Methods, devices, storage media, and computer equipment for predicting heat transfer tube wear

Abstract

This invention discloses a method, apparatus, storage medium, and computer equipment for predicting heat transfer tube wear. The method includes: acquiring heat transfer tube attribute information, support attribute information of the supporting component, flow field attribute information of the internal and external flow fields, constraint boundary information, and working environment information of the heat transfer tube to be predicted; constructing a nonlinear dynamic analysis nominal model of the target heat transfer tube structure based on the structural attribute information, flow field attribute information, constraint boundary information, and working environment information; determining the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component, and determining the turbulent excitation force time history of the heat transfer tube to be predicted; applying the nonlinear contact state parameters and turbulent excitation force time history to the nonlinear dynamic analysis nominal model to perform numerical simulation of dynamic analysis, and determining the interaction information between the heat transfer tube to be predicted and the supporting component based on the simulation results; and determining the degree of wear of the heat transfer tube to be predicted based on the interaction information and the wear coefficient.

Description

Technical Field

[0001] This invention relates to the field of structural mechanics analysis of nuclear power plants, and in particular to a method, apparatus, storage medium, and computer equipment for predicting the wear of heat transfer tubes. Background Technology

[0002] Heat exchangers play a crucial role in petroleum, nuclear energy, and chemical industrial production. The tube bundle structure is the core component of large heat exchange equipment, and its structure, process, and operating conditions are exceptionally complex, making it highly susceptible to flow-induced vibration (flow-induced vibration). This can lead to fatigue, collisions, wear, and leaks in the tube bundle structure. Severe flow-induced vibration can cause heat exchange tube damage and media leakage, resulting in serious safety hazards and economic losses. Since the flow field within a heat exchanger must be circulating, the wear of the tube bundle structure induced by flow-induced vibration is long-term and unavoidable. To control the impact of fretting wear, structural design can be optimized in the early stages to keep the flow-induced vibration response within acceptable limits, or dangerous tubes can be blocked during refueling and maintenance of the reactor coolant system. The implementation of both measures relies on the accurate prediction of fretting wear in the heat transfer tubes.

[0003] Currently, linear methods are typically used to analyze a single factor to predict the wear of heat transfer tubes. However, this approach only considers a single linear influencing factor that affects the wear of heat transfer tubes, resulting in an incomplete analysis of the influencing factors and thus low accuracy in predicting heat transfer tube wear. Summary of the Invention

[0004] This invention provides a method, apparatus, storage medium, and computer equipment for predicting the wear of heat transfer tubes, which mainly improves the accuracy of heat transfer tube wear prediction.

[0005] According to a first aspect of the present invention, a method for predicting wear of a heat transfer tube is provided, comprising:

[0006] The structural attribute information, constraint boundary information, and working environment information of the target heat transfer pipe structure are obtained, as well as the flow field attribute information of the flow field inside and outside the heat transfer pipe to be predicted in the target heat transfer pipe structure. The structural attribute information includes the heat transfer pipe attribute information of the heat transfer pipe to be predicted in the target heat transfer pipe structure and the support attribute information of the support components.

[0007] Based on the structural attribute information, flow field attribute information, constraint boundary information, and working environment information, a nonlinear dynamic analysis nominal model of the target heat transfer pipe structure is constructed.

[0008] The nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component are determined, and the time history of the turbulent excitation force generated by the heat transfer tube to be predicted under the flow-induced vibration of the internal and external flow fields is determined based on the flow field attribute information, heat transfer tube attribute information and support attribute information.

[0009] The nonlinear contact state parameters and the turbulent excitation force time history are applied to the nonlinear dynamic analysis nominal model to realize the numerical simulation of the transient dynamic analysis of the heat transfer tube to be predicted, and the interaction information between the heat transfer tube to be predicted and the supporting component is determined based on the simulation results.

[0010] Based on the interaction information and the wear coefficient, the degree of wear of the heat transfer tube to be predicted under flow-induced vibration is determined.

[0011] According to a second aspect of the present invention, a heat transfer tube wear prediction device is provided, comprising:

[0012] The acquisition unit is used to acquire structural attribute information, constraint boundary information, and working environment information of the target heat transfer pipe structure, as well as flow field attribute information of the flow field inside and outside the heat transfer pipe to be predicted in the target heat transfer pipe structure. The structural attribute information includes the heat transfer pipe attribute information of the heat transfer pipe to be predicted in the target heat transfer pipe structure and the support attribute information of the support components.

[0013] The construction unit is used to construct a nonlinear dynamic analysis nominal model of the target heat transfer pipe structure based on the structural attribute information, flow field attribute information, constraint boundary information, and working environment information.

[0014] The first determining unit is used to determine the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component, and to determine the time history of the turbulent excitation force generated by the heat transfer tube to be predicted under the flow-induced vibration of the internal and external flow fields based on the flow field attribute information, heat transfer tube attribute information and supporting attribute information.

[0015] The simulation unit is used to apply the nonlinear contact state parameters and the turbulent excitation force time history to the nonlinear dynamic analysis nominal model to realize the numerical simulation of the transient dynamic analysis of the heat transfer tube to be predicted, and to determine the interaction information between the heat transfer tube to be predicted and the supporting component based on the simulation results.

[0016] The second determining unit is used to determine the degree of wear of the heat transfer tube to be predicted under flow-induced vibration based on the interaction information and the wear coefficient.

[0017] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the above-mentioned heat pipe wear prediction method.

[0018] According to a fourth aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the aforementioned heat pipe wear prediction method.

[0019] The present invention provides a method, apparatus, storage medium, and computer equipment for predicting heat transfer tube wear. Compared with current methods that use linear methods to analyze a single factor to predict heat transfer tube wear, the present invention constructs a nonlinear dynamic analysis nominal model of the target heat transfer tube structure and simulates all nonlinear contact state parameters, wear coefficients, and turbulent excitation force time histories that affect heat transfer tube wear. Then, the simulated nonlinear contact state parameters, wear coefficients, and turbulent excitation force time histories are applied to the nonlinear dynamic analysis nominal model to achieve numerical simulation of the transient dynamic analysis of the heat transfer tube to be predicted. Based on the simulation results, the interaction information between the heat transfer tube to be predicted and the supporting components is determined. Finally, based on the interaction information and wear coefficient, the wear degree of the heat transfer tube under flow-induced vibration is determined. Thus, by applying all nonlinear influencing factors (nonlinear contact state parameter information), wear coefficient, and linear influencing factors (turbulent excitation force time history) that affect the wear of the heat transfer tube to the nonlinear dynamic analysis model for dynamic simulation, the simulation process can be made closer to the actual working process of the heat transfer tube. That is, in the process of predicting the wear of the heat transfer tube, all linear and nonlinear influencing factors that affect the wear of the heat transfer tube are comprehensively considered, so that the interaction information between the heat transfer tube and the supporting components is more accurate, and thus the wear degree prediction is more accurate. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0021] Figure 1 A flowchart of a heat transfer tube wear prediction method provided by an embodiment of the present invention is shown;

[0022] Figure 2 A flowchart of another heat transfer tube wear prediction method provided by an embodiment of the present invention is shown;

[0023] Figure 3 A schematic diagram of a heat transfer tube structure with a linear support section provided in an embodiment of the present invention is shown;

[0024] Figure 4 The diagram illustrates a sample of a random variable and a verification diagram of the sampling effect provided in an embodiment of the present invention.

[0025] Figure 5 This invention provides a time history diagram of the flow field excitation force acting on the resistance direction of a certain unit in a dynamic analysis model of a heat transfer tube.

[0026] Figure 6 This diagram illustrates the motion trajectory of a heat transfer tube within a nonlinear support section, according to an embodiment of the present invention.

[0027] Figure 7 The diagram illustrates the probability distribution, 95% confidence interval, and failure probability of the fretting wear depth of a heat transfer tube output by a preset wear prediction model provided in an embodiment of the present invention.

[0028] Figure 8 This diagram illustrates the structure of a heat transfer tube wear prediction device according to an embodiment of the present invention.

[0029] Figure 9 A schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention is shown. Detailed Implementation

[0030] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the present application can be combined with each other.

[0031] Currently, the method of using linear analysis to predict the wear of heat transfer tubes only considers a single linear influencing factor that affects the wear of heat transfer tubes. This method does not provide a comprehensive analysis of the influencing factors, resulting in low accuracy in predicting the wear of heat transfer tubes.

