A station structure model correction analysis method and system

Abstract

The present invention belongs to the field of finite element analysis, and specifically provides a station structure model correction analysis method and system, including S1, establishing a finite element model for the station structure, and specifying the units of interest in the finite element model as design variables; S2, selecting units of interest from each part, and treating the units with the same material as super units, each super unit contributes a design variable, and the material properties of each design variable are respectively assigned an elastic modulus; S3, performing sensitivity analysis on each of the super units to obtain the Spearman rank correlation coefficient; S4, comparing the absolute value of the Spearman rank correlation coefficient of each super unit with a preset value to delete unqualified design variables; S5, performing optimization analysis on the screened model to obtain an optimized and corrected model. By secondary development of large-scale finite element analysis software, parametric modeling and the use of optimization modules to achieve model correction, the data analysis accuracy of subsequent models is improved.

Description

Technical Field

[0001] The present invention relates to the field of finite element analysis, and more specifically, to a station structure model correction analysis method and system. Background Art

[0002] Structural health monitoring is widely used in aerospace, civil engineering and bridge safety. However, when health monitoring is actually applied to the life assessment of large station structures, the following problems exist, making the assessment results inaccurate:

[0003] (1) Incomplete measurement information. Due to insufficient frequency and modal information obtained from the measurements, the model modification results are not unique. For example, when modifying the unit stiffness, if the number of measured frequencies and modes is far less than the number of units, the stiffness modification parameters cannot be uniquely determined. The resulting model is not unique, and therefore the structural life cannot be accurately predicted.

[0004] (2) Lack of high-order modes. The high-order modes of the structure cannot be excited under the environment and excitation, so a reliable structural health system must use the low-order modal information of the structure for damage diagnosis. However, the relationship between stress and high-order modes is large, so the stress estimate may be inaccurate, and it is difficult to obtain accurate results when using stress to estimate the life of the structure.

[0005] (3) The damage index is not sensitive enough and cannot effectively distinguish whether the structure is abnormal.

[0006] Therefore, when analyzing the life of a structure, traditional modeling has major flaws, and the use of defective model analysis can no longer meet actual engineering needs. Summary of the invention

[0007] The present invention aims at the technical problems existing in the prior art and provides a station structure model correction analysis method and system.

[0008] The present invention provides a station structure model correction analysis method, comprising the following steps:

[0009] S1, establishing a finite element model for the station structure, and specifying the units of interest in the finite element model as design variables;

[0010] S2, select the units of interest from each part, and treat the units with the same material as super units. Each super unit contributes a design variable, and the material properties of each design variable are assigned an elastic modulus.

[0011] S3, performing sensitivity analysis on each of the super units to obtain the Spearman rank correlation coefficient;

[0012] S4, comparing the absolute value of the Spearman rank correlation coefficient of each super unit with the preset value to delete unqualified design variables;

[0013] S5, performing optimization analysis on the screened model to obtain an optimized and revised model.

[0014] Optionally, the design variables include roof truss upper chord members, roof truss lower chord members, roof truss web members, sandwich truss upper chord, sandwich truss lower chord, sandwich truss web members, waiting layer beams, platform layer, track supporting layer, roof columns, sandwich columns, columns above the track supporting layer and columns below the track supporting layer.

[0015] Optionally, S3 specifically includes: the random input parameter is taken as the elastic modulus of each super unit, and the random output parameter is taken as follows according to the objective function J:

[0016]

[0017] Among them, f i is the frequency corresponding to each vibration mode of the state variable, f i * are the frequency values ​​of each order measured by the experiment, and the state variables are functions of the constrained design variables.

[0018] Optionally, S4 specifically includes: taking a preset value of 0.025 as a boundary, if the absolute value of the Spearman rank correlation coefficient is less than 0.025, it indicates that the design variable is unqualified and is deleted.

[0019] Optionally, S5 specifically includes: limiting the variation range of the design variable as follows:

[0020] 0.5E i ≤p i ≤1.5E i E i is the elastic modulus corresponding to the design variable, P i is the parameter constraint value of the corresponding design variable. Optionally, S5 specifically includes: limiting the variation range of the state variable as follows:

[0021] 0.7f i * ≤f i ≤1.3f i *

[0022] Among them, f i is the frequency corresponding to each vibration mode of the state variable, f i * are the frequency values ​​of each order measured by the experiment, and the state variables are functions of the constrained design variables.

[0023] Optionally, S5 specifically includes: first optimizing and solving the screened model using a first-order algorithm, and then verifying it using a zero-order algorithm.

[0024] The present invention also provides a station structure model correction analysis method system, comprising:

[0025] A finite element analysis module, for establishing a finite element model of the station structure and specifying units of interest in the finite element model as design variables;

[0026] Secondary development module, used to select units of interest from each part, and treat the units with the same material as super units. Each super unit contributes a design variable, and the material properties of each design variable are assigned an elastic modulus.

