High-flux near-field dynamics numerical simulation method for in-service equipment damage fracture analysis

Abstract

The invention discloses a high-throughput near-field dynamics numerical simulation method for in-service equipment damage fracture analysis, which uses a neural network to assist in solving to improve model solving efficiency, and combines a near-field dynamics basic theory, a high-throughput calculation method and a data driving model to carry out research. A nuclear reactor system in-service equipment near-field dynamic damage fracture analysis and calculation model is established, a near-field dynamic damage fracture solving high-throughput method is developed, mass data is acquired, and a data driving model based on a neural network is established. According to the method, the solving efficiency of the elastic-plastic near-field dynamic model of the pressure vessel can be greatly improved.

Description

Technical Field

[0001] The present invention relates to the fields of mechanics, deep learning and model simulation technology, and in particular to a high-throughput peridynamic numerical simulation method for damage and fracture analysis of in-service equipment. Background Art

[0002] The structural integrity of the reactor pressure vessel (RPV) is crucial to the safe operation of nuclear power plants as a key device in nuclear power plants. However, during long-term service, pressure vessels are often exposed to harsh environments such as high temperature, high pressure, and high radiation. These factors can lead to the degradation of the mechanical properties of the material and may induce defects such as cracks. Over time, the initiation, expansion, and intersection of cracks may lead to equipment failure and threaten the safe operation of the system.

[0003] Traditional crack analysis methods mainly rely on the finite element method (FEM), which can obtain relatively accurate results in single crack problems. However, when faced with complex situations with multiple cracks and multiple working conditions, the meshing and boundary condition processing of the finite element method become extremely complex, resulting in low computational efficiency and difficulty in meeting the needs of real-time monitoring and evaluation. Therefore, there is an urgent need for a numerical simulation method that can efficiently simulate the damage and fracture behavior of in-service equipment.

[0004] As a meshless numerical method, peridynamics (PD) can effectively overcome the limitations of the traditional finite element method in large deformation and crack propagation problems. It describes the interaction relationship between any two points in the material through a non-local interaction model, and can naturally handle the crack initiation, propagation and intersection process. Combining high-throughput simulation technology and machine learning models, it can achieve rapid prediction and offline evaluation of multi-crack damage fracture behavior of in-service equipment. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to solve the deficiencies in the prior art and to provide a high-throughput peridynamic numerical simulation method for damage and fracture analysis of in-service equipment.

[0006] Technical solution: A high-throughput peridynamic numerical simulation method for damage and fracture analysis of in-service equipment of the present invention comprises the following steps:

[0007] Step 1: Discretize the geometric model of the in-service equipment, define the interaction radius, construct the near field of each material point, and define the physical and mechanical performance parameters of the material;

[0008] Step 2: Determine the initial temperature field, stress field, boundary conditions, total calculation time t and problem solving time step Δt according to the actual working conditions of the equipment, with a total of t / Δt time steps;

[0009] Step 3: Calculate the temperature change ΔT in the model at the current time step based on the distribution of the temperature field in the model at the previous time step, and then use the explicit difference method to calculate the distribution of the temperature field in the model at the current time step;

[0010] Step 4: According to the obtained temperature field of the current time step, the stress field of the previous time step and the boundary conditions, the thermoelastic-plastic constitutive relation of the pressure vessel RPV metal material 16MnD5 steel is introduced to calculate the internal force of the pressure vessel RPV at the current moment, and then the implicit static method is used to calculate the displacement. After meeting the convergence criterion, the displacement of the current time step and the geometric configuration of the in-service equipment are obtained, and then the simulation data is obtained;

[0011] Here, the internal force of the pressure vessel RPV at the current moment is obtained by calculating the sum of the bond force density of each material point;

[0012] If the current calculation time does not reach the total calculation time t, return to step 3 to calculate the temperature field of the next time step; if the current calculation time reaches the total calculation time t, return to step 1; after reaching the specified number of samples, execute step 5;

[0013] Step 5: Based on the simulation data obtained in step 4, a fully connected neural network FCNN (Fully Connected Neural Network) is trained to establish a rapid prediction model for equipment damage and fracture behavior; then, the real-time monitoring data of the in-service equipment is analyzed to obtain the corresponding input data, and the input data is transmitted to the trained rapid prediction model to realize offline prediction of the equipment status.

