CN116305522A - Digital twin-based aircraft structure reliability simulation test model calibration method, device and medium - Google Patents
Digital twin-based aircraft structure reliability simulation test model calibration method, device and medium Download PDFInfo
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Abstract
The invention provides a digital twin-based aircraft structure reliability simulation test model calibration method, equipment and medium. The method comprises the following steps: and carrying out parameterized modeling on the reliability simulation test model, carrying out a simulation model pre-experiment, carrying out simulation data reduction and proxy model construction, carrying out a physical experiment to obtain actual response data, and calibrating proxy model parameters. According to the invention, a reduced-order model method is adopted, a simulation database is built in an off-line manner, a proxy model exposing simulation parameters is quickly built, the simulation parameters are calibrated based on a genetic algorithm, an accurate and high-confidence model can be provided for a reliability simulation test, the analysis efficiency of the reliability simulation test is improved, the development amount of a physical test is reduced, and the product research, development and test cost is saved.
Description
Technical Field
The invention belongs to the field of structural reliability analysis and product test verification, and particularly relates to a digital twin-based aircraft structural reliability simulation test model calibration method, equipment and medium.
Background
Structural reliability analysis is a key research topic faced by aviation and mechanical fields for a long time, and in engineering application, reliability tests are widely carried out to verify whether a structure can keep integrity under working conditions and for a specified time. The simulation test means provides a new analysis tool for verifying the structural reliability, can partially replace or supplement a physical test in the product research and development process, and plays an important role in the analysis effect of a complex structural system. Digital Twinning (DT) is one of the digital technologies that have been developed in recent years, creates a digital representation of a physical product in the full life cycle of numerous products, can well support operation, maintenance and research and development, and performs efficient predictive analysis by establishing a system simulation model of the physical product, which is a great technical feature and advantage thereof, and is compatible with structural reliability simulation tests, in particular, evaluation of the reliability of a complex structural system.
In practical application, the test frequency of the reliability test requirement is high, the test environment is complex, and the complexity of the test object is different, so that the modeling, verification and development of the simulation test form various requirements, in particular the requirements of high-fidelity simulation modeling and the requirements of high-efficiency experiment development. Because it is difficult to have a complete and clear knowledge of the simulation test object, it is difficult to build a complete and accurate simulation test model at the beginning, which means that parameters of the simulation model may not be coincident with the true values, so that the parameters of the model need to be calibrated through a simulation test, and the mapping capability of the simulation test model to the actual object behavior is improved. The finite element model is an important means for the simulation of the aircraft structure, the model precision directly influences the precision of the simulation result, and under the condition of meeting a certain test reliability, the quality of the numerical simulation is directly related to the precision and the fidelity of the simulation model, and the simulation model needs to be verified and confirmed through consistency evaluation to be properly corrected. However, the means of manually tuning or repeatedly simulating tuning by using finite elements has high labor or calculation cost in practical operation.
The above problems can be solved by a data reduction method. From the classification of the realization method of the reduced order, the traditional reduced order model can be divided into three types, namely a simplified model method, a projection method and a data fitting method. The data fitting method is also called as a proxy model method, and aims to establish a black box type mapping relation between input and output parameters of a model so as to replace fine simulation. The common methods include polynomial response surface, gaussian process regression, support vector regression, artificial neural network and the like, and various methods have advantages and disadvantages and are often selected according to actual conditions. In order to solve the problem of insufficient or reduced prediction precision, the adaptive dynamic model reduction method reconstructs the proxy model by adding new sample points, and can adaptively update the model in the using stage for auxiliary solving of the optimization problem.
For the model accuracy checking problem of the aircraft structure in the reliability simulation test, on one hand, the simulation model of the test object is basically complete, the test load distribution points can be more and large-scale simulation data can be easily formed after the test is established at the beginning of development; on the other hand, a certain physical test needs to be designed to form a data set used for calibrating the model, and the actual response of the model under different loads is fully ascertained. And the simulation model can be effectively calibrated by comprehensively using a large amount of data in two aspects, so that a test scheme and a test plan aiming at the structural reliability are further improved. Therefore, a reliability simulation test model calibration method and tool for an aircraft structure need to be designed.