[0032] To address the above problems, embodiments of the present invention provide a method for predicting heat transfer tube wear, such as... Figure 1 As shown, the method includes:

[0033] 101. Obtain structural attribute information, constraint boundary information, and working environment information of the target heat transfer pipe structure, as well as flow field attribute information of the flow field inside and outside the heat transfer pipe to be predicted in the target heat transfer pipe structure. The structural attribute information includes the heat transfer pipe attribute information of the heat transfer pipe to be predicted in the target heat transfer pipe structure and the support attribute information of the support components.

[0034] The heat transfer tube attribute information includes: the geometric dimensions and material properties of the heat transfer tube to be predicted (such as material density, elastic modulus, Poisson's ratio, etc.), and the arrangement information of the heat transfer tube to be predicted and adjacent heat transfer tubes (such as arrangement method, pitch, etc.); the support attribute information includes the geometric dimensions, material, support type (such as circular hole support, quincunx hole support, flat plate support, etc.), and support state parameters (such as support stiffness, support gap, friction coefficient, initial eccentricity, preload, etc.); the constraint boundary information refers to the mechanical constraint conditions of the heat transfer tube outside the nonlinear support components, such as fixed constraints, simply supported constraints, elastic constraints, sliding constraints, etc.; the flow field attribute information includes: the flow field density, velocity, temperature, and pressure inside and outside the heat transfer tube to be predicted; and the working environment information refers to the temperature, pressure, and medium corresponding to the actual working scenario of the heat transfer tube.

[0035] Specifically, when a flow field (such as water or steam) flows through a heat transfer tube bundle, it induces vibration (flow-induced vibration) in the tube bundle. This vibration causes frequent contact between the heat transfer tubes and supporting components, leading to wear and leakage in the tube bundle structure. This invention primarily applies to the prediction of flow-induced vibration wear of heat transfer tubes. In this embodiment, structural attribute information and constraint boundary information of the target heat transfer tube structure, as well as flow field attributes and operating environment information of the internal and external flow fields of the heat transfer tube to be predicted, can be obtained through design drawings, databases, or measuring instruments. Then, a nonlinear dynamic analysis nominal model of the target heat transfer tube structure is constructed based on this information. Subsequently, using the nonlinear dynamic analysis nominal model, a probabilistic analysis method is employed to simulate the wear of the heat transfer tube in actual operating scenarios, ultimately obtaining statistical and reliability information on heat transfer tube wear. This prediction method can comprehensively consider the vibration response of heat transfer tubes under various influencing factors with random characteristics, thereby more accurately simulating the nonlinear vibration and material wear loss of heat transfer tubes, providing a reliable basis for design and maintenance. At the design level, this prediction method is an improvement and perfection of the traditional deterministic engineering design method. At the operation and maintenance level, it can more accurately predict the degree and trend of wear of heat transfer tubes before serious wear actually occurs, thereby formulating maintenance and tube plugging plans in advance and avoiding equipment downtime and safety accidents caused by sudden failure of heat transfer tubes.

[0036] 102. Based on structural attribute information, flow field attribute information, constraint boundary information, and working environment information, construct a nominal model for nonlinear dynamic analysis of the target heat transfer pipe structure.

[0037] Specifically, based on the structural properties, constraint boundary information, operating environment information, and flow field properties of the target heat transfer pipe structure, a nominal model for nonlinear dynamic analysis of the target heat transfer pipe structure is established using the finite element method (FEM). For example, the target heat transfer pipe structure can be discretized into multiple elements or nodes using the FEM method, considering the mass, stiffness, damping, and other properties of the heat transfer pipe structure, to obtain a nominal model for nonlinear dynamic analysis of the target heat transfer pipe structure (the input parameters of the nominal model are all ideal design values). The constructed nominal model for nonlinear dynamic analysis should accurately reflect the ideal contact form and characteristics between the target heat transfer pipe structure and supporting components in the flow field. The finite element elements of the nominal model for nonlinear dynamic analysis can be set according to actual needs (e.g., the length of the finite element elements can be set according to the elastic modulus, moment of inertia of the pipe material, linear mass of the pipe, and natural frequency of the pipe). Therefore, by constructing a nominal model for nonlinear dynamic analysis of the target heat transfer pipe structure, the influence of nonlinear factors of the heat transfer pipe under ideal operating scenarios can be comprehensively considered, thereby improving the accuracy of the heat transfer pipe wear model.

[0038] 103. Determine the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting components, and based on the flow field attribute information, heat transfer tube attribute information, and support attribute information, determine the time history of the turbulent excitation force generated by the heat transfer tube to be predicted under the flow-induced vibration of the internal and external flow fields.

[0039] The nonlinear contact state parameters include: the support stiffness, support gap, friction coefficient, initial eccentricity, and preload between the heat transfer tube to be predicted and the support component; the wear coefficient measures the wear resistance between the heat transfer tube to be predicted and the support component, and is the ratio of wear volume fraction to wear power; the turbulent excitation force time history describes the time-varying excitation force generated by the heat transfer tube to be predicted under the action of turbulent fluid.

[0040] In this embodiment of the invention, random variables are first determined, including but not limited to the support stiffness, support gap, friction coefficient, initial eccentricity, and wear coefficient between the heat transfer tube and the supporting components to be predicted. Then, considering the design, manufacturing, installation, and various errors of the target heat transfer tube structure, as well as the influence of flow field properties, constraint boundary information, and working environment information, the probability distribution of the random variables is determined, including the probability density function and distribution interval. Subsequently, based on the probability density function and distribution interval, all predetermined random variables (including nonlinear contact state parameters and wear coefficient) are jointly sampled to obtain a sample set of support stiffness, support gap, friction coefficient, initial eccentricity, and wear coefficient. This method of generating a sample set of random variables in the heat transfer tube wear prediction analysis through probability distribution ensures that the generated data samples conform to the actual distribution law of the input variables, thereby improving the realism of the heat transfer tube wear simulation and ensuring more accurate prediction results. Furthermore, based on the turbulent excitation force model of the heat transfer tube in the lift and drag directions, the spectral-to-time-history method is used to obtain the time history of the turbulent excitation force acting on each element of the nominal model of the nonlinear dynamic analysis. In actual operation, especially under conditions of high fluid velocity or complex fluid properties (such as multiphase flow), heat transfer tubes are subjected to turbulent excitation forces. These excitation forces lead to interactions between the heat transfer tube and its support, resulting in wear. Therefore, by introducing the time history of turbulent excitation forces, the dynamic stress conditions of the heat transfer tube in the flow field are simulated, completing a necessary step in predicting heat transfer tube wear.

[0041] 104. Apply nonlinear contact state parameters and turbulent excitation force time history to the nonlinear dynamic analysis nominal model to realize numerical simulation of transient dynamic analysis of the heat transfer tube to be predicted, and determine the interaction information between the heat transfer tube to be predicted and the supporting components based on the simulation results.

[0042] The interaction information refers to the dynamic response between the heat transfer tube and the supporting component to be predicted, including information such as the normal contact load and relative slip distance between the heat transfer tube and the supporting component to be predicted.

[0043] In this embodiment of the invention, nonlinear contact state parameters (sample values) are used to replace the nominal values ​​in the nonlinear dynamic analysis nominal model, and the turbulent excitation force time history is applied to the nonlinear dynamic analysis nominal model after the nominal values ​​are replaced, so as to realize the numerical simulation of the transient dynamic response of the heat transfer tube to be predicted, and extract the interaction information between the heat transfer tube to be predicted and the supporting components during the simulation process.

[0044] Specifically, the time history of turbulent excitation force is applied to the nominal model of nonlinear dynamic analysis, and the nominal values ​​in the nominal model are replaced with sample values ​​of random variables (nonlinear contact state parameters). The vibration differential equation is solved using the direct integration method or the nonlinear modal superposition method to achieve a more realistic simulation of the flow-induced vibration response of the heat transfer tube to be predicted. By using the extracted normal contact load and relative slip distance information and the sample values ​​of the wear coefficient, the wear degree of the heat transfer tube to be predicted under flow-induced vibration can be finally determined. Thus, by establishing a nonlinear finite element model (achieving accurate simulation of nonlinear contact state), which has a significant impact on the accuracy of heat transfer tube wear prediction, and by reasonably simulating the random variables (including the nonlinear contact state parameters and wear coefficient between the heat transfer tube and the supporting components) and turbulent excitation force in the heat transfer tube wear prediction, the simulation process is made closer to the actual operation of the heat transfer tube, thereby making the predicted interaction information between the heat transfer tube and the supporting components more accurate, and thus making the prediction of the wear degree of the heat transfer tube under flow-induced vibration more accurate.