[0014] The present invention uses a fully connected neural network structure and data processing flow to train a fast prediction model that can quickly predict the damage state of the equipment, with significantly improved accuracy and efficiency. The fast prediction model combines the real-time monitoring data of the in-service equipment to achieve offline prediction and dynamic update, which enhances the timeliness and applicability of the prediction results. The input data in the present invention may include the length of the first crack, the length of the second crack, the angle between the two cracks, the inner wall temperature, and the inner wall pressure five-dimensional vector; the final predicted output data may include the J integral of the first crack, the J integral of the second crack, the maximum opening displacement of the first crack, the maximum opening displacement of the second crack, the average radial displacement of the two-dimensional annulus, the average radial strain of the two-dimensional annulus, the average crack tip damage of the first crack, the average crack tip damage of the second crack, and the inner wall circumference change rate.

[0015] In the above step 1, a large number of simulation models with different crack distributions and working conditions are generated through an automated crack generation program. Batch processing technology and high-performance computing resources are used to perform parallel calculations on all simulation models to obtain damage and fracture behavior data under different working conditions.

[0016] Furthermore, in step 3, an enhanced peridynamic differential operator (E-PDDO) is used to solve the temperature field;

[0017] The local heat conduction equation of peridynamics is expressed as follows:

[0018]

[0019] In the above formula, As an enhanced peridynamic differential operator, the basic solution of the heat conduction equation is introduced compared with the original peridynamic differential operator (PDDO); for material points x and x'∈Ω, the position vectors of the two material points are x and x' respectively, and the relative position between the two material points is ξ=x'-x, H x is the interaction domain of material point x; N is the total number of material points;

[0020] In M-dimensional space, the N-order Taylor series expansion of the function f(x') = f(x+ξ) is:

[0021]

[0022] Among them, n1, n2, ···, n M For variable x k (k=1,…,M) differential order, n i =0,…,N; R(N,x) is the residual term;

[0023] The enhanced peridynamic function is constructed by introducing the fundamental solution of the heat conduction equation and the orthogonal basis:

[0024]

[0025] Among them, p i Represents the variable x k The differential order of i ,q i =0,…,N; is the weight function of the Hermite polynomial For x k The qth component i In step 3, the enhanced peridynamic differential operator (E-PDDO) is used to solve the temperature field, which has higher accuracy and convergence.

[0026] Furthermore, the step 3 uses the explicit difference method to calculate the distribution of the temperature field in the current time step model, that is, the step-by-step integration method is used to complete the calculation of the heat conduction process. When the explicit forward difference format is used, the temperature from the nth step to the n+1th step is:

[0027]

[0028] Txt n+1 ) is x at tn+ 1 The temperature at the moment, ΔT refers to the temperature change, which is The temperature change in a short period of time is calculated, that is, the temperature change ΔT is obtained by multiplying the time by the temperature change rate.

[0029] Furthermore, the detailed process in step 4 is as follows:

[0030] First, solve the peridynamic equation of motion as follows:

[0031]

[0032] Where r is the material density, is the acceleration, u and u′ are the i and the material point x in its near field j displacement, b is the external force density, dV x′ is the material point x j The volume element; H x is the material point x i Near field range;

[0033] Based on the elastic-plastic linear hardening elastic-plastic model, the material point x in the PD thermo-elastic-plastic model is calculated. i and material point x j The bond force density f ji , the formula is as follows:

[0034]