Disclosure of Invention
In order to solve the technical problems, the invention provides a digital twin-based aircraft structure reliability simulation test model calibration method, equipment and medium. The method comprises the steps of: and carrying out parameterized modeling on the reliability simulation test model, carrying out a simulation model pre-experiment, carrying out simulation data reduction and proxy model construction, carrying out a physical experiment to obtain actual response data, and calibrating proxy model parameters. According to the invention, a reduced-order model method is adopted, a simulation database is built in an off-line manner, a proxy model exposing simulation parameters is quickly built, the simulation parameters are calibrated based on a genetic algorithm, an accurate and high-confidence model can be provided for a reliability simulation test, the analysis efficiency of the reliability simulation test is improved, the development amount of a physical test is reduced, and the product research, development and test cost is saved.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a digital twin-based aircraft structure reliability simulation test model calibration method comprises the following steps:
step 1, parameterized modeling of a reliability simulation test model, which comprises the following steps: converting a digital model of an aircraft structure into a simulation model capable of performing finite element calculation, extracting main parameters influencing simulation calculation results of the model, wherein the main parameters comprise material properties and geometric dimensions, and realizing parameterization construction of the model by means of finite element modeling and simulation tools; determining a parameter feasible domain and a distribution form according to the material batch data and the geometric measurement result;
step 2, carrying out a simulation model pre-test;
step 3, reducing the order of the simulation data and constructing a proxy model;
step 4, carrying out a physical test to obtain actual response data;
step 5, calibrating proxy model parameters;
and 6, outputting the calibrated simulation model, and performing a reliability simulation test on the simulation model.
Further, the step 2 includes:
selecting parameters in a Latin hypercube sampling mode in the range of the parameter feasible region according to the parameter feasible region determined in the step 1 and the load applied in the test, setting a simulation model pre-test, applying the simulation test load, and solving the deformation of the structural simulation model by a finite element method; and selecting a result data point of the simulation model according to the strain of the physical test, the arrangement condition of the displacement sensor and the related data point of the structural reliability simulation analysis.
Further, the step 3 includes:
constructing a proxy model by using a model order reduction method based on the result data points of the simulation model obtained in the step 2, wherein the simulation test load in the step 1 is used as the input of the proxy model, the main parameters of the simulation test model are used as the parameters of the proxy model, the relevant data points of the simulation analysis in the step 2 are used as the output of the proxy model, and the proxy model is trained and generated by using a neural network and fitting coefficient equation method; and (3) packaging parameters and solvers of the reduced order model into an FMU module for joint simulation by using an FMI standardized simulation interface, and establishing a system-level simulation model according to the association relation of the simulation test.
Further, the step 4 includes:
developing multiple groups of physical tests under different load conditions to obtain main displacement and strain data of related data points
Is provided to step 5 for calibrating proxy model parameters.
Further, the step 5 includes:
and (3) evaluating the consistency of the simulation result and the physical test result of the agent model constructed in the step (3) through a comparison and verification formula, further applying the comparison and verification formula as an objective function of a calibration algorithm, and calibrating model parameters of the agent model by using an optimization method of a genetic algorithm.
Further, the step 5 specifically includes:
step 5.1, setting an initialized population of the agent model:
generating agent model parameters of an initialized population by random sampling according to the parameter feasible domain determined in the step 1, setting a population scale N, and forming a parameter list of a simulation model
According to the type of the physical test load, the number K of test working conditions is set, and the load scale under the kth working condition is set as F k ；
Step 5.2 evaluating the comparative verification results of individuals in the population:
for the agent model individuals established in the step 5.1, calculating the simulation test response of each individual under the kth working condition through a comparison verification algorithm
Comparing with the corresponding physical test results, calculating the objective function +.>
Objective function->
The RSME formula is adopted:
step 5.3, judging whether the ending condition is met:
setting the maximum coefficient number of population evolution as M, and setting the objective function rechecking coefficient number as M c If the current number of generations reaches M or returns to M c Personal of the world
If the variation is smaller than the set precision E, judging that the optimization process is finished;
and 5.4, performing population evolution operation:
setting crossover and variation of individual parameter genes in a population and a selection mechanism of dominant individuals in current generations;
repeating steps 5.2 to 5.4 until the end condition is reached.
Further, the step 6 includes:
and (3) carrying the proxy model parameters marked in the step (5) into the established simulation model in the step (3), setting a reliability simulation test load working condition, and simulating and calculating a predicted life result of the proxy model.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, and is characterized in that the processor realizes the steps of the digital twin-based aircraft structure reliability simulation test model calibration method when executing the program.
The invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the digital twin-based aircraft structure reliability simulation test model calibration method.
