Modeling and life prediction method of hot corrosion fatigue based on crystal plasticity finite element

Abstract

This invention relates to the field of microscopic modeling and analysis methods for mechanical structure strength, specifically a modeling and life prediction method for hot corrosion fatigue based on crystal plasticity finite element method. The method includes: obtaining grain size information based on EBSD characterization analysis of the material; importing the CTF format result file into Dream.3D software to obtain grain orientation information; generating RVEs (Representative Volume Elements) using Dream.3D software, including four types of feature information: _nodes.inp (nodes), elems.inp (elements), elset.inp (sets), and sections.inp (sections), as well as a text file containing Euler angle information; modeling hot corrosion pits, surface oxide layers, and sulfide layers using Python scripts based on the material characteristics affected by hot corrosion; assigning boundary conditions to the model in ABAQUS finite element analysis software; setting its constitutive relation using the crystal plasticity umat subroutine; and submitting the calculation to complete the simulation process. This invention enables a modeling and low-cycle fatigue life prediction method that considers the effects of hot corrosion within the framework of the crystal plasticity method.

Description

Technical Field

[0001] This invention relates to the field of microscopic modeling and analysis methods for mechanical structure strength, specifically to a modeling and life prediction method based on crystal plastic finite element thermal corrosion fatigue. Background Technology

[0002] Aero-engine turbine components typically endure significant mechanical and thermal loads during service. This complex loading environment can lead to fatigue failure of the rotor components. Furthermore, for ocean-going aircraft, which must withstand complex loads and also face environmental corrosion from the marine atmosphere, combined with the high-temperature environment of the turbine rotor, hot corrosion damage is highly likely. Related research shows that salt-coated hot corrosion can cause corrosion pits and oxide and sulfide layers on the material surface. These characteristics provide an effective starting point for microstructural modeling and analysis. Therefore, from a microscopic perspective, using crystal plasticity finite element methods to consider the effects of hot corrosion fatigue shows promising application potential.

[0003] For existing finite element simulation methods for crystal plasticity, the constitutive models are divided into two types: those based on rate-related power-law functions and those based on dislocation density as internal variables. Based on these two models, many studies have been conducted considering factors such as materials and temperature. However, there is not much research on modeling crystal plasticity RVE itself. At the same time, the massive amount of data on microstructures is difficult to process efficiently through manual processing and statistics alone. Therefore, computer finite element technology is combined with Python scripts to realize the programmability and convenience of the calculation process. Summary of the Invention

[0004] The purpose of this invention is to provide a modeling and life prediction method for thermal corrosion fatigue based on crystal plasticity finite element method, which is driven by relevant computer software and programming languages ​​to make modeling and calculation more efficient.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] Step 1: Prepare a surface-polished polycrystalline metal sample, and use electron backscatter diffraction (EBSD) characterization technology to scan the polished sample and copy the scan results.

[0007] Step 2: Import the CPR or CTF file format from the scan results into AZtec Crystal software. This software is mainly used for crystal structure analysis and can be used to export statistical data on size.

[0008] Step 3: Import the CTF file into the Dream.3D software. Use the software's built-in filter to process the CTF file and export a TXT file containing orientation distribution information.

[0009] Step 4: Import the size and orientation distribution information into the Dream.3D software. Using the software's built-in filter, these two pieces of information can be used to generate cube representative elements with custom sizes and meshes. The exported files are: .inp, _nodes.inp (nodes), _elems.inp (elements), _elset.inp (sets), _sects.inp (sections), and an orientation information text file. Combine the file contents and open them as .inp files in the ABAQUS finite element software.

[0010] Step 5: In the ABAQUS finite element software, batch material assignment is performed on the imported model, analysis steps are set, boundary conditions are set, crystal plasticity umat is imported, and the calculation is submitted. The total strain energy dissipation density of the material is used as the fatigue index. Through the experimental results of low-cycle fatigue, the fatigue life of the thermal corrosion effect is predicted.

[0011] Furthermore, in step 4, the exported file needs to be reconstructed. The specific steps are as follows: For the _nodes.inp (node) file, its content contains node coordinates. Through a Python script, its function is to modify the node coordinates of the upper surface and near-upper surface of the cube, so that the node coordinates of the central area of ​​the upper surface are linearly reduced, thereby realizing the modeling of the pit appearance.

[0012] Furthermore, in step 4, the exported files need to be reconstructed. The specific steps are as follows: For the _elset.inp (set) and _sects.inp (section) files, their contents contain element sets. Through a Python script, the function is to delete the elements near the upper surface of the model from the original set and form a new set in sequence. The set number content corresponding to the orientation information text file is copied sequentially to the end of the text. This operation can separate multiple layers according to the required thickness while keeping the orientation information of the model surface elements unchanged. In the subsequent material assignment step, its Young's modulus is modified to realize the modeling of the oxide layer and the sulfide layer.