[0035] In the formula, c ji is a material point x j For material point x i The microelastic modulus constant, Represents the material point x i and its near field material point x j The critical elongation of the bond formed between the two electrodes when it enters the yield stage, s ji Represents the material point x i and its near field material point x j The elongation of the bond formed between When the inertia effect is not considered and the temperature effect is considered, the equilibrium equation is as follows:

[0036]

[0037] In the above formula, is the material point x i and other material points x in its near field j The displacement vector of pis the displacement vector between the material point x and other material points x' in its near field, η ji is the material point x i and its near field material point x j The relative displacement between the two points; the displacement vector of each material point can be obtained to obtain the displacement;

[0038] Next, the elastic-plastic linear hardening elastic-plastic model equation is solved and the displacement increment δu is solved by the Newton-Raphson iteration method. p , then:

[0039]

[0040] Where dL is the residual, is the tangent stiffness matrix;

[0041] Then, the tangent stiffness matrix of each material point is assembled into the overall stiffness matrix [K] by using the “matching” method. 2N×2N , and then the incremental equation of motion is written in matrix form as follows:

[0042] [K] 2N×2N [dU] 2N×1 =-[L * +b * ] 2N×1 (9)

[0043] Where N is the total number of material points, the vector matrix δU contains all unknown displacement increments, and K is the overall stiffness matrix; the vector L * +b * A known unbalanced force density is specified;

[0044] The constraint equations are imposed by the Lagrange multiplier method. Finally, the algebraic equations for δU and λ are solved.

[0045]

[0046] Since the global stiffness matrix [K] 2N×2N is a sparse matrix whose dimension depends on the refinement of the geometric model. The stiffness matrix [K] 2N×2N The assembly of the matrix will consume a lot of time and occupy a large amount of computer memory; therefore, the present invention uses a matrix compressed sparse row (CSR) scheme to directly store the stiffness coefficients, and uses a large sparse linear equation solver (such as Intel direct solver PARDISO or Intel iterative solver GMRES) to efficiently solve the above algebraic equations.

[0047] Furthermore, in step 4, a scalar state function μ is introduced to characterize whether the bond is broken or not:

[0048]

[0049] Where s0 is the critical elongation of the bond;

[0050] The local damage of a material point is defined as the weighted ratio of the number of broken bonds to the total number of initial bonds within the near field of the material point, expressed as:

[0051]

[0052] In the formula, the damage value changes from 0 to 1. When , the material point is completely damaged and does not interact with any material point, and the damage value , indicating that all interactions are well preserved.

[0053] Furthermore, in step 5, by optimizing the neural network structure and data processing flow, the trained deep learning model can quickly predict the damage state of the equipment, with significantly improved accuracy and efficiency. The model combines the real-time monitoring data of the equipment to achieve offline prediction and dynamic update, enhancing the timeliness and applicability of the prediction results.

[0054] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0055] (1) The present invention significantly improves the efficiency of damage and fracture behavior analysis of in-service equipment such as pressure vessels by introducing high-throughput computing and machine learning models. Compared with the traditional finite element method, the peridynamic method of the present invention does not require meshing, can automatically adapt to the initiation, expansion and intersection of cracks, and simplifies the analysis process of complex structures.

[0056] (2) The present invention uses an enhanced peridynamic differential operator to perform temperature field analysis, which improves the calculation accuracy. In particular, under complex temperature boundary conditions, it can accurately calculate the transient heat conduction problems of in-service equipment and more accurately predict the damage development of equipment.

[0057] (3) The present invention trains a deep learning model based on a large amount of simulation data, realizes real-time monitoring and offline prediction of equipment status, effectively improves the timeliness of equipment status assessment, and avoids the lag of traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of the overall method of the present invention;

[0059] Figure 2 It is the plane strain model of the RPV cylinder section in the embodiment of the present invention.

[0060] Figure 3 These are the temperature boundary conditions and pressure boundary conditions under the start-up conditions of the pressure vessel in the embodiment of the present invention.

