Structure multi-parameter rapid calibration method capable of simultaneously meeting multi-scene operation requirements

Abstract

The invention provides a structure multi-parameter rapid calibration method capable of simultaneously meeting multi-scene operation requirements, and belongs to the field of structure design, and the method comprises the steps: S1, inputting a multi-parameter initial value of a to-be-calibrated structure; s2, successively carrying out finite element pre-processing, solving and post-processing on an operation scene of the structure to obtain a finite element simulation result of the structure; s3, performing data acquisition, data alignment and data alignment according to a structural finite element simulation result in combination with standard data; s4, performing data comparison through multiple methods to obtain a comparison result; s5, constructing a structure credibility evaluation system, performing confidence evaluation on a comparison result, if the comparison result is credible, ending calibration, and otherwise, turning to S6; and S6, carrying out sensitivity analysis on the parameters, giving out calibration values of the parameters, inputting the calibration values into S1, and repeating the steps S1 to S6 until the confidence coefficient is credible. According to the method, calibration of multiple parameters of the structure in multiple operation scenes can be completed in a short time, and the method has high accuracy and reliability.

Description

Technical Field

[0001] The present invention belongs to the field of structural design, and in particular relates to a method for rapid calibration of multiple parameters of a structure that simultaneously meets the requirements of multi-scenario operation. Background Art

[0002] Under the requirements of multi-scenario operation, the design requirements of the structure are becoming more and more stringent, and multiple parameters of the structural design need to be calibrated quickly and simultaneously. With the continuous development of science and technology, the application of finite element analysis (FEA) technology in the engineering field is becoming more and more extensive, which can provide a simulation verification method for whether the structural design can meet the requirements of scenario operation. As a numerical analysis method, finite element analysis decomposes complex physical systems or objects into several interconnected simple units (i.e., finite elements), uses mathematical theory and computer technology to solve the behavior of each unit, and then obtains the performance of the entire system. However, the accuracy and effectiveness of finite element analysis are highly dependent on the accurate calibration of parameters.

[0003] In finite element analysis, the calibration of structural parameters is a key step to ensure the accuracy of analysis results. These parameters usually include the mechanical properties of materials, the geometric characteristics of structures, etc. Accurate parameter calibration can ensure that the finite element model is closer to the actual situation, thereby providing more reliable prediction and analysis results.

[0004] Traditional parameter calibration methods mainly rely on experimental data and empirical formulas. However, this method has the following limitations: First, the acquisition of experimental data is often time-consuming and costly, especially in the case of complex structures or materials; second, experimental data may be affected by many factors, such as test conditions, operating errors, etc., resulting in inaccurate or unstable data; third, empirical formulas are usually based on specific assumptions and simplifications and may not fully reflect the actual situation. Therefore, how to quickly calibrate the multiple parameters of the structure while meeting the requirements of multiple scenarios has become an urgent problem to be solved. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for rapid calibration of multiple parameters of a structure that simultaneously meets the requirements of multi-scenario operation, comprising the following steps: Step S1: inputting the initial values ​​of multiple parameters of the structure to be calibrated, wherein the parameters include geometric dimension parameters and material parameters of the structure; Step S2: performing finite element pre-processing, solving, and post-processing on an operation scenario of the structure to obtain a structural finite element simulation result; Step S3: performing data acquisition, data alignment, and data correction according to the structural finite element simulation results combined with standard data; Step S4: According to the data type and the purpose of data comparison, data comparison is performed through chart correlation analysis, covariance and covariance matrix analysis, correlation coefficient analysis, regression analysis, information entropy and mutual information analysis to obtain comparison results; Step S5: complete finite element simulation, data processing and data comparison of all operation scenarios of the structure, extract characteristic values ​​of all data comparison results, build a structural credibility assessment system, and perform confidence assessment. If it is credible, the multi-parameter initial values ​​calibrated in step S1 meet the requirements, otherwise go to step S6; Step S6: Perform sensitivity analysis on the parameters, comprehensively analyze the sensitivity analysis results of each parameter, the data comparison results, and the applicable range of each parameter, give the calibration value of the parameter, and then input the calibration value into step S1 as the initial value of the parameter to be calibrated, and repeat steps S1 to S6 until the confidence level after evaluation by the structural credibility assessment system in step S5 is credible.