[0027] The data correction module is used to perform sensitivity analysis on each of the super units to obtain the Spearman rank correlation coefficient, compare the absolute value of the Spearman rank correlation coefficient of each super unit with a preset value to delete unqualified design variables, and perform optimization analysis on the screened model to obtain an optimized and corrected model.

[0028] Beneficial effects: The present invention provides a station structure model correction analysis method and system, including S1, establishing a finite element model for the station structure, specifying the units of interest in the finite element model as design variables; S2, selecting units of interest from each part, and treating the units with the same material as super units, each super unit contributes a design variable, and the material properties of each design variable are respectively assigned an elastic modulus; S3, performing sensitivity analysis on each of the super units to obtain the Spearman rank correlation coefficient; S4, comparing the absolute value of the Spearman rank correlation coefficient of each super unit with a preset value to delete unqualified design variables; S5, performing optimization analysis on the screened model to obtain an optimized and corrected model. By secondary development of large-scale finite element analysis software, parametric modeling and the use of optimization modules to achieve model correction, the data analysis accuracy of subsequent models is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flow chart of a station structure model correction analysis method provided by the present invention;

[0030] Figure 2 A curve diagram showing the variation of the objective function of the first-order method of the station structure model correction analysis method provided by the present invention with the number of iterations;

[0031] Figure 3 A curve diagram showing the change of the objective function of the zero-order method of the station structure model correction analysis method provided by the present invention with the number of iterations;

[0032] Figure 4 The secondary optimization of the station structure model correction analysis method provided by the present invention can obtain the change rate of each parameter after optimization and correction. DETAILED DESCRIPTION

[0033] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0034] Figure 1 A flow chart of a station structure model correction analysis method provided by the present invention, such as Figure 1 As shown, the method includes:

[0035] S1, establishing a finite element model for the station structure, and specifying the units of interest in the finite element model as design variables;

[0036] S2, select the units of interest from each part, and treat the units with the same material as super units. Each super unit contributes a design variable, and the material properties of each design variable are assigned an elastic modulus.

[0037] S3, performing sensitivity analysis on each of the super units to obtain the Spearman rank correlation coefficient;

[0038] S4, comparing the absolute value of the Spearman rank correlation coefficient of each super unit with the preset value to delete unqualified design variables;

[0039] S5, performing optimization analysis on the screened model to obtain an optimized and revised model.

[0040] This method is based on the analysis of ANSYS software. Since there are many structural units in the station, and the number of ANSYS design variables cannot exceed 60, it is impossible to correct each unit. Therefore, it is necessary to specify the design variables, that is, the parameters to be corrected. In a specific implementation scenario, the finite element model obtained by modeling the station structure has 107,299 units. According to the structural characteristics of the station, the entire station structure is divided into 13 parts, namely, the roof grid upper chord members, the roof grid lower chord members, the roof grid web members, the sandwich grid upper chord, the sandwich grid lower chord, the sandwich grid web members, the waiting layer beam, the platform layer, the track bearing layer, the roof column, the sandwich column, the column above the track bearing layer, and the column below the track bearing layer. Select the unit of interest from each part, and treat the unit with the same material as a super unit. Each super unit contributes a design variable. The result of this is that the entire finite element model has only 63 design variables in total. The specific distribution is shown in Table 1 below:

[0041] Table 1. Modified design variables of the finite element model of the station structure

[0042]

[0043] Among them, the unit of interest can select the most dangerous and vulnerable parts of the station structure based on experience.

[0044] It can be seen that even if one design variable is calculated for each super unit, there will still be 63 design variables, which exceeds the limit of 60 design variables of the ANSYS optimization module. In order to further reduce the design variables, a sensitivity analysis is performed on the super unit. The random input parameter is taken as the elastic modulus of each super unit, and the random output parameter is taken as the value of the objective function J:

[0045]

[0046] Among them, f i is the frequency corresponding to each vibration mode of the state variable, f i * are the frequency values ​​of each order measured by the experiment, and the state variables are functions of the constrained design variables.

[0047] The Spearman rank correlation coefficient obtained by sensitivity analysis is specifically determined by taking the preset value of 0.025 as the boundary. If the absolute value of the Spearman rank correlation coefficient is less than 0.025, it means that the design variable is unqualified and deleted. After this treatment, 37 super units are left, and the elastic modulus of these 37 super units is used as the optimized design variable.

[0048] Optimization analysis requires the selection of appropriate design variables, state variables and objective functions. The optimization results are obtained by changing the values ​​of the design variables.

[0049] The range of variation of the design variables is limited as follows:

[0050] 0.5E i ≤p i ≤1.5E i

[0051] E i is the elastic modulus corresponding to the design variable, P i are the parameter constraint values ​​corresponding to the design variables.

[0052] The range of changes of state variables is limited as follows:

[0053] 0.7f i * ≤f i ≤1.3f i *

[0054] Among them, fi is the frequency corresponding to each vibration mode of the state variable, f i * are the frequency values ​​of each order measured by the experiment, and the state variables are functions of the constrained design variables.

[0055] The objective function is the value to be minimized and must be a function of the design variables. That is, changing the value of the design variables will change the value of the objective function.