[0061] Figure 4 It is a distribution cloud diagram of damage, radial displacement and circumferential displacement of the RPV cylinder section containing multiple cracks in the embodiment of the present invention.

[0062] Figure 5 1 is a diagram of a neural network structure in an embodiment of the present invention.

[0063] Figure 6 1 is a comparison chart between the predicted value and the true value of the neural network in the embodiment of the present invention. DETAILED DESCRIPTION

[0064] The technical solution of the present invention is described in detail below, but the protection scope of the present invention is not limited to the embodiments.

[0065] like Figure 1 As shown, the high-throughput peridynamic numerical simulation method for damage and fracture analysis of in-service equipment of the present invention comprises the following steps:

[0066] Step 1: Discretize the geometric model of the in-service equipment, define the interaction radius, construct the near field of each material point, and define the physical and mechanical performance parameters of the material;

[0067] Step 2: Determine the initial temperature field, stress field, boundary conditions, total calculation time t, and problem-solving time step Δt according to the actual working conditions of the equipment; (there are t / Δt time steps in total)

[0068] Step 3: Calculate the temperature change ΔT in the model at the current time step based on the distribution of the temperature field in the model at the previous time step, and then use the explicit difference method to calculate the distribution of the temperature field in the model at the current time step;

[0069] Step 4: According to the obtained temperature field of the current time step, the stress field of the previous time step and the boundary conditions, the thermoelastic-plastic constitutive relation of the pressure vessel RPV metal material 16MnD5 steel is introduced to calculate the internal force of the pressure vessel RPV at the current moment, and then the implicit static method is used to calculate the displacement. After meeting the convergence criterion, the displacement of the current time step and the geometric configuration of the in-service equipment are obtained, and then the simulation data is obtained;

[0070] Here, the internal force of the pressure vessel RPV at the current moment is obtained by calculating the sum of the bond force density of each material point;

[0071] If the current calculation time does not reach the total calculation time t, return to step 3 to calculate the temperature field of the next time step; if the current calculation time reaches the total calculation time t, return to step 1; after reaching the specified number of samples, execute step 5;

[0072] Step 5: Based on the simulation data obtained in step 4, a fully connected neural network FCNN is trained to establish a rapid prediction model for equipment damage and fracture behavior;

[0073] Then, the real-time monitoring data of the in-service equipment is analyzed to obtain the corresponding input data, and the input data is transmitted to the trained fast prediction model to realize offline prediction of the equipment status.

[0074] In step 3 of this embodiment, an enhanced peridynamic differential operator (E-PDDO) is used to solve the temperature field;

[0075] The local heat conduction equation of peridynamics is expressed as follows:

[0076]

[0077] In the above formula, is an enhanced peridynamic differential operator; for material points x and x'∈Ω, the position vectors of the two material points are x and x' respectively, and the relative position between the two material points is ξ=x'-x, H x is the interaction domain of material point x;

[0078] In M-dimensional space, the N-order Taylor series expansion of the function f(x') = f(x+ξ) is:

[0079]

[0080] Among them, n1, n2, ···, n M For variable x k (k=1,…,M) differential order, n i =0,…,N; R(N,x) is the residual term;

[0081] The enhanced peridynamic function is constructed by introducing the fundamental solution of the heat conduction equation and the orthogonal basis:

[0082]

[0083] Among them, p i Represents the variable x k The differential order of i ,q i =0,…,N; is the weight function of the Hermite polynomial For x k The qth component i Hermitian polynomials.