[0013] Furthermore, in step 5, a lifespan prediction method is used to predict lifespan. The specific lifespan prediction model is as follows:

[0014] .

[0015] In the formula, The total strain energy dissipation density during material fatigue fracture; This represents the number of fatigue cycles simulated during finite element fatigue simulation. This represents the strain energy dissipation density at the end of the finite element simulation of fatigue. For low-cycle fatigue material life; This represents the increase in strain energy dissipation density during the final fatigue cycle in a finite element simulation.

[0016] Using experimental life data from low-cycle fatigue and simulation results without the aforementioned reconstruction, the following can be calculated: Using a reference value, and then substituting that reference value back into the formula, the reconstructed model, which considers the effects of corrosion pits and oxidation / sulfidation layers, can then be used to calculate... This is how lifespan is predicted.

[0017] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0018] This method offers a microscopic solution to fatigue problems that are difficult to address from a macroscopic perspective. Since the formation of corrosion pits and corrosion layers are micrometer-scale phenomena, they are difficult to observe and assess from a macroscopic standpoint. Most general thermal corrosion fatigue models do not consider the physical mechanisms; this method, however, can practically model and reconstruct these phenomena, making it significant. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0020] Figure 1 This is a flowchart of the modeling and life prediction method for thermal corrosion fatigue based on crystal plastic finite element method of the present invention;

[0021] Figure 2 This is a crystal orientation diagram of an embodiment of the present invention;

[0022] Figure 3 This is a size statistics example of an embodiment of the present invention;

[0023] Figure 4 This is an example diagram of a representative volume unit generated by dream.3d in an embodiment of the present invention;

[0024] Figure 5 This is a model example diagram considering corrosion pits and oxidation and sulfide layers in an embodiment of the present invention;

[0025] Figure 6 This is an example of a stress cloud diagram considering corrosion pits and oxide / sulfidation layers in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see Figures 1-6 The present invention provides the following technical solution:

[0028] Example 1: This method discloses a modeling and lifetime prediction method for thermal corrosion fatigue based on crystal plastic finite element method. The implementation process is as follows: Figure 1 As shown. Includes the following steps:

[0029] (1) For a certain alloy material, samples are taken, polished, and characterized using electron backscatter diffraction (EBSD) technology. The characterization results are then analyzed and processed. The CPR or CTF file format is imported into AZtec Crystal software to observe its grain morphology, orientation distribution, and export grain size statistics. Specific examples are as follows: Figure 2 , Figure 3 As shown.

[0030] (2) Import the characterization result CTF file into the Dream.3D software. Use its built-in filter function to export the orientation information text file. Import this text file and the grain size statistics data into the StatsGenerator module of Dream.3D. Then set the number of elements and the element size to export .inp, _nodes.inp, _elems.inp, _elset.inp, _sects.inp, and the orientation information text file. The cross-sectional morphology of the .inp file imported into Abaqus is as follows: Figure 4 As shown.

[0031] (3) Before reconstructing the model to take into account the effects of hot corrosion, the initial material parameters of the original model need to be calibrated. The parameters are calibrated by constitutive parameter calibration methods in the prior art until the material parameters are adjusted so that the stress-strain curve of the test results fits the stress-strain curve of the simulation under the same conditions. The calibration results are also applicable to the hot corrosion model.

[0032] (4) Subsequently, modeling considering the effects of thermal corrosion is performed. First, the coordinate information of the nodes in _nodes.inp is reconstructed. The coordinates of the upper surface and near-surface center region along the z-axis are reduced as a whole using a Python script. The specific formula is as follows:

[0033] .

[0034] in, This represents the depth of the corrosion pit. The radius of the corrosion pit; , ( ) represents the coordinates of the center of the corrosion pit.

[0035] At this point, the corrosion pit eventually takes the shape of a circular depression.

[0036] After modeling the corrosion pit morphology, the element sets and cross-section assignments in the _elset.inp and _sects.inp files are reconstructed. A Python script is used to remove elements near the upper surface of the model from the original sets and sequentially form new sets. The orientation information text file and the corresponding set numbers are copied sequentially to the end of the document. This operation allows for the separation of multiple layers according to the required thickness while maintaining the orientation information of the model surface elements. In subsequent material assignment steps, the Young's modulus is modified to model the oxide and sulfide layers. The reconstructed model appearance is as follows. Figure 5 As shown.

[0037] (5) Import the reconstructed model into Abaqus for calculation, run the material assignment script, assign the initial constitutive parameters and orientation information in the text file to each set respectively, and then modify the Young's modulus values ​​of the two surface unit layers, increasing the upper layer by 15% and decreasing the lower layer by 15% to simulate the mechanical properties of the oxide layer and sulfide layer respectively. Then set the time step, apply fatigue boundary conditions, select the crystal plasticity constitutive model and submit the calculation.

[0038] (7) After the calculation is completed, a stress cloud diagram considering the effects of hot corrosion can be obtained in the visualization module, such as... Figure 6 As shown. The cumulative plastic strain energy dissipation density is derived for lifetime prediction.

