A micro-motion fatigue life prediction method, system, device, medium and product for a turbine disk-bleed disk rotor system

Abstract

This application discloses a method, system, device, medium, and product for predicting the fretting fatigue life of turbine disk-guide disk rotor systems, relating to the field of damage analysis of turbine disk-guide disk rotor systems. The method includes: acquiring the geometry and metallic material of the turbine disk-guide disk rotor system, and further constructing a hazardous area identification model; identifying hazardous areas of the turbine disk-guide disk rotor system based on the hazardous area identification model; determining the corresponding surface optimization process based on the metallic material and optimizing the surface condition of the hazardous areas to obtain optimized hazardous areas; further constructing a life prediction model; and determining the fretting fatigue life of the turbine disk-guide disk rotor system based on the life prediction model. This application can accurately predict the fretting fatigue life of turbine disk-guide disk rotor systems.

Description

Technical Field

[0001] This application relates to the field of damage analysis of turbine disk-guide disk rotor systems, and in particular to a method, system, device, medium and product for predicting fretting fatigue life of turbine disk-guide disk rotor systems. Background Technology

[0002] Analyzing the damage of a turbine disk-guide disk rotor system requires studying the mechanical behavior and damage evolution of the hot-end components during service. However, the mechanical behavior and damage evolution of hot-end components during service are extremely complex, manifesting not only at the nanoscale (e.g., atomic arrangement, dislocation motion, or lattice distortion) but also at the macroscale (deformation behavior and stress state). Damage analysis of high-temperature structures can be conducted at four scales: nanoscale, microscale, macroscale, and structural scale.

[0003] Currently, researchers mostly focus on parallel and series multi-scale damage studies. Parallel multi-scale damage studies are primarily used for qualitative damage analysis. While micro- and nano-scale characterization and simulation methods can effectively reveal the deformation and damage mechanisms of materials, they cannot quantitatively describe the damage patterns of engineering components. Series multi-scale damage studies can determine the correspondence between microscopic parameters and macroscopic lifetimes and achieve multi-scale simulations of high-temperature components, but they neglect the spatiotemporal synchronicity of interactions between different scales, leading to a decrease in predictive capability.

[0004] Therefore, based on the above problems, there is an urgent need to provide a method for predicting the fretting fatigue life of turbine disk-guide disk rotor systems, which can accurately predict the fretting fatigue life of turbine disk-guide disk rotor systems. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, device, medium, and product for predicting the fretting fatigue life of a turbine disk-guide disk rotor system, which can accurately predict the fretting fatigue life of the turbine disk-guide disk rotor system.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] In a first aspect, this application provides a method for predicting the fretting fatigue life of a turbine disk-guide disk rotor system, including:

[0008] Obtain the geometry and metallic materials of the turbine disk-guide disk rotor system;

[0009] Based on the aforementioned geometry and metallic material, a hazardous area identification model is constructed. The hazardous area identification model is then used to identify the hazardous areas of the turbine disk-guide disk rotor system. This model is used for finite element modeling based on the geometry and to simulate the mechanical properties of the metallic material using an improved Chaboche cyclic constitutive model, yielding a stress-strain-displacement field. Simultaneously, under fretting and fatigue coupled loads, a multi-parameter damage-driven cyclic plastic work parameter is introduced to automatically identify the hazardous areas of the turbine disk-guide disk rotor system. The hazardous areas are located at the connection interface between the turbine disk and the guide disk.

[0010] The metallic materials in the hazardous area are analyzed to determine surface optimization processes to optimize the hazardous area; the surface optimization processes include: water jet strengthening process;

[0011] Based on the optimized hazardous area, a life prediction model is constructed; and the fretting fatigue life of the turbine disk-guide disk rotor system is determined based on the life prediction model; the life prediction model is used to perform finite element modeling of the hazardous area based on the crystal plastic finite element method, and to determine the fretting fatigue life of the turbine disk-guide disk rotor system using the improved crystal plastic constitutive model.

[0012] Optionally, constructing a hazardous area identification model based on the geometric structure and metallic material specifically includes:

[0013] Based on the metallic material of the turbine disk-guide disk rotor system, the corresponding mechanical test data are determined; the mechanical test data includes: high-temperature tensile data and low-cycle fatigue data;

[0014] The parameters of the Chaboche cycle constitutive model were calibrated based on the mechanical test data to obtain an improved Chaboche cycle constitutive model.

[0015] Based on the aforementioned geometry and the improved Chaboche cycle constitutive model, a hazardous area identification model is constructed.