[0084] The derivation process of the above-mentioned peridynamic local heat conduction equation (28) in this embodiment is as follows:

[0085] The classic local heat conduction equation is obtained through Fourier's law as follows:

[0086]

[0087] The enhanced peridynamic differential operator is used to replace the local spatial derivative to construct a general bond-type peridynamic nonlocal heat conduction model. Based on the nonlocal integral expression of the second-order local differential, the integral expression of the second-order derivative of the temperature of the material point with a complete symmetric near field domain in the three-dimensional problem is obtained; the integral expression of the temperature derivative is substituted into the local heat conduction equation, and the peridynamic heat conduction equation for the three-dimensional problem is obtained as follows:

[0088]

[0089] When the material point is located in the boundary area, the near field of the material point is incomplete and asymmetric. According to the differential operator formula of peridynamics, the second-order derivative integral expression of the temperature of the three-dimensional problem can be obtained as follows:

[0090]

[0091] In the formula, is the enhanced peridynamic function; then the integral of the temperature derivative is substituted into the local heat conduction equation, and the following peridynamic heat conduction equation is obtained:

[0092]

[0093] Where tr represents the trace of the matrix.

[0094] In step 3 of this embodiment, the temperature field distribution in the current time step model is calculated using the explicit difference method, that is, the heat conduction process calculation is completed using the step-by-step integration method. When the explicit forward difference format is used, the temperature from the nth step to the n+1th step is:

[0095]

[0096] Txt n+1 ) is x at tn+ 1 The temperature at the moment, ΔT refers to the temperature change, which is The temperature change in a short period of time is calculated, that is, the temperature change ΔT is obtained by multiplying the time by the temperature change rate.

[0097] The detailed process of step 4 of this embodiment is as follows:

[0098] First, solve the peridynamic equation of motion as follows:

[0099]

[0100] Where r is the material density, is the acceleration, u and u′ are the i and the material point x in its near field j displacement, b is the external force density, dV x′ is the material point x j The volume element; H x is the material point x i Near field range;

[0101] Based on the elastic-plastic linear hardening elastic-plastic model, the material point x in the PD thermo-elastic-plastic model is calculated. i and material point x j The bond force density f ji , the formula is as follows:

[0102]

[0103] In the formula, c ji is a material point x j For material point x i The microelastic modulus constant, Represents the material point x i and its near field material point x j The critical elongation of the bond formed between the two electrodes when it enters the yield stage, s ji Represents the material point x i and its near field material point x j When the inertia effect is ignored and the temperature effect is considered, the equilibrium equation is as follows:

[0104]

[0105] In the above formula, is the material point x i and other material points x in its near field j The displacement vector of p is the displacement vector between the material point x and other material points x' in its near field, η ji is the material point x i and its near field material point x j The relative displacement between the two points; the displacement vector of each material point can be obtained to obtain the displacement;

[0106] Next, the elastic-plastic linear hardening elastic-plastic model equation is solved and the displacement increment δu is solved by the Newton-Raphson iteration method. p , then:

[0107]

[0108] Where dL is the residual, is the tangent stiffness matrix / Jacobian matrix;

[0109] Then, the tangent stiffness matrix of each material point is assembled into the overall stiffness matrix [K] by using the “matching” method. 2N×2N , and then the incremental equation of motion is written in matrix form as follows:

[0110] [K] 2N×2N [dU] 2N×1 =-[L * +b * ] 2N×1 (9)

[0111] Where N is the total number of material points, the vector matrix δU contains all unknown displacement increments, and K is the overall stiffness matrix; the vector L * +b * A known unbalanced force density is specified; to apply the force boundary conditions, the external forces are applied via the body force density at several layer points and added to the right-hand side vector; in addition, the incremental displacement condition is expressed as:

[0112] [G][dU]+[dU * ]=0

[0113] In the formula, the known matrix G contains the coefficients of the constraint equations related to the unknown vector δU. The vector δU * contains the imposed displacement increment; then the constraint equations are imposed by the Lagrange multiplier method. Finally, the algebraic equations for δU and λ are solved.

[0114]

[0115] In step 4, the embodiment introduces a scalar state function μ to characterize whether the bond is broken or not:

[0116]

[0117] Where s0 is the critical elongation of the bond;

[0118] The local damage of a material point includes the weighted ratio of the number of broken bonds within the near field of the material point to the total number of initial bonds, expressed as:

[0119]

[0120] In the formula, the damage value changes from 0 to 1. When , the material point is completely damaged and does not interact with any material point, and the damage value , indicating that all interactions are well preserved.

