Super-model automatic modeling method

Abstract

The invention relates to the technical field of dynamic design and predictive design, and discloses a super-model automatic modeling method, which specifically comprises the following steps of: 1, geometric model cleaning: performing geometric cleaning on an original structure model, eliminating geometric errors in a modeling process, and obtaining a geometric model; according to the automatic super-model modeling method, the number of iterations in the model building process can be remarkably reduced, manpower and computing resources are saved, the convergence and precision of the model are ensured through step-by-step refinement and correlation analysis, the reliability of the model is improved, modal matching is carried out through the modal confidence criterion (MAC), modal analysis of the model is more accurate, and the modeling efficiency is improved. According to the method, the dynamic characteristics of the structure can be accurately predicted, the convergence model can be rapidly determined by setting a clear convergence judgment standard, meaningless calculation is avoided, the working efficiency is improved, the finally determined super model can meet the double indexes of precision and efficiency, and a powerful tool is provided for dynamic design and prediction.

Description

Technical Field

[0001] The invention relates to the technical field of dynamic design and prediction design, and in particular to a super model automatic modeling method. Background Art

[0002] In the structural dynamics design of aircraft engines, with the rapid development of computer science and technology, finite element modeling and simulation are used to analyze the dynamic characteristics and predict the response of parts and complete machines, gradually replacing the actual structural test, and even realizing the electronic design of the complete machine. Before conducting simulation design, it is necessary to first improve and ensure that the model can reflect the real dynamic characteristics of the structure. The application of super models can ensure the accuracy of the model. The super model is a very detailed 3D finite element model with sufficient accuracy to reflect the physical characteristics of the structure. It not only accurately describes the geometric characteristics of the structure, but also accurately reflects its dynamic characteristics (natural frequency, mode vibration shape).

[0003] The hyper-model modeling process is relatively complicated and requires several iterations. First, structural finite element models of different sizes must be established and the current model must be correlated with the previous generation model for analysis. The hyper-model mesh size can only be determined when the model-related parameters converge with the mesh size. This process will consume a lot of manpower and computing costs.

[0004] To this end, we propose a hyper-model automatic modeling method. Summary of the invention

[0005] The present invention mainly solves the technical problems existing in the above-mentioned prior art and provides a super model automatic modeling method.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a super model automatic modeling method, which specifically comprises the following steps:

[0007] Step 1: Cleaning the geometric model: Clean the original model of the structure to eliminate geometric errors in the modeling process and ensure that the structure can be meshed.

[0008] Step 2: Initial modeling calculation: Given a coarse initial grid, perform grid division and initial modal calculation;

[0009] Step 3: Gradually refine the calculation: perform correlation analysis between the current model and the previous model;

[0010] Step 4: Model convergence analysis: Check the convergence of the modal calculation results, and stop the calculation if converged;

[0011] Step 5: Determine the super model: Select the appropriate grid size model as the super model based on the super model accuracy index.

[0012] Preferably, the specific steps of cleaning the geometric model in the first step are: using the commands of deleting faces and area offsets in the modeling software to clean the geometric model, eliminating errors generated in the geometric modeling process, compatibility issues between the geometric modeling software and the finite element software, ensuring that the geometric model is consistent with the design expectations and can be meshed.

[0013] Preferably, the specific steps of the initial modeling calculation in the second step are: given a coarser grid size, finite element grid division is performed with second-order tetrahedral 187 units, and initial modal calculations are performed under free-free boundary conditions within the frequency range of interest.

[0014] Preferably, the specific steps of gradually refining the calculation in the third step are: gradually refining the grid size, and performing correlation analysis on the previous model and the current model to determine the modal pairs of the same vibration mode, analyzing the curve relationship between the frequency difference of the modal pair and the change of the degree of freedom, using the correlation analysis theory and method to perform modal pairing on two different models, and determining the corresponding modes with consistent vibration modes in the two models. The correlation analysis can be quantitatively analyzed by the modal assurance criterion (MAC), and the calculation formula is as follows:

[0015]

[0016] Among them, {ψ A} i , {ψ B} j They represent the i-th order modal vibration data of model A and the j-th order modal vibration data of model B respectively. When models A and B are the same model, the result is autocorrelation. The modal assurance criterion (MAC) takes values ​​from 0 to 1. If the MAC value is close to 1, it means that the correlation between the two compared modal vibration modes is very high, and the two have consistent correlation, and they are a possible modal pair; if the MAC value is very low and approaches 0, it means that the correlation between the two compared modal vibration modes is very poor, that is, the two compared models are very different, there is no consistent correlation, and they may not be a modal pair. When all MAC values ​​are arranged in a matrix, if the correlation between the two models is good, then the MAC value on the diagonal of the matrix should be relatively large. It is generally believed that when the MAC value is greater than 0.8, the two-order modes may be a modal pair.