[0016] Optionally, the surface optimization process is used to optimize the surface condition of the hazardous area to obtain an optimized hazardous area, specifically including:

[0017] Extract the coordinates of the surface contour points and the normal vector of the hazardous area, and determine the contour of the hazardous area;

[0018] Based on the outline of the danger zone, an optimized trajectory for the danger zone is planned;

[0019] Based on the optimized trajectory, the grain morphology of the surface of the hazardous area is optimized using a surface optimization process to obtain the optimized hazardous area.

[0020] Optionally, determining the fretting fatigue life of the turbine disk-guide disk rotor system based on the life prediction model specifically includes:

[0021] The parameters of the crystal plastic constitutive model were calibrated based on mechanical test data to obtain an improved crystal plastic constitutive model.

[0022] The finite element method of crystal plasticity is used to model the hazardous area; and the cumulative energy dissipation of the hazardous area is determined by the improved crystal plasticity constitutive model.

[0023] The fretting fatigue life of the turbine disk-guide disk rotor system is determined based on the cumulative energy dissipation.

[0024] Optionally, the determination of the cumulative energy dissipation in the hazardous region using an improved crystal plastic constitutive model specifically includes:

[0025] Using formula Determine the cumulative energy dissipation in the danger zone ;

[0026] in, Numbering of slip systems The total number of slip systems, For a moment, For the first Decomposed shear stress on a slip system For the first Shear strain rate on a slip system.

[0027] Optionally, determining the fretting fatigue life of the turbine disk-guide disk rotor system based on cumulative energy dissipation specifically includes:

[0028] Using formula Determine the fretting fatigue life of the turbine disk-guide disk rotor system;

[0029] in, This is the critical value for cumulative energy dissipation. These are hardening parameters. For fatigue crack initiation life, This represents the cumulative energy dissipation value per week.

[0030] Secondly, this application provides a fretting fatigue life prediction system for turbine disk-guide disk rotor systems, the fretting fatigue life prediction system for turbine disk-guide disk rotor systems comprising:

[0031] A geometry and metal material acquisition module is used to acquire the geometry and metal material of the turbine disk-guide disk rotor system.

[0032] A hazardous area identification model construction module is used to construct a hazardous area identification model based on the geometric structure and metallic material; and to identify hazardous areas of the turbine disk-guide disk rotor system based on the hazardous area identification model; the hazardous area identification model is used to perform finite element modeling based on the geometric structure, and to simulate the mechanical properties of the metallic material using an improved Chaboche cyclic constitutive model to obtain the stress-strain-displacement field; simultaneously, under the action of fretting and fatigue coupled loads, a multi-parameter damage-driven cyclic plastic work parameter is introduced to automatically identify the hazardous areas of the turbine disk-guide disk rotor system; the hazardous areas are located at the connection interface between the turbine disk and the guide disk;

[0033] The hazardous area optimization module is used to analyze the metal materials in hazardous areas and determine surface optimization processes to optimize the hazardous areas; the surface optimization processes include: water jet strengthening process;

[0034] The life prediction model construction module is used to construct a life prediction model based on the optimized hazardous area; and to determine the fretting fatigue life of the turbine disk-guide disk rotor system based on the life prediction model; the life prediction model is used to perform finite element modeling of the hazardous area based on the crystal plastic finite element method, and to determine the fretting fatigue life of the turbine disk-guide disk rotor system using the improved crystal plastic constitutive model.

[0035] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for predicting the fretting fatigue life of a turbine disk-guide disk rotor system.

[0036] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for predicting the fretting fatigue life of a turbine disk-guide disk rotor system.

[0037] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for predicting the fretting fatigue life of a turbine disk-guide disk rotor system.

[0038] According to the specific embodiments provided in this application, this application has the following technical effects:

[0039] This application provides a method, system, device, medium, and product for predicting the fretting fatigue life of a turbine disk-guide disk rotor system. Based on the geometry and metallic materials of the turbine disk-guide disk rotor system, a hazardous area identification model is constructed. An improved Chaboche cyclic constitutive model is used, introducing a multi-parameter damage-driven cyclic plastic work parameter to automatically identify the hazardous areas of the turbine disk-guide disk rotor system. Surface optimization technology is employed to optimize the surface condition of the hazardous areas, improving the fretting fatigue life of the turbine disk-guide disk rotor system. A life prediction model is constructed in the optimized hazardous areas to determine the fretting fatigue life of the turbine disk-guide disk rotor system, achieving accurate prediction of the fretting fatigue life of the turbine disk-guide disk rotor system. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating a fretting fatigue life prediction method for a turbine disk-guide disk rotor system according to an embodiment of this application.