[0045] 105. Based on interaction information and wear coefficient, determine the wear degree of the heat transfer tube to be predicted under flow-induced vibration.

[0046] In this embodiment of the invention, the operation of the heat transfer tube is simulated using a nominal model of nonlinear dynamic analysis to obtain the interaction information between the heat transfer tube and the supporting components (time history of normal contact load and time history of relative slip distance under each sample). Then, based on the interaction information and the wear coefficient (sample value) (which has been jointly sampled and obtained together with the nonlinear contact state parameters in step 103), the wear degree of the heat transfer tube to be predicted under flow-induced vibration is determined. Finally, the wear degree (wear volume or wear depth) results of the heat transfer tube under all samples are statistically analyzed to obtain statistical information such as probability density function, 95% confidence interval and failure probability. Based on this, step 105 specifically includes: determining the work done by the support component and the wear area of ​​the heat transfer tube to be predicted during the steady-state wear stage, based on the normal contact load and relative sliding distance; determining the wear power of the heat transfer tube to be predicted based on the work done by the wear area during the steady-state wear stage; determining the wear volume of the heat transfer tube to be predicted under flow-induced vibration based on the wear power, the preset operating time of the heat transfer tube to be predicted, and the wear coefficient; determining the wear depth of the heat transfer tube to be predicted under flow-induced vibration based on the wear volume; and determining the wear degree of the heat transfer tube to be predicted under flow-induced vibration based on the wear depth. Further, statistical analysis is performed on the wear degree of all heat transfer tubes under flow-induced vibration to obtain statistical information on the wear degree (wear volume or wear depth) of the heat transfer tubes, including the probability distribution of the wear degree, the 95% confidence interval, and the failure probability. Finally, the accurate wear degree is determined based on the statistical information.

[0047] Specifically, the work performed by the support components on the wear area of ​​the heat transfer tube to be predicted is calculated using the following formula:

[0048] W(t)=F(t)l(t)

[0049] Where t represents time, W(t) is the work done by the supporting component on the wear area of ​​the heat transfer tube to be predicted, F(t) is the normal contact load of the wear area, and l(t) is the relative sliding distance between the heat transfer tube to be predicted and the supporting component. Further, the wear power g is calculated according to the following formula:

[0050]

[0051] Furthermore, the wear volume V(t) of the heat transfer tube under flow-induced vibration is calculated according to the following formula:

[0052] V(t) = k v gt

[0053] Where, k v The wear coefficient is then used. Based on the actual support form of the heat transfer tube, the conversion relationship between the wear volume and wear depth is determined. Typical support forms include: flat plate support, proximity tube support, trilobal perforation support, and circular hole support. Alternatively, the conversion relationship between wear volume and wear depth can be determined based on wear track profiles obtained from numerous experiments. Further, this conversion form is used to transform the wear volume into the predicted wear depth of the heat transfer tube under flow-induced vibration. Further, statistical analysis is conducted based on wear depth samples to obtain statistical information such as the probability density function, 95% confidence interval, and failure probability of the wear depth. In the process of predicting the wear of the heat transfer tube, this embodiment of the invention comprehensively considers the nonlinear influencing factors and random variables that affect the wear of the heat transfer tube, thereby making the predicted wear degree closer to the actual situation.

[0054] In brief, this invention patent has three key features:

[0055] (1) Establish a nominal model for nonlinear dynamic analysis to more accurately simulate the interaction between the heat transfer tube and the support.

[0056] (2) Some input parameters with large uncertainties in the model parameters are determined as random variables and analyzed by probability analysis, which more realistically reflects the actual operation of the heat transfer tube and the obtained heat transfer tube wear prediction results (statistical distribution) are more representative.

[0057] (3) When determining the structural integrity of heat transfer tubes, the deterministic method of comparing the nominal model calculation results with the design allowable values ​​is generally adopted (all model parameters are taken under the nominal design values, and the model parameters are considered to be deterministic). However, this method can not only make a comprehensive judgment on structural integrity from the deterministic level (given a set of deterministic parameters), but also from the probabilistic level (reliability).

[0058] According to the present invention, a method for predicting heat transfer tube wear differs from current methods that use linear methods to analyze a single factor to predict heat transfer tube wear. This invention constructs a nonlinear dynamic analysis nominal model of the target heat transfer tube structure and simulates samples of all nonlinear contact state parameters, wear coefficients, and turbulent excitation force time histories that affect heat transfer tube wear. The simulated nonlinear contact state parameter samples and turbulent excitation force time histories are then applied to the nonlinear dynamic analysis nominal model to achieve numerical simulation of the transient dynamic analysis of the heat transfer tube to be predicted. Based on the simulation results, the interaction information between the heat transfer tube to be predicted and the supporting components is determined. Finally, based on the interaction information and the wear coefficient... By using samples to determine the wear degree of the heat transfer tube under flow-induced vibration, and by applying all nonlinear influencing factors (nonlinear contact state parameters), wear coefficients (linear influencing factors), and turbulent excitation force time histories that affect the wear of the heat transfer tube to the nominal model of nonlinear dynamic analysis for dynamic simulation, the simulation process can be made closer to the actual working process of the heat transfer tube. That is, in the process of predicting the wear of the heat transfer tube, all linear and nonlinear influencing factors that affect the wear of the heat transfer tube, as well as the uncertainties of nonlinear and linear influencing factors, are comprehensively considered. This makes the interaction information between the heat transfer tube and the supporting components more accurate, and thus makes the prediction of the wear degree more accurate.

[0059] Furthermore, to better illustrate the process of predicting heat transfer tube wear described above, as a refinement and extension of the above embodiments, this invention provides another method for predicting heat transfer tube wear, such as... Figure 2 As shown, the method includes:

[0060] 201. Obtain structural attribute information, constraint boundary information, and working environment information of the target heat transfer pipe structure, as well as flow field attribute information of the flow field inside and outside the heat transfer pipe to be predicted in the target heat transfer pipe structure. The structural attribute information includes the heat transfer pipe attribute information of the heat transfer pipe to be predicted in the target heat transfer pipe structure and the support attribute information of the support components.

[0061] The target heat transfer tube structure includes the heat transfer tube to be predicted and supporting components. When fluid flows inside and outside the heat transfer tube, it can cause vibration. This vibration leads to collisions and friction at the contact surfaces between the heat transfer tube and the supporting components, resulting in wear on the heat transfer tube. Specifically, structural attribute information, constraint information, operating environment information, and flow field attribute information of the target heat transfer tube structure are obtained through design documents (design drawings, design specifications, and design drawings, etc.), measuring instruments, general databases, or experimental data.

[0062] 202. Based on structural attribute information, flow field attribute information, constraint boundary information, and working environment information, a nominal model for nonlinear dynamic analysis of the target heat transfer pipe structure is constructed.

[0063] Specifically, the finite element method, finite volume method, and finite difference method can be used to construct a nominal model for nonlinear dynamic analysis of the target heat transfer pipe structure. When constructing the nominal model for nonlinear dynamic analysis, the heat transfer pipe structure can be discretized into a series of elements or nodes, and the dynamic behavior of the entire structure can be approximately described by solving the physical quantities on these elements or nodes.

[0064] 203. Determine the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting components, and based on the flow field attribute information, heat transfer tube attribute information, and support attribute information, determine the time history of the turbulent excitation force generated by the heat transfer tube to be predicted under the flow-induced vibration of the internal and external flow fields.