[0006] Beneficial effects: The present invention discloses a method for rapid calibration of multiple parameters of a structure that simultaneously meets the requirements of multiple operating scenarios. Based on simulation technology and comprehensive evaluation methods, the method can complete the calibration of multiple parameters of the structure in multiple operating scenarios in a relatively short time, and has high accuracy and reliability, which is specifically manifested in: (1) Improve calibration efficiency: The present invention can greatly shorten the time of parameter calibration and improve analysis efficiency; (2) Reducing calibration costs: The present invention can quickly obtain a large amount of data through simulation technology, reduce the dependence on experimental data, and thus reduce calibration costs.

[0007] (3) Improving model accuracy: Based on a comprehensive evaluation method, the present invention conducts a comprehensive evaluation on the simulation results of multiple scenarios of structural operation, so that the structural model designed according to the calibration parameters is more accurate and meets the requirements of multi-scenario operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a schematic flow chart of a method for rapid calibration of multiple parameters of a structure that meets the requirements of multiple scenarios at the same time; Figure 2 It is a schematic diagram of structural finite element simulation; Figure 3 It is a schematic diagram of data processing; Figure 4 This is a schematic diagram for data comparison; Figure 5 This is a schematic diagram of the result evaluation; Figure 6 Schematic diagram of parameter calibration. DETAILED DESCRIPTION

[0009] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0010] Embodiment 1 like Figure 1 As shown, an embodiment of the present invention provides a method for rapid calibration of multiple parameters of a structure that meets the requirements of multiple scenarios at the same time, including the following steps: Step S1: inputting the initial values ​​of multiple parameters of the structure to be calibrated, wherein the parameters include geometric dimension parameters and material parameters of the structure; Step S2: performing finite element pre-processing, solving, and post-processing on a running scenario of the structure to obtain a finite element simulation result of the structure; Step S3: performing data acquisition, data alignment, and data correction according to the structural finite element simulation results combined with standard data; Step S4: According to the data type and the purpose of data comparison, data comparison is performed through chart correlation analysis, covariance and covariance matrix analysis, correlation coefficient analysis, regression analysis, information entropy and mutual information analysis to obtain comparison results; Step S5: complete the finite element simulation, data processing and data comparison of all operation scenarios of the structure, extract the characteristic values ​​of all data comparison results, build a structural credibility assessment system, and conduct a confidence assessment. If it is credible, the multi-parameter initial values ​​calibrated in step S1 meet the requirements, otherwise go to step S6; Step S6: Perform sensitivity analysis on the parameters, comprehensively analyze the sensitivity analysis results of each parameter, the data comparison results, and the applicable range of each parameter, give the calibration value of the parameter, and then input the calibration value into step S1 as the initial value of the parameter to be calibrated, and repeat steps S1 to S6 until the confidence level after evaluation by the structural credibility assessment system in step S5 is credible.

[0011] In one embodiment, the above step S1: inputting initial values ​​of multiple parameters of the structure to be calibrated, wherein the parameters include geometric dimension parameters and material parameters of the structure; The embodiment of the present invention takes a valve as an example to quickly calibrate multiple parameters of the valve, such as geometric dimensions, materials, flow, pressure, etc. First, the initial values ​​of multiple parameters such as geometric dimensions, materials, flow, pressure, etc. of the valve structure to be calibrated are input, and the parameters do not set units.

[0012] like Figure 2As shown, in one embodiment, the above step S2: successively performing finite element pre-processing, solving, and post-processing on a running scenario of the structure to obtain a finite element simulation result of the structure, specifically includes: Step S21: performing finite element pre-processing on the structure including: meshing, material property assignment, boundary condition setting, and external load application; The finite element pre-processing of a valve operation scenario (e.g., controlling the passage of a medium with a known pressure and flow rate at room temperature) according to the embodiment of the present invention includes: finite element pre-processing of valve model simplification, material setting, mesh division, boundary condition setting, internal friction resistance, pressure, density and viscosity of the medium passing through, etc.; Step S22: solving the structure using a solver, including selection, setting and operation of the solver; Step S23: performing finite element post-processing on the structure includes: generating and viewing cloud maps and extracting and generating structural finite element simulation results.

[0013] The finite element post-processing of the valve includes: obtaining the stress-strain values, generating cloud maps, and obtaining the maximum stress value and position.