[0042] Figure 2 The diagram shows the parameter calibration results of the improved Chaboche cycle constitutive model and the improved crystal plastic constitutive model of the turbine disk-guide disk rotor system metallic material specimen in one embodiment of this application. Figure 2 Part (a) presents a comparison of the simulated hysteresis loops obtained from mechanical test data with a strain amplitude of 0.6%, the improved Chaboche cyclic constitutive model, and the improved crystal plastic constitutive model. Figure 2 Part (b) is a comparison of the simulated hysteresis loops obtained from mechanical test data with a strain amplitude of 1.0%, the improved Chaboche cyclic constitutive model, and the improved crystal plastic constitutive model.

[0043] Figure 3 This is a schematic diagram illustrating the identification effect of a hazardous area identification model in one embodiment of this application;

[0044] Figure 4 This is a schematic diagram of a CPFE model in one embodiment of this application;

[0045] Figure 5 This is a schematic diagram of the life prediction results of a metallic material sample in one embodiment of this application;

[0046] Figure 6This is a schematic diagram of the cumulative energy dissipation in a hazardous area according to one embodiment of this application. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] In one exemplary embodiment, such as Figure 1 As shown, a method for predicting the fretting fatigue life of a turbine disk-guide disk rotor system is provided, including the following S1 to S4. Wherein:

[0050] S1: Obtain the geometry and metal materials of the turbine disk-guide disk rotor system.

[0051] S2: Construct a hazardous area identification model based on the geometric structure and metal material; and identify the hazardous areas of the turbine disk-guide disk rotor system based on the hazardous area identification model.

[0052] The danger zone is located at the interface between the turbine disk and the guide disk.

[0053] S2 specifically includes:

[0054] S21: Determine the corresponding mechanical test data based on the metal material of the turbine disk-guide disk rotor system.

[0055] Obtain metallic material samples made of the same material as the turbine disk-guide disk rotor system, and determine the mechanical test data of the metallic material samples, including high-temperature tensile data and low-cycle fatigue data.

[0056] S22: Based on mechanical test data, the parameters of the Chaboche cycle constitutive model are calibrated to obtain an improved Chaboche cycle constitutive model.

[0057] Specifically, the parameters of the Chaboche cyclic constitutive model were calibrated using a trial-and-error method to obtain an improved Chaboche cyclic constitutive model. This improved model was then used to simulate the mechanical properties of metallic materials. After parameter calibration, the material mechanical property data obtained from the improved Chaboche cyclic constitutive model were compared with experimental mechanical data to verify the effectiveness of the improved model. The material mechanical property data included high-temperature tensile data and low-cycle fatigue data.

[0058] The improved Chaboche cycle constitutive model mentioned in this application can be a model obtained by improving the Chaboche cycle constitutive model using any reasonable method, such as by introducing a new static recovery term to develop a modified Chaboche kinematic hardening criterion, thereby accurately describing the creep or relaxation deformation behavior of the material in the turbine disk-guide disk rotor system during cruise; specifically, the kinematic hardening criterion is expressed using the back stress evolution equation:

[0059] ;

[0060] ;

[0061] ;

[0062] in, The back stress evolution equation is... and For the material parameters of each back stress component, It is an inelastic strain rate tensor. For the back stress component, To accumulate inelastic strain rate, For the range of plastic strain, The exponential equation for the plastic strain range q is given. , and All of these are model parameters fitted using experimental data. The symbol for the exponent in scientific notation. For equivalent back stress, and These are the control parameters for the amplitude of two traditional static recovery forces.

[0063] The improved Chaboche cyclic constitutive model is programmed in Fortran and formed into a user-defined subroutine (UMAT) recognizable by the ABAQUS finite element software. The UMAT of the improved Chaboche cyclic constitutive model is called by ABAQUS, thereby calibrating and verifying the relevant parameters of the improved Chaboche cyclic constitutive model based on mechanical test data.

[0064] In one exemplary embodiment, taking a turbine disk made of powder superalloy material as an example, high-temperature tensile data and low-cycle fatigue data of the powder superalloy sample are obtained. The parameters of the Chaboche cycle constitutive model are calibrated and verified to obtain an improved Chaboche cycle constitutive model of the powder superalloy FGH109 sample. The improved Chaboche cycle constitutive model is programmed using Fortran language and formed into a UMAT. The UMAT of the improved Chaboche cycle constitutive model is called through ABAQUS. The parameters of the improved Chaboche cycle constitutive model are further verified based on the mechanical test data of the FGH109 sample.

[0065] like Figure 2 As shown, the stress-strain curve of the FGH109 specimen was simulated using an improved Chaboche cyclic constitutive model and compared with mechanical test data (hereinafter referred to as test data). According to the comparison, the improved Chaboche cyclic constitutive model of this application can better match the mechanical test data of the powder superalloy FGH109 specimen.