[0065] In this embodiment of the invention, due to factors such as manufacturing deviations, on-site assembly, and calculation errors, the prediction of nonlinear contact state parameters such as the gap, eccentricity, preload, and normal contact stiffness between the heat transfer tube and the supporting component, as well as linear parameters such as the wear coefficient between the heat transfer tube and the supporting component, has a certain degree of uncertainty. These parameters are random variables and will deviate from the ideal design dimensions. Therefore, the data for nonlinear contact state parameters and wear coefficients cannot be determined based on nominal design values. Statistical simulation of the aforementioned nonlinear and linear contact state parameters is required. Based on this, step 203 specifically includes: determining the probability distribution of the nonlinear contact state parameters to be obtained based on the actual design requirements, manufacturing requirements, installation requirements, and calculation error information of the target heat transfer tube structure. The probability distribution of the nonlinear contact state parameters includes: a state parameter probability density function, an upper limit of the state parameter distribution, and a lower limit of the state parameter distribution. Based on the actual design requirements, manufacturing requirements, installation requirements, and calculation error information of the target heat transfer tube structure, determining the probability distribution of the wear coefficient to be obtained. The probability distribution of the wear coefficient to be obtained includes: a wear coefficient probability density function. The method for determining the nonlinear contact state parameters and wear coefficient between the heat transfer tube and the support component based on the probability distribution of the nonlinear contact state parameters and the wear coefficient to be obtained includes: jointly sampling the nonlinear contact state parameters and wear coefficient using a preset state sampling algorithm based on the probability distribution of the nonlinear contact state parameters and the wear coefficient to be obtained, to obtain initial nonlinear contact state parameters and initial wear coefficient; verifying the accuracy of the probability distribution using a preset probability distribution; if the probability distribution passes the accuracy verification, then the initial nonlinear contact state parameters and the initial wear coefficient are determined as the nonlinear contact state parameters and wear coefficient between the heat transfer tube and the support component to be obtained; otherwise, resampling the nonlinear contact state parameters and wear coefficient using a preset sampling algorithm until the probability distribution of the sampled nonlinear contact state parameters and wear coefficient passes the accuracy verification.

[0066] The nonlinear contact state parameters include: the design gap, eccentricity, preload, and normal contact stiffness between the heat transfer tube and the supporting components; the wear coefficient includes the wear coefficient between the heat transfer tube and the supporting components. Actual design requirements include the heat transfer performance requirements, structural requirements, material requirements, and geometric dimension requirements of the heat transfer tube; manufacturing requirements refer to the manufacturing process of the heat transfer tube; installation requirements refer to the installation safety information and reasonable layout information of the heat transfer tube; error information includes manufacturing errors, installation errors, instrument accuracy errors, human reading errors, and material property errors. The preset probability distribution is set according to actual requirements.

[0067] Specifically, taking into account the influence of design, manufacturing, installation, and errors, the probability distribution (probability density function and upper and lower limits of distribution) of the analytical input variables (nonlinear contact state parameters) is determined. The design gap and eccentricity can be represented by the following state parameter probability density function, i.e., the normal distribution function:

[0068]

[0069] Where σ is the standard deviation of the design gap or eccentricity, μ is the mean of the design gap or eccentricity, and a l To determine the lower limit of the distribution of gaps or eccentricities in the design, a u Let P(x) be the upper limit of the design gap or eccentricity distribution, P(x) be the probability density function of the design gap or eccentricity, and w be the value of the design gap or eccentricity. Therefore, using the above function and the upper and lower limits of the distribution, the probability distribution (state probability distribution) of the design gap or eccentricity can be determined. Meanwhile, the normal contact stiffness can be represented by the following uniform distribution function:

[0070]

[0071] Among them, a l a is the lower limit of the distribution of normal contact stiffness. u Let w be the upper bound of the normal contact stiffness distribution, and P(x) be the value of the normal contact stiffness. Then, based on the above function and the upper and lower bounds of the distribution, the probability distribution of the normal contact stiffness (sample dataset) can be determined.

[0072] Similarly, the wear coefficient can be statistically simulated based on the following wear coefficient probability density function:

[0073]

[0074] Where P(m) is the probability density function of the wear coefficient, a i a is the lower limit of the wear coefficient distribution. mLet be the upper limit of the wear coefficient distribution, and m be the possible values ​​of the wear coefficient. Therefore, based on the wear coefficient probability density function and its upper and lower limits, the probability distribution of the wear coefficient (sample dataset) can be determined.

[0075] Furthermore, based on the probability density functions and upper and lower bounds (i.e., state probability distribution and wear probability distribution) of the gap, eccentricity, preload, wear coefficient, and normal contact stiffness between the heat transfer tube and the supporting components, a pre-defined sampling algorithm, such as the Latin hyperisolated method or the Markov chain Monte Carlo method, is used to jointly sample the random variables in the high-dimensional space. This allows for the acquisition of more spatial information with fewer sample points; for example, the number of sample points can be N. S For example, random sampling of the design gap and normal contact stiffness is performed. The design gap (range 0.1-0.3 mm) + normal contact stiffness (1E5-2E5 N / m) are both uniformly distributed. To extract 50 sample points using the Latin hypercube method, the problem becomes a point-splitting operation in a 2D space (plane, upper and lower limits) using the Latin hypercube method. Each of the 50 sample points is represented as (x1, x2), where x1 falls within 0.1-0.3 mm and x2 falls within the 1E5-2E5 N / m range. Statistical analysis of x1 or x2 from these 50 sample points ensures that the probability density function satisfies a uniform distribution. Thus, through this random sampling method, initial samples of the nonlinear contact state parameters and wear coefficient between the heat transfer tube and the supporting component can be obtained. Each sample corresponds to specific data on the gap, eccentricity, preload, normal contact stiffness, and wear coefficient. Next, the probability distributions corresponding to the initial samples of nonlinear contact state parameters and the initial samples of wear coefficients are determined. These probability distributions are then compared with their corresponding preset probability distributions to verify the accuracy of the sampling. If the sample probability distribution is inaccurate, random sampling is performed again until the sampled data meets the accuracy verification criteria. The preset probability distribution is defined based on experience or measured data, and is typically described using classical probability distribution models.

[0076] Furthermore, in order to accurately predict the wear of the heat transfer tube, it is also necessary to determine the time history of the turbulent excitation force in the flow field where the heat transfer tube to be predicted is located. Based on this, step 203 also includes: determining the dimensionless equivalent power spectral density function of the target heat transfer tube structure; converting the dimensionless equivalent power spectral density function into a dimensional equivalent power spectral density function using a preset conversion method; and converting the dimensional equivalent power spectral density function into the time history of the turbulent excitation force acting on each element of the nominal model of the nonlinear dynamic analysis using a preset power spectral density to time history conversion method based on the flow field attribute information, heat transfer tube attribute information, and support attribute information.

[0077] Specifically, firstly, a turbulent excitation force model based on the heat transfer tube in the lift and drag directions is determined, i.e., the dimensionless equivalent power spectral density function. Then, combining the structural parameters of the heat transfer tube, such as tube diameter and element length, and flow field characteristics, such as velocity distribution and vibration characteristics, such as modal information, the dimensionless equivalent power spectral density function is transformed into a dimensional equivalent power spectral density function according to the following formula:

[0078]

[0079] in, Φ is the dimensionless equivalent power spectral density function. E (f) is the dimensionless equivalent power spectral density function, L0 is the reference value for the length of the heat transfer tube unit, D0 is the reference value for the diameter of the heat transfer tube, a is the modal correlation coefficient, and f R Here, p0 is the reduced frequency, f0 is the hydrodynamic head of the heat transfer tube, D is the diameter of the heat transfer tube in the nonlinear dynamic analysis model, and L is the length of the heat transfer tube element in the nonlinear dynamic analysis model. The reduced frequency f0 is... R The formulas for determining the fluid dynamic head p0 and the frequency scaling parameter f0 of the heat transfer tube are as follows:

[0080] f R =f / f0

[0081]

