; otherwise, select sample points. Calculate g(x) u ), and with (x u ,g(x u Update training set T ss The updated T ss For 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 inside, i.e. The sample is denoted as Q k Indicates the failure domain F k The number of samples within the range. Then, the conditional failure probability P is calculated using the following formula. k The estimated value

[0099]

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

[0101] Step 4.10: Let k = k + 1, and apply it in the failure domain F k-1 M in k-1 One failed sample As the initial sample for a Markov chain, a conditional distribution of size Nk is generated using the MCMC method. sample pool

[0102] Step 4.11: Use Calculate S k N in k The predicted mean of the failure state function for each sample Sort the values ​​in ascending order, and denote the sorted result as follows: With the [p0N]th of this sequence k ] values ​​as intermediate failure events F k 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] Based on the above steps, the failure probability value of the steam generator tube fatigue reliability in this paper is calculated to be 4.19 × 10⁻⁶ using the SS-AK method. -4 The finite element model was called 10 times. 3 The order of magnitude. In related technologies, Monte Carlo numerical simulation can also estimate the fatigue reliability of steam generator pipes. Under the same computational accuracy, the Monte Carlo simulation method requires 10 calls to the finite element model of the steam generator pipe. 6 The magnitude of the difference indicates that the method presented in this paper, with the same computational accuracy, requires far less computation than the Monte Carlo method described above, greatly improving the efficiency of fatigue reliability analysis of steam generator pipes.

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

[0105] The present application proposes a nuclear steam generator tube fatigue failure reliability analysis method for steam generator tube fatigue failure probability estimation. The method establishes a digital prototype model of the steam generator tube, determines the test site by using finite element simulation method, constructs a fatigue reliability evaluation model based on the probability fatigue probability life curve of the steam generator tube, and based on the principle of subset simulation algorithm, expresses the failure probability of the steam generator tube as the product of a series of intermediate failure probabilities, and calculates and obtains the intermediate failure probability by using the Kriging model (Kriging) updated by iteration, to finally realize the evaluation of nuclear steam generator tube fatigue reliability and improve the fatigue reliability analysis efficiency of the steam generator tube.

[0106] In another aspect, the present application also discloses a computer readable storage medium storing a computer program, which makes the processor execute the steps of the above method when executed by the processor.

[0107] In another aspect, the present application also discloses a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program makes the processor execute the steps of the above method when executed by the processor.

[0108] In another embodiment provided in the present application, a computer program product containing instructions is also provided, which makes the computer execute the steam generator tube fatigue reliability analysis method in any of the above embodiments when running on the computer.

[0109] It can be understood that the system, device and storage medium provided by the embodiments of the present application correspond to the method provided by the embodiments of the present application, and the explanation, examples and beneficial effects of the related contents can be referred to the corresponding part in the above method.

[0110] In the embodiments described above, all or some of the steps can be implemented by hardware, software, firmware or any combination thereof. When implemented by software, all or some of the steps can be implemented in the form of one or more computer programs or program elements. The computer programs reside (at least temporarily) in a memory of a computer during execution. The memory can be a RAM memory, a flash memory, a ROM memory, an EPROM memory, or any other suitable memory. The memory can be integral to or separate from the computer. The computer programs can be written in any suitable programming language, such as C, C++, Java, Visual Basic, etc. The computer programs can be written in assembly or machine language, if desired. The computer programs can be distributed over network coupled file servers, or can be distributed by any other suitable means.

[0111] It is to be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily implying any actual relationship or order between such entities or actions. Also, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. In addition, terms such as "first" and "second" are used herein for purposes of nomenclature only and are not intended to order or sequence unless explicitly stated otherwise.

[0112] Each of the embodiments described in the present specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other. Each of the embodiments mainly describes the difference from other embodiments. In particular, the system embodiments are described simply because they are basically similar to the method embodiments, and the same or similar parts can be referred to the description of the method embodiments.