[0066] S23: Construct a hazardous area identification model based on the geometric structure and the improved Chaboche cycle constitutive model.

[0067] The hazardous area identification model is based on the finite element model of the turbine disk-guide disk rotor system geometry, and the mechanical properties of the metallic material are simulated using the improved Chaboche cyclic constitutive model to obtain the stress-strain-displacement field. At the same time, under the action of fretting and fatigue coupled loads, a multi-parameter damage-driven cyclic plastic work parameter is introduced to automatically identify the hazardous areas of the turbine disk-guide disk rotor system.

[0068] Finite element modeling includes geometric modeling, material assignment, model assembly, analysis step settings, mesh generation, application of load boundary conditions, and application of interaction conditions. After finite element modeling, an improved Chaboche cyclic constitutive model is used to simulate the mechanical properties of the metallic materials used in the turbine disk-guide disk rotor system, enabling high-precision solutions for the stress-strain-displacement field of the metallic materials. A hazardous region identification model is used to simulate the interactive loads of fretting and fatigue. The simulation results include fatigue damage and fretting damage. Based on the simulation results, the cyclic plastic work parameter driven by multi-parameter damage is obtained, and the potential risk level of the region is determined based on the cyclic plastic work parameter driven by multi-parameter damage, thus identifying the hazardous region. The hazardous region of the turbine disk-guide disk rotor system is located at the connection interface between the turbine disk and the guide disk.

[0069] The multi-parameter damage-driven cyclic plastic work parameter combines parameters coupled with fatigue damage and fretting damage, with fatigue cyclic plastic work as the main component, corrected by the fretting influence coefficient. The calculation formula is as follows:

[0070] ;

[0071] in, For cyclic plastic work under pure fatigue conditions, The fretting damage index is composed of contact normal pressure and slip amplitude. The material coefficient is used to characterize the fatigue amplification effect of fretting.

[0072] In one exemplary embodiment, finite element modeling of the turbine disk-guide disk rotor system (metal material FGH109) is performed in ABAQUS based on the dimensions of each component. A calculation example is submitted via ABAQUS, and the improved Chaboche cyclic constitutive model in UMAT is invoked. Based on the multi-parameter damage-driven cyclic plastic work parameter, the critical areas of the turbine disk-guide disk rotor system are identified, such as... Figure 3 As shown.

[0073] S3: Analyze the metallic materials in the hazardous area and determine the surface optimization process to optimize the hazardous area.

[0074] Critical areas are prone to fatigue failure; therefore, surface optimization processes are required to optimize the surface condition of critical areas.

[0075] S3 specifically includes:

[0076] S31: Determine the corresponding surface optimization process based on the metal material.

[0077] A flat plate, made of the same material as the turbine disk-guide disk rotor system and measuring 20mm × 40mm × 8mm, is used as the process part. The corresponding surface optimization process is determined based on the metal material of the process part. The surface optimization process can induce plastic deformation in critical areas, thereby optimizing the grain morphology. The surface optimization processes include shot peening, ultrasonic rolling, and waterjet peening. The main process parameters for shot peening include spray speed, spray pressure, and spray angle; for ultrasonic rolling, the main process parameters include rolling pressure, ultrasonic frequency, number of rolling passes, and feed rate; and for waterjet peening, the main process parameters include jet pressure, feed rate, trajectory interval, and jet angle.

[0078] In one exemplary embodiment, a cavitation abrasive composite waterjet process is used to optimize the surface condition of a hazardous area. The basic principle of cavitation abrasive composite waterjet surface strengthening is as follows: tap water is pressurized to 50MPa~250MPa by an ultra-high pressure booster and transmitted to the jet nozzle. The high-pressure water is then accelerated as it passes through a 0.33mm sapphire orifice and injected into the nozzle mixing chamber. The ultra-high-speed water flow creates a Venturi effect in the mixing chamber, generating a certain degree of negative pressure. Most of the abrasive and a small amount of air are drawn into the mixing chamber under the combined action of gravity and negative pressure. Therefore, the abrasive, air, and water are thoroughly mixed in the mixing chamber to form a multiphase flow. Finally, the mixed multiphase fluid is ejected through a focusing tube. The waterjet strengthening process parameters include 16 angled nozzle parameters and 3 straight nozzle parameters. Selecting appropriate combinations of waterjet strengthening process parameters for cavitation abrasive composite waterjet strengthening of the hazardous area of ​​the turbine disk-guide disk rotor system can improve the fretting fatigue life of the rotor system.

[0079] S32: Extract the coordinates of the surface contour points and the normal vector of the hazardous area, and determine the contour of the hazardous area.