[0082] f0 = U p / D

[0083] Where f is the excitation frequency of the flow field, ρ is the density of the fluid, and U pLet be the interstitial velocity of the heat transfer tube, and D be the pipe diameter in the nominal model of the nonlinear dynamic analysis of the heat transfer tube. Therefore, using the above formulas, based on the dimensionless equivalent power spectral density functions of the heat transfer tube in the lift and drag directions, and combining the structural parameters (pipe diameter, element length), flow field characteristics (interstitial velocity, fluid density, dynamic head), vibration characteristics (modal information), and support attribute information of the heat transfer tube, the time history of the turbulent excitation force acting on each element of the finite element model is obtained using the power spectral to time history method. Because the gap, eccentricity, preload, wear coefficient, normal contact stiffness, and turbulent excitation force between the heat transfer tube and the supporting components have certain uncertainties, this invention utilizes probability density functions, upper and lower distribution limits, and random sampling to numerically simulate various factors affecting sensor wear, including nonlinear contact state parameters, wear coefficient, and turbulent excitation force time histories. It also uses power spectrum to time history conversion to numerically simulate the randomness of turbulent excitation force. This avoids the problem of selecting incorrect values ​​based on experience, thereby improving the accuracy of determining parameters such as gap, eccentricity, preload, and wear coefficient, and consequently improving the accuracy of predicting the wear degree of the heat transfer tube. Simultaneously, this invention uses a classical sampling strategy within the parameter space to determine various parameters affecting heat transfer tube wear, achieving comprehensive consideration of the influence of important parameters related to fretting wear with less computational cost, avoiding the massive computational resource requirements for fretting wear prediction in high-dimensional input parameter spaces. This invention combines the accuracy of nonlinear finite element time-domain analysis with the advantages of probabilistic simulation based on big data, which can take into account the randomness of parameters, to realize nonlinear vibration analysis of complex heat transfer tubes. This improves the R&D efficiency of analysis and design and provides a more accurate and practical general analysis method for predicting the fretting wear of heat transfer tube equipment.

[0084] 204. Apply the nonlinear contact state parameters and turbulent excitation force time history to the nonlinear dynamic analysis nominal model to realize the numerical simulation of the transient dynamic analysis of the heat transfer tube to be predicted, and determine the interaction information between the heat transfer tube to be predicted and the supporting components based on the simulation results.

[0085] Specifically, based on the established nonlinear dynamic analysis nominal model of the heat transfer tube, the nonlinear contact state parameters (sample values ​​of nonlinear contact state parameters) obtained by random sampling and verified for accuracy, the wear coefficient (sample values ​​of wear coefficient), and the constructed turbulent excitation force time history, numerical simulation of transient dynamic analysis of flow-induced vibration of the heat transfer tube is carried out using finite element analysis software. The interaction between the heat transfer tube and the support is simulated, thereby obtaining the normal contact load and relative slip distance between the heat transfer tube and the support component for each parameter.

[0086] 205. Determine the interaction feature vector corresponding to the interaction information, and determine the wear feature vector corresponding to the wear coefficient.

[0087] Specifically, in order to improve the accuracy and efficiency of heat transfer tube wear prediction, wear prediction models can be used to predict the wear of heat transfer tubes. Based on this, it is first necessary to use word embedding and other methods to determine the interaction feature vector corresponding to the interaction information, and to determine the wear feature vector corresponding to the wear coefficient.

[0088] 206. Perform cross processing on the interaction feature vector and the wear feature vector to obtain the wear cross feature vector.

[0089] In practical applications, the interaction information between the heat transfer pipe and the supporting components, and the wear coefficient, belong to different dimensions of data. They need to be processed into data of the same dimension, i.e., processed into interaction feature vectors and wear feature vectors. Then, to extract more latent features, the above vectors need to be cross-processed. Based on this, step 206 specifically includes: multiplying each element in the interaction feature vector with the corresponding element in the wear feature vector to obtain an initial feature-level cross vector; determining the process convolution transformation function and process vector weights corresponding to the initial feature-level cross vector; performing a convolution transformation on the initial feature-level cross vector using the process convolution transformation function based on the process vector weights to obtain a feature-level cross vector; horizontally concatenating the interaction feature vector and the wear feature vector to obtain a concatenated feature vector; determining the concatenation coefficients corresponding to the concatenated feature vector; performing a linear transformation on the concatenated feature vector based on the concatenation coefficients to obtain a low-order cross vector; and combining the feature-level cross vector and the low-order cross vector to obtain a wear cross feature vector.

[0090] The process convolution transformation function, process vector weights, and concatenation coefficients are set according to actual needs. Specifically, if the interaction feature vector is (a1, a2, a3) and the wear feature vector is (b1, b2, b3), firstly, a feature-level cross is performed on the interaction and wear feature vectors, that is, after performing a Hadamard product on all elements at the same position between the vectors, a convolution transformation is performed under a certain weight w1 and the process convolution transformation function f(), and the resulting feature-level cross vector is f(w1*(a1*b1, a2*b2, a3*b3)). At the same time, the interaction and wear feature vectors are concatenated laterally to obtain a concatenated feature vector, and a linear transformation is performed on the concatenated feature vector using the concatenation coefficient w2, resulting in a low-order cross vector f(w2(a1, a2, a3)). 3,The model uses a set of parameters (b1, b2, b3) and combines the feature-level cross vectors and low-order cross vectors with a preset transformation function to perform a transformation process, resulting in a wear cross feature vector. The preset transformation function can be set according to the actual situation, and this embodiment does not impose any restrictions on it. By performing cross processing on the interactive feature vectors and wear feature vectors, different features can be automatically or explicitly combined to generate new feature combinations. These combined features may contain complex nonlinear relationships between the original features, enabling the model to capture more refined and richer information in the data. This means it can fully utilize the relationships between various data points, extract more latent features, and simultaneously handle both high-order and low-order data, making data utilization more efficient and resulting in more accurate wear predictions that meet the needs of practical application scenarios.

[0091] 207. Input the wear cross feature vector into the preset wear prediction model to predict the wear degree of the heat transfer tube under flow-induced vibration.

[0092] In this embodiment of the invention, to improve the prediction accuracy of the preset wear prediction model, it is first necessary to train and construct the preset wear prediction model. Based on this, the method includes: constructing at least one initial wear prediction model, wherein the model structures of each initial wear prediction model can be the same or different; obtaining a sample dataset, wherein the sample dataset contains sample interaction information between the sample heat transfer pipe and the sample support component, the sample wear coefficient, and the actual wear degree of the sample heat transfer pipe during actual operation; dividing the sample dataset into multiple training datasets and multiple test datasets based on the number of initial wear prediction models; training the corresponding initial wear prediction model using each training dataset and validating the corresponding trained initial wear prediction model using each test dataset to obtain the validation results of each trained initial wear prediction model; and selecting the model with the highest prediction accuracy among the trained initial wear prediction models as the preset wear prediction model. Thus, by training the model on a large-scale dataset, the model can learn rich general features and knowledge representations, thereby laying the foundation for accurate wear prediction of the heat transfer pipe in the future. Furthermore, after constructing the preset wear prediction model, the wear cross feature vector is input into the preset wear prediction model for wear prediction. The preset wear prediction model can output the wear degree of the heat transfer tube. In another embodiment of the present invention, the preset wear prediction model can also output information such as the probability distribution of wear loss of the heat transfer tube, the 95% confidence interval, and the failure probability.

[0093] In another embodiment of the present invention, a preset lifespan of the heat transfer tube can be determined. Then, the lifespan feature vector corresponding to the preset lifespan and the wear cross-feature vector are input into a preset wear prediction model for wear prediction, obtaining the wear degree of the heat transfer tube to be predicted within the preset lifespan. Since the heat transfer tube is a critical component in the steam generator, its wear degree directly affects the safe operation of the equipment. By predicting the wear degree of the heat transfer tube within its lifespan, potential wear problems can be detected in a timely manner, preventing serious accidents such as primary-side coolant leakage caused by heat transfer tube rupture, thereby avoiding radiation leakage and meltdown phenomena, and ensuring system safety. It should be noted that if the preset wear prediction model is to be used to predict the wear degree of the heat transfer tube within its lifespan, the corresponding sample dataset should also include information such as the expected lifespan of the sample heat transfer tubes when training the preset wear prediction model.

[0094] The real-time process of the present invention is explained in detail by the following examples. It should be noted that the following examples are merely illustrative and do not specifically limit the embodiments of the present invention.