[0014] like Figure 3 As shown, in one embodiment, the above step S3: performing data acquisition, data alignment, and data correction according to the structural finite element simulation results combined with standard data specifically includes: Step S31: data acquisition includes: data extraction of structural finite element simulation results and input of standard data, the standard data includes: standard specification data, physical test data, and expert experience data; Step S32: Data alignment starts from a preset time and selects a common time of the structural finite element simulation result data and the standard data as the end point to align the two; Step S33: Data alignment is to extract the structural finite element simulation result data and the standard data respectively using the least common multiple of the time intervals for collecting the structural finite element simulation result data and the standard data as the latest time interval, thereby completing data alignment.

[0015] The numerical values ​​of the stress-strain simulation results of the valve in the embodiment of the present invention and the numerical values ​​of the stress-strain of the valve collected in the physical experiment under the same conditions (standard data) are used for data acquisition, data alignment and data correction.

[0016] like Figure 4 As shown, in one embodiment, the above step S4: according to the data type and the purpose of data comparison, data comparison is performed through chart correlation analysis, covariance and covariance matrix analysis, correlation coefficient analysis, regression analysis, information entropy and mutual information analysis to obtain comparison results, specifically including: Method 1: Graphical correlation analysis method. The advantage is that the correlation is clearly presented; the disadvantage is that the correlation cannot be accurately measured, lacks persuasiveness, and when there are more than two groups of data, the correlation analysis between the groups of data cannot be completed.

[0017] Method 2: Covariance and covariance matrix method. Covariance is used to measure the overall error of two variables. The positive or negative covariance indicates that the two variables are positively or negatively correlated. A covariance of 0 indicates that the two variables are unrelated. When there are more than two groups of data, a covariance matrix is ​​needed, but it cannot measure the closeness of the correlation.

[0018] Method 3: Correlation coefficient method, a statistical indicator of the closeness of the relationship between the reaction variables. The advantage of the correlation coefficient is that the relationship between the variables can be measured by numbers, and it has directionality, 1 represents positive correlation, -1 represents negative correlation, and the strength of the relationship between the variables can be measured. The closer to 0, the weaker the correlation; the disadvantage is that this relationship cannot be used to predict the data, that is, the relationship between the variables has not been refined and solidified to form a model.

[0019] Method 4: Regression analysis is a statistical method to determine the relationship between two or more groups of variables. Two variables use univariate regression, and more than two variables use multivariate regression. There are two preparations before performing regression analysis. The first is to determine the number of variables; the second is to determine the independent variables and dependent variables.

[0020] Method 5: Information entropy and mutual information method. Information entropy is an indicator used to measure the uncertainty of information in a univariate model. The greater the uncertainty of the information, the greater the entropy value. The main factor affecting the entropy value is probability. Mutual information is an indicator used to measure the correlation between information. When two pieces of information are completely correlated, the mutual information is 1, and when they are uncorrelated, it is 0.

[0021] The recommended usage scenarios of the above five methods are shown in Table 1. The data comparison method can be selected based on the advantages and disadvantages of the above methods.

[0022] Table 1: Recommended usage scenarios of the five methods

[0023]

[0024] The valve design of the embodiment of the present invention requires measuring the overall error between the stress-strain values ​​of the simulated and tested valves, without paying attention to the degree of closeness, so method 2, covariance and covariance matrix method, is selected to obtain the overall error value.