[0080] Because the hazardous area of ​​the turbine disk-guide disk rotor system is large and complex, it is necessary to plan the optimization trajectory of the surface optimization process. Before that, it is necessary to extract the surface contour point coordinates and normal vectors of the hazardous area to determine the contour of the hazardous area.

[0081] In one exemplary embodiment, a water jet enhancement process is used to optimize the hazardous area. The geometric model of the turbine disk-guide disk rotor system is imported into CAE software to determine the hazardous area range to be enhanced at the connection interface of the turbine disk-guide disk rotor system. The hazardous area contour curve is divided according to the predetermined trajectory interval, jet target distance, and feed velocity. The surface contour point coordinates and normal vectors of the hazardous area are extracted, and the corresponding optimized trajectory is planned based on the obtained contour point cloud data.

[0082] S33: Based on the outline of the hazardous area, plan the optimized trajectory of the hazardous area.

[0083] The optimization trajectory of the surface optimization process is planned based on the contour of the hazardous area, so that the selected surface optimization process can completely optimize the entire hazardous area.

[0084] In one exemplary embodiment, a water jet enhancement process is used to optimize the hazardous area. The corresponding optimization trajectory planning process is as follows: During the water jet enhancement process, the jet nozzle moves radially along the turbine disk in a "Z" shape under the drive of the robotic arm and feeds circumferentially along the turbine disk. During the jet nozzle feeding process, the jet exit direction is always kept parallel to the normal of the hazardous area surface and a fixed jet target distance is maintained with the hazardous area surface to ensure that the high-energy jet impacts the turbine disk surface perpendicularly, forming a uniform and stable enhancement layer. Finally, the optimized trajectory point cloud data is converted into robotic arm motion code and uploaded to the Robotstudio interactive platform.

[0085] S34: Based on the optimized trajectory, the grain morphology of the surface of the hazardous area is optimized using surface optimization technology to obtain the optimized hazardous area.

[0086] S4: Construct a life prediction model based on the optimized hazardous area; and determine the fretting fatigue life of the turbine disk-guide disk rotor system based on the life prediction model.

[0087] The life prediction model is used to perform finite element modeling of the critical area based on the crystal plastic finite element method, and to determine the fretting fatigue life of the turbine disk-guide disk rotor system using an improved crystal plastic constitutive model.

[0088] S4 specifically includes:

[0089] S41: Based on the mechanical test data, the parameters of the crystal plastic constitutive model are calibrated to obtain an improved crystal plastic constitutive model.

[0090] The parameters of the crystal plasticity constitutive model were calibrated using a trial-and-error method to obtain an improved crystal plasticity constitutive model (CPM). The material mechanical property data obtained from the improved CPM were compared with mechanical test data to verify the effectiveness of the improved CPM.

[0091] The improved crystal plasticity constitutive model mentioned in this application can be any model obtained by improving the crystal plasticity constitutive model using any reasonable method. For example, the crystal plasticity constitutive model can be modified by introducing the Hall-Petch relation into the initial slip resistance equation, that is, the grain size effect is considered in the initial slip resistance equation to describe the influence of the micro-gradient structure at the pore root. The relationship between the initial slip resistance S0 and the grain size d can be written similarly to the Hall-Petch criterion:

[0092] ;

[0093] in, The initial sliding resistance is independent of grain size. These are the material parameters related to Hall-Petch.

[0094] Similar to the improved Chaboche cyclic constitutive model, the improved crystal plastic constitutive model is programmed in Fortran and formed into a user-defined subroutine (UMAT) recognizable by the ABAQUS finite element software. The UMAT of the improved crystal plastic constitutive model is then called through ABAQUS, thereby calibrating and verifying the relevant parameters of the improved crystal plastic constitutive model based on mechanical test data.

[0095] In one exemplary embodiment, taking a turbine disk made of powder superalloy material as an example, high-temperature tensile data and low-cycle fatigue data of powder superalloy samples are obtained to calibrate and verify the parameters of the improved crystal plastic constitutive model. The improved crystal plastic constitutive model is programmed using Fortran language and formed into UMAT. The UMAT of the improved crystal plastic constitutive model is called through ABAQUS. The parameters of the improved crystal plastic constitutive model are further verified based on the mechanical test data of FGH109 sample.

[0096] like Figure 2 As shown, the stress-strain curve of the FGH109 sample was simulated using an improved crystal plastic constitutive model and compared with mechanical test data (hereinafter referred to as test data). According to the comparison, the improved crystal plastic constitutive model of this application can better match the mechanical test data of the powder superalloy FGH109 sample.