[0017] Preferably, the specific steps of the model convergence analysis in the fourth step are: check the convergence of the modal calculation results according to the convergence index of the model, if it does not converge, continue to refine, if it converges, stop the calculation, confirm the converged model, when two (or more) consecutive models meet the accuracy standard and the adjacent frequency difference meets the convergence standard, it is considered to be converged, and the model with the smallest grid size until the convergence is determined to be the converged model, the convergence judgment index is: within the frequency range of interest, the average frequency difference threshold is less than 0.5%, and the maximum frequency difference threshold is less than 1%, wherein the average frequency difference threshold is the absolute value of the difference between adjacent average frequency differences, the maximum frequency difference threshold is the absolute value of the difference between adjacent maximum frequency differences, the average frequency difference and the maximum frequency difference are respectively the average and maximum values ​​of the frequency differences of each mode of adjacent models, when the average frequency difference and the maximum frequency difference meet the convergence judgment standard, it is judged to be converged, and the model with the smallest grid size is taken as the converged model. In order to quantitatively describe the convergence of the model, the forward difference quotient is used instead of the derivative as the slope of the modal frequency difference curve, and the calculation formula is as follows:

[0018]

[0019] Where Δf i,j represents the modal frequency difference percentage of the jth mode of the ith model relative to the most refined model, x i is the degrees of freedom of the ith model, Δf' i,j It represents the slope of the modal-frequency difference curve, and the percentage of change in the modal-frequency difference for every increase of 100,000 degrees of freedom.

[0020] Preferably, the specific steps for determining the super model in the fifth step are: determining the appropriate grid size according to the super model accuracy index (modal frequency difference relative to the most refined model) and the computational efficiency index (measured by the slope of the curve), re-dividing the grid, and performing correlation analysis with the convergence model; if the model and the convergence model satisfy the average frequency difference of less than 1% and the maximum frequency difference of less than 3%, the model is considered to be a super model.

[0021] The present invention provides a super model automatic modeling method. It has the following beneficial effects:

[0022] 1. A super model automatic modeling method. The super model automatic modeling method of the present invention can significantly reduce the number of iterations in the model building process, save manpower and computing resources, ensure the convergence and accuracy of the model through gradual refinement and correlation analysis, improve the reliability of the model, and use the modal confidence criterion (MAC) for modal pairing to make the modal analysis of the model more accurate, which is helpful to accurately predict the dynamic characteristics of the structure. By setting clear convergence judgment criteria, the convergence model can be quickly determined, unnecessary calculations can be avoided, and work efficiency can be improved. The super model finally determined can meet the dual indicators of accuracy and efficiency, and provides a powerful tool for dynamic design and prediction.

[0023] 2. This super model automatic modeling method ensures the convergence and accuracy of the model and improves the reliability of the model through gradual refinement and correlation analysis.

[0024] 3. This super-model automatic modeling method uses the modal assurance criterion (MAC) for modal pairing, making the modal analysis of the model more accurate and helping to accurately predict the dynamic characteristics of the structure.

[0025] 4. This super model automatic modeling method can quickly determine the convergence model by setting clear convergence judgment criteria, avoiding unnecessary calculations and improving work efficiency.