[0025] like Figure 5As shown, in one embodiment, the above step S5: completes finite element simulation, data processing and data comparison of all operation scenarios of the structure, extracts characteristic values ​​of all data comparison results, builds a structural credibility assessment system, and performs confidence assessment. If it is credible, the multi-parameter initial values ​​calibrated in step S1 meet the requirements, otherwise go to step S6, which specifically includes: Step S51: Repeat steps S2 to S4 to obtain structural finite element simulation results and comparison results of all operating scenarios of the structure, and then extract feature data of each comparison result; The embodiment of the present invention completes all valve operation scenarios, such as the control performance of the valve under a certain high temperature condition; the corrosion resistance of the valve; the sealing performance of the valve; the pressure loss performance generated after the medium flows through the valve; the action sensitivity and reliability, etc., and performs simulation analysis, compares the result data, and then extracts the characteristic data (value) of the comparison results of each valve operation scenario, such as the stress-strain simulation result value of the valve, and its overall error value; Step S52: First, a structural credibility evaluation system is constructed according to actual business, and then the evaluation indicators in the structural credibility evaluation system are divided into two categories: qualitative indicators and quantitative indicators. The values ​​of quantitative indicators are calculated and obtained by inputting the characteristic data of the comparison result of the previous step according to the actual business calculation method; the values ​​of qualitative indicators are calculated and obtained by setting the evaluation calculation method, and the experts refer to the characteristic data of the comparison result of the previous step to score; The present invention can construct a structural credibility evaluation system by using different methods according to the different requirements of different product / system design, production, operation, maintenance, etc., and the application unit can define the structural credibility evaluation system by itself. For example, the embodiment of the present invention adopts a comprehensive evaluation method to construct a structural credibility evaluation system; then the evaluation indicators of the structural credibility evaluation system are divided into qualitative (such as valve aesthetics, maintainability, etc.) and quantitative (such as valve strength performance, flow performance, opening and closing performance, etc.) two categories, for quantitative indicator values, directly according to the actual business calculation method, input the characteristic data of the comparison results of the previous step, calculate and obtain (calculate the strength, pressure loss, opening and closing force and opening and closing torque of the valve respectively), for qualitative indicator values, by setting the evaluation calculation method (it is recommended to use the hierarchical analysis method, which is a mature method and will not be repeated), experts refer to the characteristic data of the comparison results of the previous step, etc. for scoring, and then calculate and obtain (calculate the valve aesthetic percentage and maintainability percentage according to the scoring results); Step S53: Set weights for each evaluation index, normalize the values ​​of each evaluation index, multiply them by their respective weights, and then sum them up to obtain a comprehensive evaluation value: W ; Among them, W is the comprehensive evaluation value; is the weight of the i-th evaluation index, is the result after normalization of the i-th evaluation index value; Step S54: Perform a confidence assessment on the comprehensive assessment value according to the structural credibility assessment system. If the assessment result is credible, the initial values ​​of the multiple parameters calibrated in step S1 meet the requirements and the parameter calibration is ended; otherwise, proceed to step S6.

[0026] like Figure 6 As shown, in one embodiment, the above step S6: performing sensitivity analysis on the parameters, comprehensively analyzing the sensitivity analysis results of each parameter, the data comparison results, and the applicable range of each parameter, giving the calibration value of the parameter, and then inputting the calibration value into step S1 as the initial value of the parameter to be calibrated, and repeating steps S1 to S6 until the confidence level after evaluation by the structural credibility assessment system in step S5 is credible, specifically including: Step S61: performing sensitivity analysis on each parameter to be calibrated to quantify the influence of each parameter on the simulation result; The embodiment of the present invention performs sensitivity analysis on multiple parameters such as the geometric size, material, flow rate, pressure, etc. of the valve structure to obtain the influence of each parameter on the simulation result. The sensitivity analysis method for the independent variable parameters of the simulation analysis is relatively mature, and the present invention will not repeat it; Step S62: among the multiple parameters of the structure to be calibrated, calibrate each parameter according to the sensitivity from high to low, comprehensively considering the comparison results of each parameter and the applicable range of each parameter; For example, for highly sensitive parameters (such as valve inner diameter), improve their accuracy and strictly control their adjustment range; Step S63: Input the calibrated multi-parameter values ​​into step S1, and repeat steps S1 to S6 until the evaluation result of step S5 is credible.

[0027] The above description is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for rapid calibration of multiple parameters of a structure that simultaneously meets the requirements of multi-scenario operation, characterized in that: The steps include: Step S1: inputting the initial values ​​of multiple parameters of the structure to be calibrated, wherein the parameters include geometric dimension parameters and material parameters of the structure; Step S2: performing finite element pre-processing, solving, and post-processing on an operation scenario of the structure to obtain a structural finite element simulation result; Step S3: acquiring data, aligning data, and correcting data according to the structural finite element simulation results combined with standard data; Step S4: According to the data type and the purpose of data comparison, data comparison is performed through chart correlation analysis, covariance and covariance matrix analysis, correlation coefficient analysis, regression analysis, information entropy and mutual information analysis to obtain comparison results; Step S5: complete finite element simulation, data processing and data comparison of all operation scenarios of the structure, extract characteristic values ​​of all data comparison results, build a structural credibility assessment system, and perform confidence assessment. If it is credible, the multi-parameter initial values ​​calibrated in step S1 meet the requirements, otherwise go to step S6; Step S6: Perform sensitivity analysis on the parameters, comprehensively analyze the sensitivity analysis results of each parameter, the data comparison results, and the applicable range of each parameter, give the calibration value of the parameter, and then input the calibration value into step S1 as the initial value of the parameter to be calibrated, and repeat steps S1 to S6 until the confidence level after evaluation by the structural credibility assessment system in step S5 is credible.