[0113] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for fatigue reliability analysis of steam generator pipes, characterized in that, Includes the following steps, Step 1: Use 3D modeling software to create a digital prototype simulation model of the steam generator pipe and its supporting structures; Step 2: Use ANSYS finite element software to analyze the stress and strain response of the steam generating pipe established in Step 1 under temperature load, pressure load, and thermal shock load, so as to determine the test parts of the steam generating pipe. Step 3: Based on the probabilistic fatigue life curve of the steam generator pipe structure, construct a fatigue failure analysis model for the steam generator pipe; denoted as the failure state function of the steam generator pipe is... ,in The random input variables represent the influence of uncertain parameters of material properties, external loads, and PSN curves on the fatigue life of steam generator pipes. This represents the difference between the actual lifespan and the designed lifespan of the steam generator pipe. Step 4: Based on Step 2 and Step 3, use subset simulation combined with adaptive Kriging to estimate the fatigue reliability of the steam generator tube; Step 3 specifically includes, Step 3.1: Determine the low-cycle probabilistic fatigue analysis model for the steam generator pipe; The fatigue life of steam generator tubes is predicted using the Manson-Coffin formula, which is expressed as follows: （1） In the above formula, The total strain amplitude, For elastic strain amplitude, For plastic strain amplitude, The fatigue strength coefficient, The fatigue strength index, The fatigue plasticity coefficient, The fatigue plasticity index; To describe the dispersion of low-cycle fatigue life, the Manson-Coffin formula is randomized, and a probabilistic model for the low-cycle life of the steam generator pipe is established as follows: （2） （3） In the above formula, , , , , , , Let the coefficients of the standard linear equation for elasticity and plasticity be... , , , All are random variables. , , , These are their means, Let the variables be standard normal random variables; the final low-cycle probabilistic fatigue life model of the steam generator pipe is as follows: （4） In the formula, This represents the total strain at the danger point. The low-cycle fatigue life of the turbine blade is given; the parameter estimates for the elastic and plastic segments are respectively... , ; Step 3.2: Failure State Function Construction; For the reliability analysis of the steam generator pipe, considering the uncertainties in its material properties and external loads, the elastic modulus, thermal conductivity, Poisson's ratio of the steam generator pipe material at 20℃, 100℃, 200℃, and 300℃, as well as the water pressure borne by the steam generator pipe, can be regarded as uncertain variables following a normal distribution, denoted as... Furthermore, in the probabilistic lifetime model, the standard normal random variable... It also affects the fatigue life of the steam generator pipe, thus influencing the fatigue life of the steam generator pipe. The input variables are represented as Fatigue life Indicated as about function The design value for the fatigue life of the steam generator pipe is set to... Then the fatigue failure state function of the steam generator pipe is expressed as: （5）。 2. The fatigue reliability analysis method for steam generating pipes according to claim 1, characterized in that: Step 1: Use UG software to create a 3D prototype simulation model of the steam generator pipe. The created steam generator pipe model takes into account the welding process structure of the steam generator pipe and its assembly connection with the feedwater header pipe sheet.

3. The fatigue reliability analysis method for steam generating pipes 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: Grid generation; Step 2.5: Analysis of calculation results; Apply temperature load, thermal shock load, and water pressure load to the model of the steam generating pipe to obtain stress and strain distribution cloud maps under different working conditions; Based on this, determine the dangerous points of the steam generating pipe based on the principle of maximum stress, and use the dangerous points as the fatigue test sites.

4. The fatigue reliability analysis method for steam generating pipes according to claim 3, characterized in that: Step 2.2 specifically includes: Based on the given typical working profile of the steam generating pipe, thermal analysis of the steam generating pipe is carried out in ANSYS finite element software to calculate and obtain the transient temperature field of the pipe wall and the temperature time history of the inner and outer walls. For different working states of the steam generating pipe wall, the corresponding inner and outer wall temperature time histories are applied as known parameters to the corresponding nodes of the inner and outer walls of the steam generating pipe analysis model. Considering load pairs, the applied loads in the static analysis process also include thermal shock loads and water pressure loads.

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

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

7. The fatigue reliability analysis method for steam generating pipes according to claim 1, characterized in that: Step 4 specifically includes, Step 4.1: Set the pointer and conditional probability value initial value, , ,in Intermediate failure event Serial number; Step 4.2: Based on the input variables probability density function Number of samples drawn To form a pool of candidate samples ; Step 4.3: From Randomly selected from Sample As initial training samples, the state function values ​​are calculated. Construct a training sample set ;Will Sort the values ​​in ascending order, and denote the sorted result as follows: , with the first in the sequence Each value is used as an intermediate failure event. Failure threshold ,Right now ; Step 4.4: Based on the training sample set Establish failure state function Kriging proxy model ; Step 4.5: Through the proxy model calculate In The predicted mean of the failure state function for each sample and the predicted standard deviation ; Step 4.6: Calculate using the following formula In Improved U-learning function values ​​for each sample: （6） Step 4.7: When ,show exist If convergence has been achieved, proceed to step 4.8; otherwise, select sample points. ,calculate and with Update training set The updated for And return to step 4.4; Step 4.8: From Selecting the failure domain The samples inside, i.e. The sample is denoted as ,in Indicates the failure domain The number of samples within; then, the probability of conditional failure is calculated using the following formula. The estimated value : （7）； Step 4.9: If The SS-AK method is then terminated, and the final estimated failure probability is obtained. for Otherwise, proceed to step 4.10; Step 4.10: Let Used in the failure domain In One failure sample As the initial sample of a Markov chain, a size of [size missing] is generated according to the MCMC method. It follows a conditional distribution sample pool ; Step 4.11: Use calculate In The predicted mean of the failure state function for each sample Sort by numerical value in ascending order, and the sorted result is denoted as , with the first of the sequence Each value is used as an intermediate failure event. Failure threshold ,Right now ;like ,make If the condition is met, return to step 4.6; otherwise, return directly to step 4.

6.

8. The fatigue reliability analysis method for steam generating pipes according to claim 1, characterized in that: In step 3.1, the above parameters are solved using linear heteroscedasticity regression analysis.

9. The fatigue reliability analysis method for steam generating pipes according to claim 1, characterized in that: In step 3.1, based on the task profile load of the steam generator tube in step 2.2, the fatigue life of the steam generator tube is calculated using the linear cumulative damage theory—Miner's theory. .

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 9.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 9.

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