[0056] The first-order method is selected for optimization. The convergence tolerance of the objective function is 0.001, the initial iteration is set to 100, and the curve of the objective function changing with the number of iterations is obtained. Figure 2 As shown in the figure, the objective function decreases monotonically with the number of iterations, which is the characteristic of the first-order method. The disadvantage is that it is possible to obtain a local minimum instead of a global minimum. Therefore, it is necessary to select the zero-order method for further optimization, and the change curve of the objective function with the number of iterations is obtained as follows Figure 3 As shown in the figure, the objective function value changes randomly. The objective function value obtained by the zero-order method is larger than that obtained by the first-order method, so it can be considered that the first-order method obtains the global minimum value.

[0057] After optimization, the objective function dropped from 3 to 0.8. The frequency after optimization correction is closer to the test frequency value, but not completely consistent. This is mainly because the modified design variables are subject to parameter constraints of the design variables. Some design variables have reached the upper or lower limit requirements and cannot be further corrected. Error before correction = (initial value-test frequency) * 100 / test frequency, error after correction = (corrected value-test frequency) * 100 / test frequency. Therefore, a second optimization can be performed to obtain the change rate of each parameter after optimization correction. Figure 4 shown.

[0058] Beneficial effects:

[0059] (1) Conduct secondary development of large-scale finite element analysis software, perform parametric modeling, and use optimization modules to implement model correction.

[0060] (2) Model modification uses the first-order algorithm to optimize the solution and the zero-order algorithm to check to avoid obtaining a local optimal solution. Moreover, the first-order algorithm is slightly more accurate than the zero-order algorithm, but the calculation is time-consuming.

[0061] (3) After optimization, the matching degree of modal frequencies is significantly improved.

[0062] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0063] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0068] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A station structure model correction analysis method, characterized in that: The following steps are involved: S1, establishing a finite element model for the station structure, and specifying the units of interest in the finite element model as design variables; S2, select the units of interest from each part, and treat the units with the same material as super units. Each super unit contributes a design variable. According to the elastic properties of different types of materials, the elastic modulus of the material corresponding to each design variable is obtained; S3, performing sensitivity analysis on each of the super units to obtain the Spearman rank correlation coefficient; S4, comparing the absolute value of the Spearman rank correlation coefficient of each super unit with the preset value to delete unqualified design variables; S5, optimizing and analyzing the screened model to obtain an optimized and revised model; Wherein, S3 specifically includes: the random input parameter is taken as the elastic modulus of each super unit, and the random output parameter is taken as follows according to the objective function J: Among them, f i is the frequency corresponding to each vibration mode of the state variable, are the values ​​of each order frequency measured in the experiment, and the state variables are functions of the constrained design variables.

2. The station structure model correction analysis method according to claim 1 is characterized in that: The design variables include roof truss upper chord members, roof truss lower chord members, roof truss web members, sandwich truss upper chord, sandwich truss lower chord, sandwich truss web members, waiting layer beams, platform layer, track-bearing layer, roof columns, sandwich columns, columns above the track-bearing layer and columns below the track-bearing layer.

3. The station structure model correction analysis method according to claim 1 is characterized in that: The S4 specifically includes: taking the preset value of 0.025 as the boundary, if the absolute value of the Spearman rank correlation coefficient is less than 0.025, it means that the design variable is unqualified and is deleted.

4. The station structure model correction analysis method according to claim 3 is characterized in that: The S5 specifically includes: limiting the variation range of the design variables as follows: 0.5E i ≤p i ≤1.5E i E i is the elastic modulus corresponding to the design variable, P i are the parameter constraint values ​​corresponding to the design variables.

5. The station structure model correction analysis method according to claim 4 is characterized in that: The S5 specifically includes: limiting the change range of the state variable as follows: Among them, f i is the frequency corresponding to each vibration mode of the state variable, are the values ​​of each order frequency measured in the experiment, and the state variables are functions of the constrained design variables.

6. The station structure model correction analysis method according to claim 3 is characterized in that: The S5 specifically includes: firstly optimizing and solving the selected model by a first-order algorithm, and then verifying it by a zero-order algorithm.

7. A station structure model correction analysis method system, characterized in that: include: A finite element analysis module, for establishing a finite element model of the station structure and specifying units of interest in the finite element model as design variables; The secondary development module is used to select the units of interest from each part and treat the units with the same material as super units. Each super unit contributes a design variable. According to the elastic properties of different types of materials, the elastic modulus of the material corresponding to each design variable is obtained. A data correction module is used to perform sensitivity analysis on each of the super units to obtain a Spearman rank correlation coefficient, compare the absolute value of the Spearman rank correlation coefficient of each super unit with a preset value to delete unqualified design variables, and perform optimization analysis on the screened model to obtain an optimized and corrected model; The data correction module is also used to: randomly input the elastic modulus of each super unit, and randomly output the objective function J to obtain the value of: Among them, f i is the frequency corresponding to each vibration mode of the state variable, are the values ​​of each order frequency measured in the experiment, and the state variables are functions of the constrained design variables.

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