[0095] This embodiment selects one of the three-layer helical tube bundles as the research object. Its structural parameters are: a pitch ratio of 1.3 for the triangularly arranged helical tube bundle, an outer diameter of 0.02 m, a wall thickness of 0.0018 m, a helix angle of 4.3°, and a radius of curvature of 0.9 m. The fluid densities inside and outside the tube are 500 kg / m³. 3 and 10000kg / m 3 The fluid velocity inside the pipe is 0.5 m / s, and the fluid velocity outside the pipe is 1 m / s. The nonlinear flow-induced vibration analysis model of the helical pipe was established using the finite element software ANSYS. The model's node information, element meshing, and constraint information (each layer of the helical pipe has 6 support constraints, one every 60°) are shown in the diagram. Figure 3 ( Figure 3 (where offset is the eccentricity). The example study investigates four random input variables, including the nonlinear contact state parameters between the helical tube and the support, and the fretting wear parameters: design gap g; eccentricity r. offset (with eccentricity δ) offset The relationship between the design clearance g and the design clearance g is: Normal contact stiffness FKN; Fretting wear coefficient k frett The design values ​​and probability distributions of the input variables are listed in Table 1. It should be noted that preload refers to the period after the helical tubes are installed but before the reactor is operational, due to the eccentricity r of the heat transfer tubes. offset The compressive load generated between the support and a value greater than 1 is calculated as follows:

[0096] F PRE =(δ offset -g)×FKN when

[0097] F PRE =0 when

[0098] Among them, F PRE This refers to the clamping load between the heat transfer tube and the support. As shown in Table 1, due to the random variable 1(r) offset The parameter range of ) has exceeded [-1 1], and the effect of preload has been implicitly considered in the embodiment.

[0099] Table 1 Nominal design values ​​and probability distributions of input variables

[0100]

[0101]

[0102] The samples in this embodiment were generated using the LHS (Latin Hypercube Sampling) method, with a sample size of 180. The sample size for random variable 1 and the verification of the sampling effect are listed below. Figure 4 ( Figure 4 In this context, ParameterRange represents the parameter range, Occurence represents the event, Sampling Histogram represents the sampling histogram, Target PDF represents the probability density function, and Normal Distribution represents the normal distribution. Using numerical computation software such as MATLAB, and combining the velocity distribution, geometric dimensions, element length, and modal information of the helical tube, the dimensionless PSD (Power Spectral Density) of the external fluid excitation force is transformed into the time history of the fluid excitation force acting on each element. The time history of the fluid excitation force acting on the resistance direction of a certain element is shown in [reference needed]. Figure 5 For each sample input, the ANSYS nonlinear direct integration method was used to solve the flow-induced vibration response of the helical tube, obtaining the normal contact force and relative slip distance between the heat transfer tube and each support. The motion trajectory of the heat transfer tube within section 1 of the nonlinear support under a certain sample input is shown below. Figure 6 ( Figure 6 In this example, Radial represents the radiation distance, Axial represents the axis, and Support represents the support. Finally, this embodiment uses the numerical calculation software MATLAB to input the ANSYS output results and samples from the fretting wear model into a preset wear prediction model, deriving the probability distribution, 95% confidence interval, and failure probability of the heat transfer tube fretting wear depth (e.g., ...). Figure 7 As shown, Figure 7In the diagram, Fretting Wear Deep represents the degree of fretting wear, PDF Distribution represents the distribution of the probability density function, Histogram represents the histogram, Fitted PDF represents the flattened probability density curve, Upper Bound of 95CI represents the upper bound of the 95% confidence interval, Nominal Design represents the average level, and Fretting Wear Deadine represents fretting wear failure. In the embodiments described in this invention, the structural parameters of the helical tube specifically include: geometric dimensions, tube bundle arrangement, tube bundle pitch ratio, internal and external fluid density, and flow velocity. The nonlinear contact state parameters between the heat transfer tube and the support specifically include: design gap, eccentricity, and normal contact stiffness. The linear parameter specifically includes: wear coefficient.

[0103] According to another method for predicting heat transfer tube wear provided by the present invention, compared with the current method of using linear methods to analyze a single factor to predict the wear of heat transfer tubes, the present invention constructs a nonlinear dynamic analysis model of the target heat transfer tube structure and simulates all nonlinear contact state parameters, wear coefficients, and turbulent excitation force time histories that affect the wear of the heat transfer tube. Then, the simulated nonlinear contact state parameters, wear coefficients, and turbulent excitation force time histories are applied to the nonlinear dynamic analysis model to achieve numerical simulation of the transient dynamic analysis of the heat transfer tube to be predicted. Based on the simulation results, the interaction information between the heat transfer tube to be predicted and the supporting components is determined. Finally, based on the interaction information and... The wear coefficient is used to determine the statistical distribution of the wear degree of the heat transfer tube under flow-induced vibration. By considering all nonlinear influencing factors (nonlinear contact state information and linear influencing factors (wear coefficient)) that affect the wear of the heat transfer tube, dynamic simulation is performed in the nonlinear dynamic analysis model. This makes the simulation process closer to the actual working process of the heat transfer tube. In other words, in the process of predicting the wear of the heat transfer tube, both linear and nonlinear influencing factors that affect the wear condition of the heat transfer tube, as well as the impact of the uncertainty of these factors on the results, are comprehensively considered. This makes the interaction information between the heat transfer tube and the supporting components more accurate, and thus makes the wear degree prediction more accurate.

[0104] Furthermore, as Figure 1 In specific implementation, embodiments of the present invention provide a heat transfer tube wear prediction device, such as... Figure 8 As shown, the device includes: an acquisition unit 31, a construction unit 32, a first determination unit 33, a simulation unit 34, and a second determination unit 35.

[0105] The acquisition unit 31 can be used to acquire structural attribute information, constraint boundary information, and working environment information of the target heat transfer pipe structure, as well as the flow field attribute information of the flow field inside and outside the heat transfer pipe to be predicted in the target heat transfer pipe structure. The structural attribute information includes the heat transfer pipe attribute information of the heat transfer pipe to be predicted in the target heat transfer pipe structure and the support attribute information of the support components.

[0106] The construction unit 32 can be used to construct a nonlinear dynamic analysis nominal model of the target heat transfer pipe structure based on the structural attribute information, flow field attribute information, constraint boundary information, and working environment information.

[0107] The first determining unit 33 can be used to determine the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component, and based on the flow field attribute information, heat transfer tube attribute information and supporting attribute information, determine the time history of the turbulent excitation force generated by the heat transfer tube to be predicted under the flow-induced vibration of the internal and external flow fields.

[0108] The simulation unit 34 can be used to apply the nonlinear contact state parameters and the turbulent excitation force time history to the nonlinear dynamic analysis nominal model to realize the numerical simulation of the transient dynamic analysis of the heat transfer tube to be predicted, and determine the interaction information between the heat transfer tube to be predicted and the support component based on the simulation results.

[0109] The second determining unit 35 can be used to determine the degree of wear of the heat transfer tube to be predicted under flow-induced vibration based on the interaction information and the wear coefficient.

[0110] In specific application scenarios, in order to determine the nonlinear contact state parameters and wear coefficient, the first determining unit 33 includes a state determining module 331, a wear determining module 332, and a first determining module 333.

[0111] The state determination module 331 can be used to determine the probability distribution of the nonlinear contact state parameters to be obtained based on the actual design requirements, manufacturing requirements, installation requirements and calculation error information of the target heat transfer pipe structure. The probability distribution of the nonlinear contact state parameters includes: state parameter probability density function, upper limit of state parameter distribution and lower limit of state parameter distribution.

[0112] The wear determination module 332 can be used to determine the probability distribution of the wear coefficient to be obtained based on the actual design requirements, manufacturing requirements, installation requirements and calculation error information of the target heat transfer pipe structure. The probability distribution of the wear coefficient to be obtained includes: wear coefficient probability density function, upper limit of wear coefficient distribution and lower limit of wear coefficient distribution.

[0113] The first determining module 333 can be used to determine the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component based on the probability distribution of the nonlinear contact state parameters to be acquired and the probability distribution of the wear coefficient to be acquired.

[0114] In specific application scenarios, in order to determine the nonlinear contact state parameters and wear coefficient, the first determining module 333 can be used to jointly sample the nonlinear contact state parameters and wear coefficient based on the probability distribution of the nonlinear contact state parameters to be acquired and the probability distribution of the wear coefficient to be acquired, using a preset state sampling algorithm to obtain initial nonlinear contact state parameters and initial wear coefficient; and to verify the accuracy of the probability distribution using a preset probability distribution. If the probability distribution passes the accuracy verification, the initial nonlinear contact state parameters and the initial wear coefficient are determined as the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component. Otherwise, the nonlinear contact state parameters and wear coefficient are resampled using a preset sampling algorithm until the probability distribution of the sampled nonlinear contact state parameters and wear coefficient passes the accuracy verification.