2. The method for rapid calibration of multiple parameters of a structure that simultaneously meets the requirements of multiple scenarios according to claim 1 is characterized in that: The step S2: performing finite element pre-processing, solving, and post-processing on a running scenario of the structure to obtain a structural finite element simulation result, specifically includes: Step S21: performing finite element pre-processing on the structure, including: meshing, material property assignment, boundary condition setting, and external load application; Step S22: solving the structure using a solver, including selecting, setting and running the solver; Step S23: performing finite element post-processing on the structure includes: generating and viewing a cloud map and extracting and generating finite element simulation results of the structure.

3. The method for rapid calibration of multiple parameters of a structure that simultaneously meets the requirements of multiple scenarios according to claim 2 is characterized in that: The step S3: performing data acquisition, data alignment, and data correction according to the structural finite element simulation results combined with standard data, specifically includes: Step S31: data acquisition includes: data extraction of the structural finite element simulation results and input of standard data, wherein the standard data includes: standard specification data, physical test data, and expert experience data; Step S32: data alignment starts from a preset time, selects a common time of the structural finite element simulation result data and the standard data as the end point, and aligns the two; Step S33: Data alignment is to extract the structural finite element simulation result data and the standard data respectively using the least common multiple of the time intervals for collecting the structural finite element simulation result data and the standard data as the latest time interval, thereby completing data alignment.

4. The method for rapid calibration of multiple parameters of a structure that simultaneously meets the requirements of multiple scenarios according to claim 3 is characterized in that: The step S5: complete the finite element simulation, data processing and data comparison of all operation scenarios of the structure, extract the characteristic values ​​of all data comparison results, build a structure credibility assessment system, and conduct a confidence assessment. If it is credible, the multi-parameter initial values ​​calibrated in step S1 meet the requirements, otherwise go to step S6, which specifically includes: Step S51: repeating steps S2 to S4 to obtain structural finite element simulation results and comparison results of all operation scenarios of the structure, and then extracting feature data of each comparison result; Step S52: First, a structural credibility evaluation system is constructed according to actual business, and then the evaluation indicators in the structural credibility evaluation system are divided into two categories: qualitative indicators and quantitative indicators, wherein the values ​​of the quantitative indicators are calculated and obtained by inputting the characteristic data of the comparison result of the previous step according to the actual business calculation method; the values ​​of the qualitative indicators are calculated and obtained by setting the evaluation calculation method, and the experts refer to the characteristic data of the comparison result of the previous step to score; Step S53: setting weights for the evaluation indicators, normalizing the values ​​of the evaluation indicators, multiplying them by their respective weights, and then summing them up to obtain a comprehensive evaluation value: W ； Among them, W is the comprehensive evaluation value; is the weight of the i-th evaluation index, is the result after normalization of the i-th evaluation index value; Step S54: Perform a confidence assessment on the comprehensive assessment value according to the structural credibility assessment system. If the assessment result is credible, the initial values ​​of the multiple parameters calibrated in step S1 meet the requirements and the parameter calibration is terminated; otherwise, proceed to step S6.

5. The method for rapid calibration of multiple parameters of a structure that simultaneously meets the requirements of multiple scenarios according to claim 4 is characterized in that: The step S6: performing sensitivity analysis on the parameters, comprehensively analyzing the sensitivity analysis results of each parameter, the data comparison results, and the applicable range of each parameter, giving the calibration value of the parameter, and then inputting the calibration value into step S1 as the initial value of the parameter to be calibrated, and repeating steps S1 to S6 until the confidence level after evaluation by the structural credibility evaluation system in step S5 is credible, specifically including: Step S61: performing sensitivity analysis on each parameter to be calibrated to quantify the influence of each parameter on the simulation result; Step S62: among the multiple parameters of the structure to be calibrated, calibrate each parameter according to the sensitivity from high to low, comprehensively considering the comparison results of each parameter and the applicable range of each parameter; Step S63: Input the calibrated multi-parameter values ​​into step S1, and repeat steps S1 to S6 until the evaluation result of step S5 is credible.

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