[0097] S42: The finite element model of the hazardous area is performed using the crystal plasticity finite element method; and the cumulative energy dissipation of the hazardous area is determined using the improved crystal plasticity constitutive model.

[0098] Based on the hazardous region, a Voronoi polycrystalline model containing the actual grain morphology and orientation distribution is constructed using the Voronoi Tessellation (VT) technique and the Crystal Plasticity Finite Element (CPFE) method. This Voronoi polycrystalline model is the Crystal Plasticity Finite Element (CPFE) model. The improved crystal plasticity constitutive model can not only identify the stress-strain field in the hazardous region but also calculate the cumulative energy dissipation within it. Specifically, it calculates the cumulative energy dissipation in the microscopic components. The calculation formula is as follows:

[0099] ;

[0100] in, Numbering of slip systems The total number of slip systems, For a moment, For the first Decomposed shear stress on a slip system For the first Shear strain rate on a slip system.

[0101] In one exemplary embodiment, the method for constructing the CPFE model is as follows: First, the microscopic grain distribution and grain size of the material under study (the metallic material of the turbine disk-guide disk rotor system) are obtained experimentally; specifically, the average grain size of the powder superalloy is approximately 5 micrometers; a model containing approximately 400 grains is generated in ABAQUS using Voronoi Tessellation (VT) technology. Subsequently, to reflect the grain size gradient in the CPFE model, a grain distribution with a size gradient needs to be constructed based on the average grain size. Finally, each grain is assigned the same material properties but different Euler angles and parameters related to the Hall-Petch criterion; the CPFE model is then meshed to establish the CPFE model.

[0102] S43: Determine the fretting fatigue life of the turbine disk-guide disk rotor system based on the cumulative energy dissipation.

[0103] Specifically, the formula for calculating the fretting fatigue life of the turbine disk-guide disk rotor system is as follows:

[0104] ;

[0105] in, This is the critical value for cumulative energy dissipation. These are hardening parameters. For fatigue crack initiation life, This represents the cumulative energy dissipation value per week.

[0106] This application uses the critical value of cumulative energy dissipation coupled with the energy critical criterion modified by the cyclic hardening parameter as the fatigue failure criterion, and realizes the accurate prediction of the fretting fatigue life of the turbine disk-guide disk rotor system.

[0107] In summary, this application constructs a macro-micro dual-scale adaptive embedding model. The macro-scale part represents the entire turbine disk-guide disk rotor system, while the micro-scale part represents the critical region. The macro-micro dual-scale adaptive embedding model includes an improved Chaboche cycle constitutive model used in the macro-scale part and an improved crystal plasticity constitutive model used in the micro-scale part. The micro- and macro-scale parts of the macro-micro dual-scale adaptive embedding model maintain spatiotemporal continuity. By improving the embedding surface structure (using mesh optimization methods), excessive stress-strain distortion is prevented in the transition region between the macro- and micro-scale parts, achieving stable stress-strain field transmission and avoiding the boundary distortion discontinuity problem of traditional embedding models. The embedding surface is the interface between the macro- and micro-scale parts. Specifically, the mesh optimization method of this application is as follows: meshing is performed on the embedding surface and the transition region around the embedding surface, and the mesh distribution on both sides of the embedding surface is coordinated. The mesh size gradually decreases from the macroscopic part to the microscopic part to avoid abrupt changes in mesh size near the embedding surface. At the same time, the stress, strain and displacement solved in the macroscopic part are more smoothly transferred to the microscopic part. After meshing and obtaining the stress and strain distribution near the embedding surface, the local mesh (the mesh with large stress changes) is further adjusted according to the stress and strain conditions to reduce the stress-strain distortion of the embedding surface and achieve stable stress and strain transfer between the macroscopic part and the microscopic part.

[0108] In one exemplary embodiment, the geometric model fusion method based on ABAQUS enables synchronous and continuous analysis of the macroscopic and microscopic parts at the spatiotemporal level. Although the constitutive models of the macroscopic and microscopic parts are different, the average stress-strain fields of the macroscopic and microscopic parts are almost the same because the constitutive parameters of both parts have been calibrated and verified.

[0109] An improved Chaboche cyclic constitutive model is used for the macroscopic part, and an improved crystal plasticity constitutive model is used for the microscopic part. By improving the embedding surface structure, the improved Chaboche cyclic constitutive model and the improved crystal plasticity constitutive model are combined into a macroscopic-microscopic dual-scale adaptive embedding model, such as... Figure 4 As shown, the macroscopic and microscopic parts remain continuous in the spatiotemporal layer, and the stress-strain field is transferred through the improved embedded surface structure.