[0115] In specific application scenarios, for the turbulent excitation force time history, the first determining unit 33 also includes a conversion module 334.

[0116] The first determining module 333 can also be used to determine the dimensionless equivalent power spectral density function of the target heat transfer pipe structure.

[0117] The conversion module 334 can be used to convert the dimensionless equivalent power spectral density function into a dimensional equivalent power spectral density function using a preset conversion method.

[0118] The conversion module 334 can be specifically used to convert the dimensional equivalent power spectral density function into the turbulent excitation force time history acting on each unit of the nominal model of the nonlinear dynamic analysis, based on the flow field attribute information, heat transfer pipe attribute information, and support attribute information, using a preset power spectral to time history conversion method.

[0119] In specific application scenarios, in order to determine the wear degree of the heat transfer tube to be predicted under flow-induced vibration, the second determining unit 35 can be specifically used to determine the wear degree of the heat transfer tube to be predicted under flow-induced vibration based on the interaction information and the wear coefficient, including: determining the work done by the support component and the wear area of ​​the heat transfer tube to be predicted in the wear steady state stage based on the normal contact load and the relative sliding distance; determining the wear power of the heat transfer tube to be predicted based on the work done by the wear area in the wear steady state stage; determining the wear volume of the heat transfer tube to be predicted under flow-induced vibration based on the wear power, the preset running time of the heat transfer tube to be predicted, and the wear coefficient; determining the wear depth of the heat transfer tube to be predicted under flow-induced vibration based on the wear volume; and determining the wear degree of the heat transfer tube to be predicted under flow-induced vibration based on the wear depth.

[0120] In specific application scenarios, in order to determine the wear degree of the heat transfer tube to be predicted under flow-induced vibration, the second determining unit 35 includes a second determining module 351, a cross-processing module 352, and a prediction module 353.

[0121] The second determining module 351 can be used to determine the interaction feature vector corresponding to the interaction information and the wear feature vector corresponding to the wear coefficient.

[0122] The cross-processing module 352 can be used to perform cross-processing on the interaction feature vector and the wear feature vector to obtain a wear cross-feature vector.

[0123] The prediction module 353 can be used to input the wear cross feature vector into a preset wear prediction model to predict the wear degree of the heat transfer tube under flow-induced vibration.

[0124] In a specific application scenario, in order to perform cross-processing on the interaction feature vector and the wear feature vector, the cross-processing module 352 can be used to multiply each element in the interaction feature vector with the corresponding element in the wear feature vector to obtain an initial feature-level cross vector, and determine the process convolution transformation function and process vector weights corresponding to the initial feature-level cross vector. Based on the process vector weights, the initial feature-level cross vector is subjected to a convolution transformation using the process convolution transformation function to obtain a feature-level cross vector. The interaction feature vector and the wear feature vector are horizontally concatenated to obtain a concatenated feature vector, and the concatenation coefficients corresponding to the concatenated feature vector are determined. Based on the concatenation coefficients, the concatenated feature vector is linearly transformed to obtain a low-order cross vector. The feature-level cross vector and the low-order cross vector are combined to obtain a wear cross feature vector.

[0125] It should be noted that other corresponding descriptions of the functional modules involved in the heat transfer tube wear prediction device provided in this embodiment of the invention can be found in [reference]. Figure 1 The corresponding descriptions of the methods shown will not be repeated here.

[0126] Based on the above, Figure 1 The method shown, correspondingly, also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the following steps: acquiring structural attribute information, constraint boundary information, and working environment information of the target heat transfer pipe structure, and acquiring flow field attribute information of the internal and external flow fields of the heat transfer pipe to be predicted in the target heat transfer pipe structure, wherein the structural attribute information includes heat transfer pipe attribute information of the heat transfer pipe to be predicted in the target heat transfer pipe structure and support attribute information of the supporting components; constructing a nonlinear dynamic analysis nominal model of the target heat transfer pipe structure based on the structural attribute information, flow field attribute information, constraint boundary information, and working environment information; and determining... The nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component, as well as the turbulent excitation force time history generated by the heat transfer tube under the flow-induced vibration of the internal and external flow fields, are determined based on the flow field attribute information, heat transfer tube attribute information, and supporting component attribute information. The nonlinear contact state parameters and the turbulent excitation force time history are applied to the nonlinear dynamic analysis nominal model to realize the numerical simulation of the transient dynamic analysis of the heat transfer tube to be predicted. The interaction information between the heat transfer tube to be predicted and the supporting component is determined based on the simulation results. Based on the interaction information and the wear coefficient, the wear degree of the heat transfer tube to be predicted under the flow-induced vibration is determined.

[0127] Based on the above, Figure 1 The method shown and as Figure 8 The embodiment of the device shown in the invention also provides a physical structure diagram of a computer device, such as... Figure 9As shown, the computer device includes: a processor 41, a memory 42, and a computer program stored in the memory 42 and executable on the processor. Both the memory 42 and the processor 41 are mounted on a bus 43. When the processor 41 executes the program, it performs the following steps: acquiring structural attribute information, constraint boundary information, and working environment information of the target heat transfer pipe structure; and acquiring flow field attribute information of the internal and external flow fields of the heat transfer pipe to be predicted within the target heat transfer pipe structure. The structural attribute information includes the heat transfer pipe attribute information of the heat transfer pipe to be predicted and the support attribute information of the supporting components within the target heat transfer pipe structure. Based on the structural attribute information, flow field attribute information, constraint boundary information, and working environment information, the target heat transfer pipe is constructed. A nominal model for nonlinear dynamic analysis of the structure is established; the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component are determined; and based on the flow field attribute information, heat transfer tube attribute information, and supporting component attribute information, the time history of the turbulent excitation force generated by the heat transfer tube to be predicted under the flow-induced vibration of the internal and external flow fields is determined; the nonlinear contact state parameters and the turbulent excitation force time history are applied to the nominal model for nonlinear dynamic analysis to achieve numerical simulation of transient dynamic analysis of the heat transfer tube to be predicted, and the interaction information between the heat transfer tube to be predicted and the supporting component is determined based on the simulation results; based on the interaction information and the wear coefficient, the wear degree of the heat transfer tube to be predicted under flow-induced vibration is determined.

[0128] Through the technical solution of this invention, a nominal model of nonlinear dynamic analysis of the target heat transfer tube structure is constructed, and all nonlinear contact state parameters, wear coefficients, and turbulent excitation force time histories affecting the wear of the heat transfer tube are simulated. Then, the simulated nonlinear contact state parameters, wear coefficients, and turbulent excitation force time histories are applied to the nominal model of nonlinear dynamic analysis to achieve numerical simulation of the transient dynamic analysis of the heat transfer tube to be predicted. Based on the simulation results, the interaction information between the heat transfer tube to be predicted and the supporting components is determined. Finally, based on the interaction information and the wear coefficient, the effect of the heat transfer tube to be predicted under flow-induced vibration is determined. The degree of wear is determined by applying all nonlinear influencing factors (nonlinear contact state parameter information), wear coefficient, and linear influencing factors (turbulent excitation force time history) that affect the wear of the heat transfer tube to the nonlinear dynamic analysis model for dynamic simulation. This makes the simulation process closer to the actual working process of the heat transfer tube. In other words, in the process of predicting the wear of the heat transfer tube, all linear and nonlinear influencing factors that affect the wear of the heat transfer tube are comprehensively considered, so that the interaction information between the heat transfer tube to be predicted and the supporting components is more accurate, and thus the wear degree prediction is more accurate.