[0026] 5. This is a super model automatic modeling method. The final determined super model can meet the dual indicators of accuracy and efficiency, providing a powerful tool for dynamic design and prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flow chart of the method of the present invention;

[0028] Figure 2 It is a schematic diagram of the automatic modeling process of the super model of the present invention;

[0029] Figure 3 It is a schematic diagram of the geometric model of the turbine rear casing of the present invention;

[0030] Figure 4 It is a schematic diagram showing the variation of the number of degrees of freedom of the finite element model according to the present invention with the mesh size;

[0031] Figure 5 It is a schematic diagram showing the variation of the average frequency difference of the finite element model of the present invention with the number of degrees of freedom of the model;

[0032] Figure 6 It is a schematic diagram of the turbine rear casing super model of the present invention. DETAILED DESCRIPTION

[0033] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings described below are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0034] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0035] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0036] In the description of the embodiments of the present invention, it should be noted that the terms "center", "upper", "lower", "inner", "outer", "side", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the invented product is usually placed when in use, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0037] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Embodiment 1: A super model automatic modeling method, such as Figure 1As shown, the specific steps include: Step 1: Geometric model cleaning: geometrically clean the original model of the structure, eliminate the geometric errors in the modeling process, and ensure that the structure can be meshed; Step 2: Initial modeling calculation: given a coarse initial mesh, meshing and initial modal calculation; Step 3: Gradually refine the calculation: perform correlation analysis between the current model and the previous model; Step 4: Model convergence analysis: check the convergence of the modal calculation results, and stop the calculation if converged; Step 5: Determine the super model: select the appropriate mesh size model as the super model according to the super model accuracy index. The specific steps of the geometric model cleaning in the first step are: use the command of deleting faces and regional offsets in the modeling software to clean the geometric model, eliminate the errors generated in the geometric modeling process, the compatibility issues between the geometric modeling software and the finite element software, ensure that the geometric model is consistent with the design expectations, and meshing can be performed. The specific steps of the initial modeling calculation in the second step are: given a coarse mesh size, finite element meshing is performed with the second-order tetrahedron 187 unit, and the initial modal calculation under the free-free boundary condition is performed within the frequency range of interest. The super model automatic modeling method of the present invention can significantly reduce the number of iterations in the model building process, save manpower and computing resources, ensure the convergence and accuracy of the model through gradual refinement and correlation analysis, improve the reliability of the model, use the modal confidence criterion (MAC) for modal pairing, make the modal analysis of the model more accurate, help to accurately predict the dynamic characteristics of the structure, and by setting clear convergence judgment criteria, the convergence model can be quickly determined, avoiding unnecessary calculations and improving work efficiency. The super model finally determined can meet the dual indicators of accuracy and efficiency, providing a powerful tool for dynamic design and prediction.

[0040] Embodiment 2: Based on Embodiment 1, Figure 2 As shown in the figure, the specific steps of the step-by-step refinement calculation in the third step are: gradually refine the grid size, and perform correlation analysis on the previous model and the current model to determine the modal pairs with the same vibration mode, analyze the curve relationship between the frequency difference of the modal pair and the change of the degree of freedom, use the correlation analysis theory and method to perform modal pairing on two different models, and determine the corresponding modes with the same vibration mode in the two models. The correlation analysis can be quantitatively analyzed by the Modal Assurance Criterion (MAC), and the calculation formula is as follows:

[0041]

[0042] Among them, {ψ A} i , {ψ B} jThey represent the i-th order modal vibration data of model A and the j-th order modal vibration data of model B respectively. When models A and B are the same model, the result is autocorrelation. The modal assurance criterion (MAC) takes values ​​from 0 to 1. If the MAC value is close to 1, it means that the correlation between the two compared modal vibration modes is very high, and the two have consistent correlation, and they are a possible modal pair; if the MAC value is very low and approaches 0, it means that the correlation between the two compared modal vibration modes is very poor, that is, the two compared models are very different, there is no consistent correlation, and they may not be a modal pair. When all MAC values ​​are arranged in a matrix, if the correlation between the two models is good, then the MAC value on the diagonal of the matrix should be relatively large. It is generally believed that when the MAC value is greater than 0.8, the two-order modes may be a modal pair. The specific steps of the model convergence analysis in the fourth step are: check the convergence of the modal calculation results according to the convergence index of the model. If it does not converge, continue to refine. If it converges, stop the calculation and confirm the converged model. When two (or more) consecutive models meet the accuracy standard and the adjacent frequency difference meets the convergence standard, it is considered to be converged. The model with the smallest grid size until the convergence is determined is selected as the converged model. The convergence judgment index is: within the frequency range of interest, the average frequency difference threshold is less than 0.5% and the maximum frequency difference threshold is less than 1%, where the average frequency difference threshold is the absolute value of the difference between adjacent average frequency differences, and the maximum frequency difference threshold is the absolute value of the difference between adjacent maximum frequency differences. The average frequency difference and the maximum frequency difference are the average and maximum values ​​of the frequency differences of each mode of adjacent models, respectively. When the average frequency difference and the maximum frequency difference meet the convergence judgment standard, it is judged to be converged. The model with the smallest grid size is taken as the converged model. In order to quantitatively describe the convergence of the model, the forward difference quotient is used instead of the derivative as the slope of the modal frequency difference curve. The calculation formula is as follows:

[0043]

[0044] Where Δf i,j represents the modal frequency difference percentage of the jth mode of the ith model relative to the most refined model, x i is the degree of freedom of the ith model, Δf' i,j It represents the slope of the modal-to-frequency difference curve, and represents the percentage change of the modal-to-frequency difference for every additional 100,000 degrees of freedom. The specific steps to determine the super model in the fifth step are: determine the appropriate grid size based on the super model accuracy index (modal-to-frequency difference relative to the most refined model) and the computational efficiency index (measured by the slope of the curve), re-divide the grid, and perform correlation analysis with the converged model. If the model and the converged model meet the average frequency difference of less than 1% and the maximum frequency difference of less than 3%, the model is considered to be a super model. Through gradual refinement and correlation analysis, the convergence and accuracy of the model are ensured, and the reliability of the model is improved. The modal pairing using the modal assurance criterion (MAC) makes the modal analysis of the model more accurate, which helps to accurately predict the dynamic characteristics of the structure.

[0045] Embodiment 3: Based on Embodiment 1 and Embodiment 2, Figure 3 As shown in Figure 2, the geometric model of the turbine rear casing is as follows: Figure 3 As shown. According to the super model modeling method, the casing was divided into meshes using second-order tetrahedral units of the following sizes: 20mm, 9.2mm, 4.1mm, 3mm, 2.6mm, and 2.2mm. Six finite element models of different sizes were established and free modal analysis was performed. The information of each entity finite element model is shown in Table 1, and the frequency difference information between each model is shown in Table 2. Figure 4 It can be seen that the number of degrees of freedom of the model increases as the mesh size decreases, and when the mesh size is small to a certain extent, the number of degrees of freedom increases rapidly. Figure 5 The curve of the average frequency difference changing with the number of model degrees of freedom is shown. It can be seen that as the number of model degrees of freedom increases, the average frequency difference tends to decrease and converges at the end of the calculation.

[0046] It can be seen from Table 2 that the average frequency difference convergence threshold of the last finite element model is less than 0.1%, the maximum frequency difference convergence threshold of the last finite element model is less than 0.5%, and the average frequency difference of the last finite element model is less than 0.5%, and the maximum frequency difference is less than 1%. At the same time, the set convergence standard and accuracy standard are met, so the model with a mesh size of 2.2mm is taken as the converged model.

[0047] Table 1 Finite element model information and modal calculation time of different mesh sizes

[0048]

[0049] Table 2 Frequency difference information between finite element models with different mesh sizes

[0050]

[0051] Taking the convergence model as the standard, according to the method of super model confirmation, a finite element model with a mesh size of 4 mm is obtained. Correlation analysis with the convergence model shows that the model meets the accuracy requirements, and the model is considered to be a super model. Figure 6 ,The correlation information between the super model and the convergent model is shown in Table 3.

[0052] Table 3 Turbine rear casing super model information

[0053]

[0054] By setting clear convergence criteria, the convergence model can be quickly determined, unnecessary calculations can be avoided, and work efficiency can be improved. The final super model can meet the dual indicators of accuracy and efficiency, providing a powerful tool for dynamic design and prediction.

[0055] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A super model automatic modeling method, characterized in that: The specific steps include: Step 1: Cleaning the geometric model: Clean the original model of the structure to eliminate geometric errors in the modeling process and ensure that the structure can be meshed. Step 2: Initial modeling calculation: Given a coarse initial grid, perform grid division and initial modal calculation; Step 3: Gradually refine the calculation: perform correlation analysis between the current model and the previous model; Step 4: Model convergence analysis: Check the convergence of the modal calculation results, and stop the calculation if converged; Step 5: Determine the super model: Select the appropriate grid size model as the super model based on the super model accuracy index.