[0121] Example

[0122] Take the thermomechanical coupling analysis simulation of the heating process of the plane strain model of the RPV cylinder section (16MnD5 steel material) with multiple radial cracks under the start-up condition based on the technical solution of the present invention as an example (e.g. Figure 2 As shown), the accuracy and effectiveness of the high-throughput peridynamic numerical simulation method proposed in the present invention are further described in detail in combination with the technical scheme and the accompanying drawings.

[0123] Step 1: Discretize the plane strain model of the RPV cylinder segment (e.g. Figure 2 As shown in the figure), define the interaction radius, construct the near field of each material point, and define the physical and mechanical properties parameters of the material.

[0124] Step 2: Determine the initial temperature field, stress field, boundary conditions, total calculation time t = 50,000 s, and problem-solving time step Δt = 2.5 s according to the actual working conditions of the equipment.

[0125] Step 3: According to the distribution of the temperature field in the model of the previous time step, calculate the temperature change ΔT in the model of the current time step, and use the explicit difference method to calculate the distribution of the temperature field in the model of the current time step.

[0126] Step 4: According to the temperature field of the current time step, the stress field of the previous time step, and the boundary conditions, the thermoelastic-plastic constitutive relationship of the RPV metal material 16MnD5 steel is introduced to calculate the internal force of the RPV at the current moment, and the displacement is calculated by the implicit static method. After the convergence criterion is met, the displacement of the current time step and the geometric configuration of the RPV are obtained; return to step 3 to calculate the temperature field of the next time step. When the calculation time reaches 50,000 seconds, return to step 1. Enter step 5 after reaching the specified number of samples.

[0127] Step 5: Based on the simulation data, train the deep learning model and establish a rapid prediction model for equipment damage and fracture behavior; analyze the real-time monitoring data of the equipment and use the trained machine learning model to realize offline prediction of the equipment status.

[0128] Experimental example: Analysis of damage and fracture behavior of a pressure vessel with multiple radial cracks under startup conditions

[0129] In step 1, the plane strain geometry of the RPV is as follows: Figure 2 As shown, the lengths of crack 1 and crack 2 and the relative positions of the two cracks are randomly generated. The model is discretized uniformly in the radial direction, Δx = 0.00625m, and the interaction radius δ = 3.015Δx. Material parameters of 16MnD5 steel: elastic modulus 191GPa, Poisson's ratio v = 0.25, density ρ = 7769kg / m 3 , yield stress σ y =377MPa, thermal conductivity k T=39.246 J / (s·m·℃), specific heat capacity c v =565.32 J / (kg·℃), thermal expansion coefficient α =1.34×10 -5 .

[0130] In this embodiment, the temperature boundary condition of the pressure vessel under the startup condition changes as follows: Figure 3 As shown in (a) in FIG. 1 , the pressure boundary conditions of the pressure vessel under the start-up condition in this embodiment are as follows: Figure 3 As shown in (b), after obtaining the calculated RPV geometric model with multiple cracks and load information, the damage and fracture behavior of the RPV is analyzed by cycling steps 3 to 4, and finally the RPV preset crack J integral, maximum crack opening displacement, RPV average radial displacement, and RPV inner wall circumference change rate under given temperature input conditions and pressure input conditions are obtained.

[0131] In this example, the RPV barrel section with multiple cracks is damaged as follows Figure 4 As shown in (a), the radial displacement of this embodiment is as follows Figure 4 As shown in (b) of FIG. 1 , the circumferential displacement distribution cloud diagram of this embodiment is as follows: Figure 4 As shown in (c) in .