[0110] In one exemplary embodiment, a computational example of a macro-micro dual-scale adaptive embedding model is submitted via ABAQUS, which calls the UMAT of the parameter-calibrated improved Chaboche cyclic constitutive model and the improved crystal plastic constitutive model, and calculates the cumulative energy dissipation in the hazardous region.

[0111] When the FGH109 specimen is subjected to fretting fatigue conditions, the method described in this application yields good life prediction results. For example... Figure 5 As shown, the error between the predicted life and the test life is within 2.5 times the dispersion band, indicating that the life prediction method of this application can accurately predict the fretting fatigue life of the specimen.

[0112] In one exemplary embodiment, such as Figure 6 As shown, the maximum cumulative energy dissipation occurs on the grains at the surface where fretting fatigue occurs. Based on the fretting fatigue life prediction of this application, the predicted lifespan of the turbine disk-guide disk rotor system is greater than 50,000 cycles.

[0113] Based on the same inventive concept, this application also provides a fretting fatigue life prediction system for turbine disk-guide disk rotor systems to implement the above-described method. The solution provided by this system is similar to the implementation described in the fretting fatigue life prediction method for turbine disk-guide disk rotor systems. Therefore, the specific limitations of one or more embodiments of the fretting fatigue life prediction system for turbine disk-guide disk rotor systems provided below can be found in the limitations of the fretting fatigue life prediction method for turbine disk-guide disk rotor systems described above, and will not be repeated here.

[0114] In one exemplary embodiment, this application provides a fretting fatigue life prediction system for a turbine disk-guide disk rotor system, comprising:

[0115] A geometry and metal material acquisition module is used to acquire the geometry and metal material of the turbine disk-guide disk rotor system.

[0116] A hazardous area identification model construction module is used to construct a hazardous area identification model based on the geometric structure and metallic material; and to identify hazardous areas of the turbine disk-guide disk rotor system based on the hazardous area identification model; the hazardous area identification model is used to perform finite element modeling based on the geometric structure, and to simulate the mechanical properties of the metallic material using an improved Chaboche cyclic constitutive model to obtain the stress-strain-displacement field; simultaneously, under the action of fretting and fatigue coupled loads, a multi-parameter damage-driven cyclic plastic work parameter is introduced to automatically identify the hazardous areas of the turbine disk-guide disk rotor system; the hazardous areas are located at the connection interface between the turbine disk and the guide disk;

[0117] The hazardous area optimization module is used to analyze the metal materials in hazardous areas and determine surface optimization processes to optimize the hazardous areas; the surface optimization processes include: water jet strengthening process;

[0118] The life prediction model construction module is used to construct a life prediction model based on the optimized hazardous area; and to determine the fretting fatigue life of the turbine disk-guide disk rotor system based on the life prediction model; the life prediction model is used to perform finite element modeling of the hazardous area based on the crystal plastic finite element method, and to determine the fretting fatigue life of the turbine disk-guide disk rotor system using the improved crystal plastic constitutive model.

[0119] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores fretting fatigue life prediction data for turbine disk-guide disk rotor systems. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a fretting fatigue life prediction method for turbine disk-guide disk rotor systems.

[0120] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0121] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0122] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0123] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0124] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0125] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0126] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0127] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for micro- fatigue life prediction for a turbine disk- bucket rotor system, characterized by, The method for predicting the fretting fatigue life of the turbine disk-guide disk rotor system includes: Obtain the geometry and metallic materials of the turbine disk-guide disk rotor system; Based on the aforementioned geometry and metallic material, a hazardous area identification model is constructed. The hazardous area identification model is then used to identify the hazardous areas of the turbine disk-guide disk rotor system. This model is used for finite element modeling based on the geometry and to simulate the mechanical properties of the metallic material using an improved Chaboche cyclic constitutive model, yielding a stress-strain-displacement field. Simultaneously, under fretting and fatigue coupled loads, a multi-parameter damage-driven cyclic plastic work parameter is introduced to automatically identify the hazardous areas of the turbine disk-guide disk rotor system. The hazardous areas are located at the connection interface between the turbine disk and the guide disk. The metallic materials in the hazardous area are analyzed to determine surface optimization processes to optimize the hazardous area; the surface optimization processes include: water jet strengthening process; Based on the optimized hazardous area, a life prediction model is constructed; and the fretting fatigue life of the turbine disk-guide disk rotor system is determined based on the life prediction model; the life prediction model is used to perform finite element modeling of the hazardous area based on the crystal plastic finite element method, and to determine the fretting fatigue life of the turbine disk-guide disk rotor system using the improved crystal plastic constitutive model. The determination of the fretting fatigue life of the turbine disk-guide disk rotor system based on the life prediction model specifically includes: The parameters of the crystal plastic constitutive model were calibrated based on mechanical test data to obtain an improved crystal plastic constitutive model. The finite element method of crystal plasticity is used to model the hazardous area; and the cumulative energy dissipation of the hazardous area is determined by the improved crystal plasticity constitutive model. The fretting fatigue life of the turbine disk-guide disk rotor system is determined based on the cumulative energy dissipation. The determination of the fretting fatigue life of the turbine disk-guide disk rotor system based on cumulative energy dissipation specifically includes: Using the formula Determine the fretting fatigue life of a turbine disk- bucket rotor system; wherein is a cumulative energy dissipation threshold value, is a hardening parameter, is a fatigue crack initiation life, is a cumulative energy dissipation value per cycle; The multi-parameter damage-driven cyclic plastic work parameter combines parameters coupled with fatigue damage and fretting damage, with fatigue cyclic plastic work as the main component, corrected by the fretting influence coefficient. The calculation formula is as follows: ； in, For cyclic plastic work under pure fatigue conditions, The fretting damage index is composed of contact normal pressure and slip amplitude. The material coefficient is used to characterize the fatigue amplification effect of fretting.