2. The super model automatic modeling method according to claim 1, characterized in that: The specific steps of cleaning the geometric model in the first step are: using the commands of deleting faces and area offsets in the modeling software to clean the geometric model, eliminate the errors generated in the geometric modeling process, and the compatibility issues between the geometric modeling software and the finite element software, ensure that the geometric model is consistent with the design expectations, and can be meshed.

3. The super model automatic modeling method according to claim 1, characterized in that: The specific steps of the initial modeling calculation in the second step are: given a coarser grid size, finite element meshing is performed with second-order tetrahedral 187 units, and initial modal calculations are performed under free-free boundary conditions within the frequency range of interest.

4. The super model automatic modeling method according to claim 1, characterized in that: The specific steps of the stepwise refinement calculation in the third step are: gradually refine the grid size, and perform correlation analysis on the previous model and the current model to determine the modal pairs with the same vibration mode, analyze the curve relationship between the frequency difference of the modal pairs and the change of the degree of freedom, use the correlation analysis theory and method to perform modal pairing on two different models, and determine the corresponding modes with the same vibration mode in the two models. The correlation analysis can be quantitatively analyzed by the Modal Assurance Criterion (MAC), and the calculation formula is as follows: Among them, {ψ A } i , {ψ B } j They represent the i-th order modal vibration data of model A and the j-th order modal vibration data of model B respectively. When models A and B are the same model, the result is autocorrelation. The modal assurance criterion (MAC) takes values ​​from 0 to 1. If the MAC value is close to 1, it means that the correlation between the two compared modal vibration modes is very high, and the two have consistent correlation, and they are a possible modal pair; if the MAC value is very low and approaches 0, it means that the correlation between the two compared modal vibration modes is very poor, that is, the two compared models are very different, there is no consistent correlation, and they may not be a modal pair. When all MAC values ​​are arranged in a matrix, if the correlation between the two models is good, then the MAC value on the diagonal of the matrix should be relatively large. It is generally believed that when the MAC value is greater than 0.8, the two-order modes may be a modal pair.

5. The super model automatic modeling method according to claim 1, characterized in that: The specific steps of the model convergence analysis in the fourth step are: check the convergence of the modal calculation results according to the convergence index of the model, if it does not converge, continue to refine, if it converges, stop the calculation, confirm the converged model, when two (or more) consecutive models meet the accuracy standard and the adjacent frequency difference meets the convergence standard, it is considered to be converged, and the model with the smallest grid size until the convergence is determined to be the converged model, the convergence judgment index is: within the frequency range of interest, the average frequency difference threshold is less than 0.5%, and the maximum frequency difference threshold is less than 1%, wherein the average frequency difference threshold is the absolute value of the difference between adjacent average frequency differences, the maximum frequency difference threshold is the absolute value of the difference between adjacent maximum frequency differences, the average frequency difference and the maximum frequency difference are the average and maximum values ​​of the frequency differences of each mode of adjacent models, respectively, when the average frequency difference and the maximum frequency difference meet the convergence judgment standard, it is judged to be converged, and the model with the smallest grid size is taken as the converged model. In order to quantitatively describe the convergence of the model, the forward difference quotient is used instead of the derivative as the slope of the modal frequency difference curve, and the calculation formula is as follows: Where Δf i,j represents the modal frequency difference percentage of the jth mode of the ith model relative to the most refined model, x i is the degrees of freedom of the ith model, Δf' i,j It represents the slope of the modal-frequency difference curve, and the percentage of change in the modal-frequency difference for every increase of 100,000 degrees of freedom.

6. The super model automatic modeling method according to claim 1, characterized in that: The specific steps for determining the super model in the fifth step are: determine the appropriate grid size according to the super model accuracy index (modal frequency difference relative to the most refined model) and the computational efficiency index (measured by the slope of the curve), re-divide the grid, and perform correlation analysis with the convergence model. If the model and the convergence model satisfy the average frequency difference of less than 1% and the maximum frequency difference of less than 3%, the model is considered to be a super model.

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