[0132] This embodiment repeats steps 1 to 4 to obtain massive data and establish a data-driven model based on a neural network. The massive data obtained includes input data and output data, the input data includes the length of the first crack, the length of the second crack, the angle between the two cracks, the inner wall temperature and the inner wall pressure five-dimensional vector; the output data includes the J integral of the first crack, the J integral of the second crack, the maximum opening displacement of the first crack, the maximum opening displacement of the second crack, the average radial displacement of the two-dimensional ring, the average radial strain of the two-dimensional ring, the average crack tip damage of the first crack, the average crack tip damage of the second crack and the change rate of the inner wall circumference.

[0133] For each output data, a separate fully connected neural network (FCNN) is constructed and trained uniformly, such as Figure 5 The trained FCNN model is used to predict the test set of this example and compared with the peridynamic results to demonstrate the accuracy and practicality of the FCNN model. In the current working condition, the average radial strain of the two-dimensional ring, the average crack tip damage of the first crack, and the average crack tip damage of the second crack have almost no change, and the mean square error between the true value and the predicted value is 2.069×10 -22 ,3.101×10 -22 , 2.991×10 -8 It can be seen that for these three outputs, the prediction effect of FCNN is very good; for the other 6 outputs, the comparison between the FCNN prediction value and the true value is as follows Figure 6As shown in the figure, the overall goodness of fit is above 0.99.

[0134] in, Figure 6 (a) is a comparison chart of the predicted value and true value of the J integral of the first crack. Figure 6 (b) is a comparison chart of the predicted value and true value of the J integral of the second crack. Figure 6 (c) is a comparison chart of the predicted value and the actual value of the maximum opening displacement of the first crack. Figure 6 (d) is a comparison chart of the predicted value and the actual value of the maximum opening displacement of the second crack. Figure 6 (e) is a comparison chart of the predicted value and the true value of the average radial displacement of the two-dimensional ring. Figure 6 (f) in the figure is a comparison chart between the predicted value and the actual value of the inner wall circumference change rate.

[0135] It only takes 4 seconds to use the trained model to predict the 1401 test data, while it takes about 7 hours to calculate the 1401 data in the peridynamics. FCNN greatly saves computational efficiency.

Claims

1. A high-throughput peridynamic numerical simulation method for damage and fracture analysis of in-service equipment, characterized in that: The following steps are involved: Step 1: Discretize the geometric model of the in-service equipment, define the interaction radius, and construct the near field of each material point; Step 2: Determine the initial temperature field, stress field, boundary conditions, total calculation time t and problem solving time step Δt according to the actual working conditions of the equipment; Step 3: Calculate the temperature change ΔT in the model at the current time step based on the distribution of the temperature field in the model at the previous time step, and then use the explicit difference method to calculate the distribution of the temperature field in the model at the current time step; Step 4: Based on the obtained temperature field of the current time step, the stress field of the previous time step and the boundary conditions, the thermo-elastic-plastic constitutive relationship of the pressure vessel metal material is introduced to calculate the internal force of the pressure vessel RPV at the current moment, and then the implicit static method is used to calculate the displacement. After meeting the convergence criterion, the displacement of the current time step and the geometric configuration of the in-service equipment are obtained, and then the simulation data is obtained; Here, the internal force of the pressure vessel RPV at the current moment is obtained by calculating the sum of the bond force density of each material point; If the current calculation time does not reach the total calculation time t, return to step 3 to calculate the temperature field of the next time step; if the current calculation time reaches the total calculation time t, return to step 1; after reaching the specified number of samples, execute step 5; Step 5: Based on the simulation data obtained in step 4, a fully connected neural network is trained to establish a rapid prediction model for equipment damage and fracture behavior; Then, the real-time monitoring data of the in-service equipment is analyzed to obtain the corresponding input data, and the input data is transmitted to the trained fast prediction model to realize offline prediction of the equipment status.