[0129] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0130] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting wear of heat transfer tubes, characterized in that, include: The structural attribute information, constraint boundary information, and working environment information of the target heat transfer pipe structure are obtained, as well as the flow field attribute information of the flow field inside and outside the heat transfer pipe to be predicted in the target heat transfer pipe structure. The structural attribute information includes the heat transfer pipe attribute information of the heat transfer pipe to be predicted in the target heat transfer pipe structure and the support attribute information of the support components. Based on the structural attribute information, flow field attribute information, constraint boundary information, and working environment information, a nonlinear dynamic analysis nominal model of the target heat transfer pipe structure is constructed. The nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component are determined, and the time history of the turbulent excitation force generated by the heat transfer tube to be predicted under the flow-induced vibration of the internal and external flow fields is determined based on the flow field attribute information, heat transfer tube attribute information and support attribute information. The nonlinear contact state parameters and the turbulent excitation force time history are applied to the nonlinear dynamic analysis nominal model to realize the numerical simulation of the transient dynamic analysis of the heat transfer tube to be predicted, and the interaction information between the heat transfer tube to be predicted and the supporting component is determined based on the simulation results. Based on the interaction information and the wear coefficient, the wear degree of the heat transfer tube to be predicted under flow-induced vibration is determined; The step of determining the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component includes: Based on the actual design requirements, manufacturing requirements, installation requirements, and calculation error information of the target heat transfer pipe structure, the probability distribution of the nonlinear contact state parameters to be acquired is determined. The probability distribution of the nonlinear contact state parameters includes: a state parameter probability density function, an upper limit of the state parameter distribution, and a lower limit of the state parameter distribution. Based on the actual design requirements, manufacturing requirements, installation requirements, and calculation error information of the target heat transfer pipe structure, the probability distribution of the wear coefficient to be acquired is determined. The probability distribution of the wear coefficient to be acquired includes: a wear coefficient probability density function, an upper limit of the wear coefficient distribution, and a lower limit of the wear coefficient distribution. Based on the probability distribution of the nonlinear contact state parameters to be acquired and the probability distribution of the wear coefficient to be acquired, the nonlinear contact state parameters and wear coefficient between the heat transfer pipe to be predicted and the supporting component are determined. The step of determining the time history of the turbulent excitation force generated by the heat transfer tube under the flow-induced vibration of the internal and external flow fields based on the flow field attribute information, heat transfer tube attribute information, and support attribute information includes: The dimensionless equivalent power spectral density function of the target heat transfer pipe structure is determined; the dimensionless equivalent power spectral density function is converted into a dimensional equivalent power spectral density function using a preset conversion method; based on the flow field attribute information, heat transfer pipe attribute information, and support attribute information, the dimensional equivalent power spectral density function is converted into the turbulent excitation force time history acting on each element of the nominal model of the nonlinear dynamic analysis using a preset power spectral density to time history conversion method.

2. The method according to claim 1, characterized in that, The determination of the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component based on the probability distribution of the nonlinear contact state parameters to be acquired and the probability distribution of the wear coefficient to be acquired includes: Based on the probability distribution of the nonlinear contact state parameters to be acquired and the probability distribution of the wear coefficient to be acquired, the nonlinear contact state parameters and the wear coefficient are jointly sampled using a preset state sampling algorithm to obtain the initial nonlinear contact state parameters and the initial wear coefficient. The probability distribution is verified for accuracy using a preset probability distribution. If the probability distribution passes the accuracy verification, the initial nonlinear contact state parameter and the initial wear coefficient are determined as the nonlinear contact state parameter and wear coefficient between the heat transfer tube to be predicted and the supporting component. Otherwise, the nonlinear contact state parameter and wear coefficient are resampled using a preset sampling algorithm until the probability distribution of the sampled nonlinear contact state parameter and wear coefficient passes the accuracy verification.

3. The method according to claim 1, characterized in that, The interaction information includes: the normal contact load and relative slip distance between the heat transfer tube to be predicted and the supporting component; The determination of the wear degree of the heat transfer tube to be predicted under flow-induced vibration based on the interaction information and the wear coefficient includes: Based on the normal contact load and relative slip distance, determine the work done by the support component and the wear area of ​​the heat transfer tube to be predicted during the wear steady state stage. The wear power of the heat transfer tube to be predicted is determined based on the work done by the wear region during the wear steady-state stage. Based on the wear power, the preset operating time of the heat transfer tube to be predicted, and the wear coefficient, the wear volume of the heat transfer tube to be predicted under flow-induced vibration is determined. Based on the wear volume, the wear depth of the heat transfer tube to be predicted under flow-induced vibration is determined, and based on the wear depth, the degree of wear of the heat transfer tube to be predicted under flow-induced vibration is determined.

4. The method according to claim 1, characterized in that, The determination of the wear degree of the heat transfer tube to be predicted under flow-induced vibration based on the interaction information and the wear coefficient includes: Determine the interaction feature vector corresponding to the interaction information, and determine the wear feature vector corresponding to the wear coefficient; The interaction feature vector and the wear feature vector are cross-processed to obtain the wear cross feature vector; The wear cross feature vector is input into a preset wear prediction model to predict the wear degree of the heat transfer tube under flow-induced vibration.

5. The method according to claim 4, characterized in that, The step of performing cross-processing on the interaction feature vector and the wear feature vector to obtain the wear cross-feature vector includes: Each element in the interaction feature vector is multiplied by the corresponding element in the wear feature vector to obtain an initial feature-level cross vector. The process convolution transformation function and process vector weights corresponding to the initial feature-level cross vector are determined. Based on the process vector weights, the initial feature-level cross vector is convolved using the process convolution transformation function to obtain the feature-level cross vector. The interaction feature vector and the wear feature vector are horizontally concatenated to obtain a concatenated feature vector, and the concatenation coefficient corresponding to the concatenated feature vector is determined. Based on the concatenation coefficient, the concatenated feature vector is linearly transformed to obtain a low-order cross vector. The wear cross feature vector is obtained by combining the feature-level cross vector and the low-order cross vector.

6. A heat transfer tube wear prediction device, characterized in that, include: The acquisition unit is used to acquire structural attribute information, constraint boundary information, and working environment information of the target heat transfer pipe structure, as well as flow field attribute information of the flow field inside and outside the heat transfer pipe to be predicted in the target heat transfer pipe structure. The structural attribute information includes the heat transfer pipe attribute information of the heat transfer pipe to be predicted in the target heat transfer pipe structure and the support attribute information of the support components. The construction unit is used to construct a nonlinear dynamic analysis nominal model of the target heat transfer pipe structure based on the structural attribute information, flow field attribute information, constraint boundary information, and working environment information. The first determining unit is used to determine the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component, and to determine the time history of the turbulent excitation force generated by the heat transfer tube under the flow-induced vibration of the internal and external flow fields based on the flow field attribute information, heat transfer tube attribute information, and supporting attribute information. The determination of the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component includes: determining the probability distribution of the nonlinear contact state parameters to be acquired based on the actual design requirements, manufacturing requirements, installation requirements, and calculation error information of the target heat transfer tube structure; wherein the probability distribution of the nonlinear contact state parameters includes: a state parameter probability density function, an upper limit of the state parameter distribution, and a lower limit of the state parameter distribution; and determining the probability distribution of the wear coefficient to be acquired based on the actual design requirements, manufacturing requirements, installation requirements, and calculation error information of the target heat transfer tube structure. The probability distribution of the coefficients includes: the wear coefficient probability density function, the upper limit of the wear coefficient distribution, and the lower limit of the wear coefficient distribution; based on the probability distribution of the nonlinear contact state parameters to be acquired and the probability distribution of the wear coefficient to be acquired, the nonlinear contact state parameters and wear coefficient between the heat transfer tube to be predicted and the supporting component are determined; the determination of the turbulent excitation force time history generated by the heat transfer tube to be predicted under the flow-induced vibration of the internal and external flow fields based on the flow field attribute information, heat transfer tube attribute information, and support attribute information includes: determining the dimensionless equivalent power spectral density function of the target heat transfer tube structure; converting the dimensionless equivalent power spectral density function into a dimensional equivalent power spectral density function using a preset conversion method; and converting the dimensional equivalent power spectral density function into the turbulent excitation force time history acting on each element of the nominal model of the nonlinear dynamic analysis using a preset power spectral density to time history method based on the flow field attribute information, heat transfer tube attribute information, and support attribute information. The simulation unit is used to apply the nonlinear contact state parameters and the turbulent excitation force time history to the nonlinear dynamic analysis nominal model to realize the numerical simulation of the transient dynamic analysis of the heat transfer tube to be predicted, and to determine the interaction information between the heat transfer tube to be predicted and the supporting component based on the simulation results. The second determining unit is used to determine the degree of wear of the heat transfer tube to be predicted under flow-induced vibration based on the interaction information and the wear coefficient.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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