The beneficial effects are that:
according to the invention, the simulation agent model of the structure analysis is constructed, so that the simulation efficiency is improved, feasibility is provided for large-scale analysis of the reliability simulation test, the reliability index can be further evaluated according to the parameter uncertainty of the structure, and a reference is provided for structural design and verification.
Drawings
FIG. 1 is a step diagram of a digital twin-based aircraft structure reliability simulation test model calibration method;
FIG. 2 is a flow chart of model calibration based on genetic algorithm;
FIG. 3 is a geometric view of a tab construction in an embodiment;
FIG. 4 is a cloud image of finite element analysis results of a tab;
FIG. 5 is a diagram of a structural digital twin proxy model built based on a reduced order method;
fig. 6 is a comparative verification result of the example.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
As shown in FIG. 1, the digital twin-based aircraft structure reliability simulation test model calibration method specifically comprises the following steps:
step 1, parameterized modeling of a reliability simulation test model:
the digital model of the aircraft structure is converted into a simulation model capable of performing finite element calculation, main parameters affecting simulation calculation results of the model are extracted, the main parameters comprise material properties, geometric dimensions and the like, and parameterization construction of the model is realized by means of finite element modeling and simulation tools. The parameter feasible region (i.e., distribution interval) and the distribution form are determined according to the material batch data and the geometric measurement result.
Step 2, carrying out a simulation model pre-test:
and (3) selecting parameters in a Latin hypercube sampling mode in the range of the parameter feasible domain according to the parameter feasible domain determined in the step (1) and the load form and range applied in the test, setting a simulation model pre-test, and solving the deformation of the structural simulation model by a finite element method. And selecting a result data point of the simulation model for processing in the step 3 according to the arrangement conditions of sensors such as strain and displacement of a physical test and the related data points of the structural reliability simulation analysis.
Step 3, reducing the order of simulation data and constructing a proxy model:
based on the simulation data obtained in the step 2, constructing a proxy model by using a model order reduction method, wherein the simulation test load in the step 1 is used as the input of the proxy model, the main physical parameters of the simulation test model are used as the parameters of the proxy model, the simulation analysis related data points in the step 2 are used as the output of the proxy model, and the proxy model is trained and generated by using a neural network, a fitting coefficient equation and other methods. And (3) packaging parameters and solvers of the reduced order model into an FMU module for joint simulation by using an FMI standardized simulation interface, and establishing a system-level simulation model according to the association relation of the simulation test.
And 4, carrying out a physical test to obtain actual response data:
developing multiple groups of physical tests under different load conditions to obtain main data such as displacement, strain and the like of related data points
Is provided to step 5 for calibrating proxy model parameters.
And 5, calibrating proxy model parameters:
and (3) evaluating the consistency of the simulation result and the physical test result of the agent model constructed in the step (3) through a comparison and verification formula, further applying the comparison and verification formula as an objective function of a calibration algorithm, and calibrating model parameters of the agent model by using optimization methods such as a genetic algorithm. Here, as shown in fig. 2, a calibration implementation procedure based on a genetic algorithm is given.
Step 5.1, setting an initialized population of the agent model:
generating agent model parameters of an initialized population by random sampling according to the parameter feasible domain determined in the step 1, setting a population scale N, and forming a parameter list of a simulation model
According to the type of the physical test load, the number K of test working conditions is set, and the load scale under the kth working condition is set as F k 。
Step 5.2 evaluating the comparative verification results of individuals in the population:
for the agent model individuals established in the step 5.1, calculating the simulation test response of each individual under the kth working condition through a comparison verification algorithm
Comparing with the corresponding physical test results, calculating the objective function +.>
The objective function here employs the RSME formula.
Step 5.3, judging whether the ending condition is met:
setting the maximum coefficient number of population evolution as M, and setting the objective function rechecking coefficient number as M c If the current number of generations reaches M or returns to M c Personal of the world
And if the variation is smaller than the set precision epsilon, judging that the optimization process is finished.
And 5.4, performing population evolution operation:
setting crossover and variation of individual parameter genes in the population and a selection mechanism of dominant individuals in the current generation.
Repeating steps 5.2 to 5.4 until the end condition is reached.
Step 6, outputting a calibrated simulation model FMU, and performing a reliability simulation test on the simulation model:
and (3) carrying the proxy model parameters marked in the step (5) into the established simulation model in the step (3), setting a reliability simulation test load working condition, and simulating and calculating a predicted life result of the proxy model. The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, and is characterized in that the processor realizes the steps of the digital twin-based aircraft structure reliability simulation test model calibration method when executing the program.
The invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the digital twin-based aircraft structure reliability simulation test model calibration method.
Examples
In this embodiment, consider an ear piece part in an aviation structure, as shown in fig. 3, two ends of the ear piece are respectively fixed with other connection structures by 2 bolts, the material is 7075 aluminum alloy, the elastic modulus is in the range of 70 GPa-73 GPa, the poisson ratio is 0.33, the thickness is 1mm, and the ear piece part is manufactured through a laser cutting process. The part bears cyclic tensile load under the working state, the service life mainly depends on the fatigue fracture problem at the maximum stress, and the reliability of the part needs to be determined by carrying out a fatigue test. The extreme stress of the part is mainly influenced by load, geometry and material properties, wherein the dispersibility and uncertainty of the elastic modulus are large, and the extreme stress is a main factor influencing the accuracy of the simulation test.
In the embodiment, the reliability of the part under 1200N maximum load and stress ratio of 0 state needs to be considered, the number of working conditions is 1, and the displacement value of the load applying end can be obtained by applying fatigue load through one end support and one end in a physical test. The force-displacement curve is selected as calibration data for the elastic modulus properties.
Firstly, setting a plurality of groups of simulation analysis tasks with different material parameters and different load sizes according to the attribute distribution range by establishing a finite element simulation model of a test object to obtain a plurality of groups of finite element simulation results, wherein a cloud chart of the results is shown in figure 4.
Then, according to the load as input, the end displacement and the maximum stress value as output, and through a neural network method, a proxy model is established, so that the model can be used for deformation response prediction, life prediction and the like of the structure digital twin, as shown in fig. 5.
And (3) performing a physical test and a simulation test with the same load input to obtain force displacement curve data, comparing the difference degree of the two data through an RSME formula, fine-adjusting an elastic modulus parameter for a simulation model FMU through an optimization algorithm until an RSME result reaches a minimum value, and outputting a calibrated elastic modulus value of 71.8GPa. The comparative verification image is shown in fig. 6, and has good consistency.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. The aircraft structure reliability simulation test model calibration method based on digital twinning is characterized by comprising the following steps of:
step 1, parameterized modeling of a reliability simulation test model, which comprises the following steps: converting a digital model of an aircraft structure into a simulation model capable of performing finite element calculation, extracting main parameters influencing simulation calculation results of the model, wherein the main parameters comprise material properties and geometric dimensions, and realizing parameterization construction of the model by means of finite element modeling and simulation tools; determining a parameter feasible domain and a distribution form according to the material batch data and the geometric measurement result;
step 2, carrying out a simulation model pre-test;
step 3, reducing the order of the simulation data and constructing a proxy model;
step 4, carrying out a physical test to obtain actual response data;
step 5, calibrating proxy model parameters;
and 6, outputting the calibrated simulation model, and performing a reliability simulation test on the simulation model.
2. The method for calibrating the simulation test model of the reliability of the aircraft structure based on the digital twin according to claim 1, wherein the step 2 comprises the following steps:
selecting parameters in a Latin hypercube sampling mode in the range of the parameter feasible region according to the parameter feasible region determined in the step 1 and the load applied in the test, setting a simulation model pre-test, applying the simulation test load, and solving the deformation of the structural simulation model by a finite element method; and selecting a result data point of the simulation model according to the strain of the physical test, the arrangement condition of the displacement sensor and the related data point of the structural reliability simulation analysis.
3. The method for calibrating the simulation test model of the reliability of the aircraft structure based on the digital twin according to claim 2, wherein the step 3 comprises the following steps:
constructing a proxy model by using a model order reduction method based on the result data points of the simulation model obtained in the step 2, wherein the simulation test load in the step 1 is used as the input of the proxy model, the main parameters of the simulation test model are used as the parameters of the proxy model, the relevant data points of the simulation analysis in the step 2 are used as the output of the proxy model, and the proxy model is trained and generated by using a neural network and fitting coefficient equation method; and (3) packaging parameters and solvers of the reduced order model into an FMU module for joint simulation by using an FMI standardized simulation interface, and establishing a system-level simulation model according to the association relation of the simulation test.
4. A method for calibrating a simulation test model of the reliability of an aircraft structure based on digital twinning according to claim 3, wherein the step 4 comprises:
developing multiple groups of physical tests under different load conditions to obtain main displacement and strain data of related data points
Is provided to step 5 for calibrating proxy model parameters.