2. The high-throughput peridynamic numerical simulation method for damage and fracture analysis of in-service equipment according to claim 1 is characterized in that: In step 3, an enhanced peridynamic differential operator (E-PDDO) is used to solve the temperature field; The local heat conduction equation of peridynamics is expressed as follows: In the above formula, is an enhanced peridynamic differential operator; for material points x and x'∈Ω, the position vectors of the two material points are x and x' respectively, and the relative position between the two material points is ξ=x'-x, H x is the interaction domain of material point x; In M-dimensional space, the N-order Taylor series expansion of the function f(x') = f(x+ξ) is: Among them, n1, n2, ···, n M For variable x k The differential order, n i =0,…,N; R(N,x) is the residual term; k=1,…,M; N is the total number of material points; The enhanced peridynamic function is constructed by introducing the fundamental solution of the heat conduction equation and the orthogonal basis: Among them, p i Represents the variable x k The differential order of p i ,q i =0,…,N; is the weight function of the Hermite polynomial For x k The qth component i Hermitian polynomials.

3. The high-throughput peridynamic numerical simulation method for damage and fracture analysis of in-service equipment according to claim 1 or 2, characterized in that: The step 3 uses the explicit difference method to calculate the distribution of the temperature field in the current time step model, that is, the step-by-step integration method is used to complete the calculation of the heat conduction process. When the explicit forward difference format is used, the temperature from the nth step to the n+1th step is: Txt n+1 ) is x at t n+1 The temperature at the moment, ΔT refers to the temperature change, which is The temperature change in a short period of time is calculated, that is, the temperature change ΔT is obtained by multiplying the time by the temperature change rate.

4. The high-throughput peridynamic numerical simulation method for damage and fracture analysis of in-service equipment according to claim 1, characterized in that: The detailed process in step 4 is: First, solve the peridynamic equation of motion as follows: Where ρ is the material density, is the acceleration, u and u′ are the i and the material point x in its near field j displacement, b is the external force density, dV x′ is the material point x j The volume element; H x is the material point x i Near field range; Based on the elastic-plastic linear hardening elastic-plastic model, the material point x in the PD thermo-elastic-plastic model is calculated. i and material point x j The bond force density f ji , the formula is as follows: In the formula, c ji is a material point x j For material point x i The microelastic modulus constant, Represents the material point x i and its near field material point x j The critical elongation of the bond formed between the two electrodes when it enters the yield stage; s ji Represents the material point x i and its near field material point x j When the inertia effect is ignored and the temperature effect is considered, the equilibrium equation is as follows: In the above formula, is the material point x i and other material points x in its near field j The displacement vector of p is the displacement vector between the material point x and other material points x' in its near field, η ji is the material point x i and its near field material point x j The relative displacement between Next, the elastic-plastic linear hardening elastic-plastic model equation is solved and the displacement increment δu is solved by the Newton-Raphson iteration method. p , then: Where dL is the residual, is the tangent stiffness matrix; Then, the tangent stiffness matrices of individual material points are assembled into the overall stiffness matrix [K] using the matching method. 2N×2N , and then the incremental equation of motion is written in matrix form as follows: [K] 2N×2N [δU] 2N×1 =-[L * +b * ] 2N×1 (9) Where N is the total number of material points, the vector matrix δU contains all unknown displacement increments, and K is the overall stiffness matrix; the vector L * +b * Specify known unbalanced force densities; The constraint equations are imposed by the Lagrange multiplier method. Finally, the algebraic equations for δU and λ are solved.

5. The high-throughput peridynamic numerical simulation method for damage and fracture analysis of in-service equipment according to claim 1 or 4, characterized in that: In step 4, a scalar state function μ is introduced to characterize whether the bond is broken or not: Where s0 is the critical elongation of the bond; The local damage of a material point includes the weighted ratio of the number of broken bonds within the near field of the material point to the total number of initial bonds, expressed as: In the formula, the damage value changes from 0 to 1. When , the material point is completely damaged and does not interact with any material point, and the damage value , indicating that all interactions are well preserved.

Images