[0039] (8) Life prediction is performed using life prediction methods. The specific life prediction formula is as follows:

[0040] .

[0041] In the formula, The total strain energy dissipation density during material fatigue fracture; This represents the number of fatigue cycles simulated during finite element fatigue simulation. This represents the strain energy dissipation density at the end of the finite element simulation of fatigue. For low-cycle fatigue material life; This represents the increase in strain energy dissipation density during the final fatigue cycle in a finite element simulation.

[0042] Using experimental life data from low-cycle fatigue and simulation results without the aforementioned reconstruction, the following can be calculated: Using a reference value, and then substituting that reference value back into the formula, the reconstructed model, which considers the effects of corrosion pits and oxidation / sulfidation layers, can then be used to calculate... This is how lifespan is predicted.

[0043] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for modeling and life prediction of hot corrosion fatigue based on crystal plasticity finite element, characterized in that, Includes the following steps: S100. Electron backscatter diffraction technology is used to scan the material to obtain grain size information and CTF format result files; S200. Import the CTF format result file into Dream.3D software and export the grain orientation information in TXT format. Subsequently, the grain orientation information and grain size information in txt format are imported into the StatsGenerator module of the dream.3d software, and the try.txt orientation information file, a blank inp file, and four feature inp files are exported. The four feature inp files include: _nodes.inp node coordinate file, _elems.inp element file, _elset.inp set file, and _sects.inp section file. S300. Initial material parameters of the original model are calibrated using constitutive parameter calibration methods. S400, Modeling based on the influence of thermal corrosion: The corrosion pit morphology is modeled by reconstructing the node coordinate information in _nodes.inp; the oxide layer and sulfide layer are modeled by reconstructing the element set and set section information in the _elset.inp and _sects.inp files. S500. Import the reconstructed model into the ABAQUS finite element analysis software, assign boundary conditions to the model, and use the UMAT subroutine to set the constitutive relation to obtain a stress cloud diagram considering the influence of hot corrosion. S600. Use lifetime prediction methods to predict the lifetime of the reconstructed model.

2. The method of modeling and life prediction of hot corrosion fatigue based on crystal plasticity finite elements of claim 1, wherein, In step S400, the morphology of the corrosion pits is modeled by reconstructing the coordinate information of the _nodes.inp nodes, specifically including: By modifying the node coordinates in the _nodes.inp file using a Python script, the coordinates of the upper and near-surface center regions along the z-axis are reduced overall, resulting in a circular pit morphology. The specific formula is as follows: ； in, This represents the depth of the corrosion pit. Where is the radius of the corrosion pit, ( , ( ) represents the coordinates of the center of the corrosion pit.

3. The method of modeling and life prediction of hot corrosion fatigue based on crystal plasticity finite elements of claim 1, wherein, Step S400 involves reconstructing the oxide and sulfide layers by assigning elements, sets, and sections to the _elems.inp, _elset.inp, and _sects.inp files. This process specifically includes: The Python script is used to reconstruct the element sets and cross sections in _elset.inp and _sects.inp by assigning information to them. The two layers of elements of arbitrary thickness on the near upper surface are deleted from the sets and new sets are formed in sequence. The orientation information of the sets involved in the deletion in the try.txt file is copied to the end of the document in sequence. Then, the elastic modulus of the material is modified in the ABAQUA material property definition module to realize the modeling of the oxidation and sulfidation layers on the material surface.

4. The method of modeling and life prediction of hot corrosion fatigue based on crystal plasticity finite elements of claim 1, wherein, In step S500, after importing the reconstructed model into the ABAQUS finite element analysis software, the material assignment script is first run to assign the initial constitutive parameters and orientation information from the text file to each set. Then, the Young's modulus values ​​of the two surface elements are modified to simulate the mechanical properties of the oxide layer and the sulfide layer respectively. Next, the time step is set, fatigue boundary conditions are applied, the crystal plastic constitutive model is selected, and the calculation is submitted. Finally, the stress cloud map considering the influence of hot corrosion is obtained in the visualization module.

5. The method of modeling and life prediction of hot corrosion fatigue based on crystal plasticity finite elements of claim 1, wherein, The specific formula for the lifetime prediction method in step S600 is as follows: ； wherein, the total strain energy dissipation density at the fatigue rupture of the material, the number of simulated fatigue cycles at the end of the finite element simulation of the fatigue, the strain energy dissipation density at the end of the finite element simulation of the fatigue; the material life for low cycle fatigue, the increment of the strain energy dissipation density at the final cycle of the finite element simulation of the fatigue; First, using experimental life data from low-cycle fatigue and simulation results without the aforementioned reconstruction, the following calculations were performed. The reference value is then substituted back into the formula. At this point, the reconstructed model is used, taking into account the effects of corrosion pits and oxidation / sulfidation layers, thus yielding the result. This is how lifespan is predicted.

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