2. The method for predicting the fretting fatigue life of a turbine disk-guide disk rotor system according to claim 1, characterized in that, The construction of the hazardous area identification model based on the geometric structure and metallic material specifically includes: Based on the metallic material of the turbine disk-guide disk rotor system, the corresponding mechanical test data are determined; the mechanical test data includes: high-temperature tensile data and low-cycle fatigue data; The parameters of the Chaboche cycle constitutive model were calibrated based on the mechanical test data to obtain an improved Chaboche cycle constitutive model. Based on the aforementioned geometry and the improved Chaboche cycle constitutive model, a hazardous area identification model is constructed.

3. The method for predicting the fretting fatigue life of a turbine disk-guide disk rotor system according to claim 1, characterized in that, The analysis of the metallic materials in the hazardous area and the determination of surface optimization processes to optimize the hazardous area specifically include: Extract the coordinates of the surface contour points and the normal vector of the hazardous area, and determine the contour of the hazardous area; Based on the outline of the danger zone, an optimized trajectory for the danger zone is planned; Based on the optimized trajectory, the grain morphology of the surface of the hazardous area is optimized using a surface optimization process to obtain the optimized hazardous area.

4. The method for predicting the fretting fatigue life of a turbine disk-guide disk rotor system according to claim 1, characterized in that, The method utilizes an improved crystal plastic constitutive model to determine the cumulative energy dissipation in the hazardous region, specifically including: Using formula Determine the cumulative energy dissipation in the danger zone ; in, Numbering of slip systems The total number of slip systems, For a moment, For the first Decomposed shear stress on a slip system For the first Shear strain rate on a slip system.

5. A fretting fatigue life prediction system for a turbine disk-guide disk rotor system, used to implement the fretting fatigue life prediction method for a turbine disk-guide disk rotor system as described in any one of claims 1-4, characterized in that, The fretting fatigue life prediction system for turbine disk-guide disk rotor systems includes: A geometry and metal material acquisition module is used to acquire the geometry and metal material of the turbine disk-guide disk rotor system. A hazardous area identification model construction module is used to construct a hazardous area identification model based on the geometric structure and metallic material; and to identify hazardous areas of the turbine disk-guide disk rotor system based on the hazardous area identification model; the hazardous area identification model is used to perform finite element modeling based on the geometric structure, and to simulate the mechanical properties of the metallic material using an improved Chaboche cyclic constitutive model to obtain the stress-strain-displacement field; simultaneously, under the action of fretting and fatigue coupled loads, a multi-parameter damage-driven cyclic plastic work parameter is introduced to automatically identify the hazardous areas of the turbine disk-guide disk rotor system; the hazardous areas are located at the connection interface between the turbine disk and the guide disk; The hazardous area optimization module is used to analyze the metal materials in hazardous areas and determine surface optimization processes to optimize the hazardous areas; the surface optimization processes include: water jet strengthening process; The life prediction model construction module is used to construct a life prediction model based on the optimized hazardous area; and to determine the fretting fatigue life of the turbine disk-guide disk rotor system based on the life prediction model; the life prediction model is used to perform finite element modeling of the hazardous area based on the crystal plastic finite element method, and to determine the fretting fatigue life of the turbine disk-guide disk rotor system using the improved crystal plastic constitutive model.

6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the fretting fatigue life prediction method for a turbine disk-guide disk rotor system according to any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the fretting fatigue life prediction method for turbine disk-guide disk rotor systems as described in any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the fretting fatigue life prediction method for turbine disk-guide disk rotor systems as described in any one of claims 1-4.

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