5. The method for calibrating the simulation test model of the reliability of the aircraft structure based on the digital twin according to claim 4, wherein the step 5 comprises the following steps:
and (3) evaluating the consistency of the simulation result and the physical test result of the agent model constructed in the step (3) through a comparison and verification formula, further applying the comparison and verification formula as an objective function of a calibration algorithm, and calibrating model parameters of the agent model by using an optimization method of a genetic algorithm.
6. The method for calibrating the simulation test model of the aircraft structure reliability based on digital twinning according to claim 5, wherein the step 5 specifically comprises the following steps:
step 5.1, setting an initialized population of the agent model:
generating agent model parameters of an initialized population by random sampling according to the parameter feasible domain determined in the step 1, setting a population scale N, and forming a parameter list of a simulation model
According to the type of the physical test load, the number K of test working conditions is set, and the load scale under the kth working condition is set as F k ；
Step 5.2 evaluating the comparative verification results of individuals in the population:
for the agent model individuals established in the step 5.1, calculating the simulation test response of each individual under the kth working condition through a comparison verification algorithm
Comparing with the corresponding physical test results, calculating the objective function +.>
Objective function->
The RSME formula is adopted:
step 5.3, judging whether the ending condition is met:
setting the maximum coefficient number of population evolution as M, and setting the objective function rechecking coefficient number as M c If the current number of generations reaches M or returns to M c Personal of the world
If the variation is smaller than the set precision E, judging that the optimization process is finished;
and 5.4, performing population evolution operation:
setting crossover and variation of individual parameter genes in a population and a selection mechanism of dominant individuals in current generations;
repeating steps 5.2 to 5.4 until the end condition is reached.
7. The method for calibrating a digital twin-based aircraft structure reliability simulation test model according to claim 5 or 6, wherein the step 6 comprises:
and (3) carrying the proxy model parameters marked in the step (5) into the established simulation model in the step (3), setting a reliability simulation test load working condition, and simulating and calculating a predicted life result of the proxy model.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of a digital twin based aircraft structure reliability simulation test model calibration method according to any of claims 1 to 7 when the program is executed.
9. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of a digital twin based aircraft structure reliability simulation test model calibration method according to any of claims 1 to 7.
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Cited By (7)
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| CN116738759A (en) * | 2023-07-19 | 2023-09-12 | 无锡雪浪数制科技有限公司 | Method and device for designing and operating equipment, computer equipment and readable storage medium |
| CN116884862A (en) * | 2023-09-07 | 2023-10-13 | 江苏长晶科技股份有限公司 | Bump manufacturing method based on 3D printing and chip packaging structure |
| CN117131708A (en) * | 2023-10-26 | 2023-11-28 | 中核控制系统工程有限公司 | Modeling method and application of digital twin anti-seismic mechanism model of nuclear industry DCS equipment |
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| CN117540589A (en) * | 2024-01-10 | 2024-02-09 | 四川启睿克科技有限公司 | Multi-parameter simulation model calibration method |
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| CN118313141A (en) * | 2024-04-23 | 2024-07-09 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Product reliability digital twin model construction method and computer equipment |
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| CN116738759A (en) * | 2023-07-19 | 2023-09-12 | 无锡雪浪数制科技有限公司 | Method and device for designing and operating equipment, computer equipment and readable storage medium |
| CN116738759B (en) * | 2023-07-19 | 2023-11-21 | 无锡雪浪数制科技有限公司 | Method and device for designing and operating equipment, computer equipment and readable storage medium |
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| CN117215728A (en) * | 2023-11-06 | 2023-12-12 | 之江实验室 | Agent model-based simulation method and device and electronic equipment |
| CN117215728B (en) * | 2023-11-06 | 2024-03-15 | 之江实验室 | Agent model-based simulation method and device and electronic equipment |
| CN117540589A (en) * | 2024-01-10 | 2024-02-09 | 四川启睿克科技有限公司 | Multi-parameter simulation model calibration method |
| CN117993270A (en) * | 2024-04-07 | 2024-05-07 | 航天精工股份有限公司 | Digital twinning-based fastening connection system assembly quality assessment method |
| CN117993270B (en) * | 2024-04-07 | 2024-06-28 | 航天精工股份有限公司 | Digital twinning-based fastening connection system assembly quality assessment method |
| CN118313141A (en) * | 2024-04-23 | 2024-07-09 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Product reliability digital twin model construction method and computer equipment |
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