Composite material structure reliability digital twin parameter updating process and method

Abstract

The application discloses a composite material structure reliability digital twinning parameter updating process and method, and is characterized in that: on the basis of a structure reliability model, a structure reliability parameter is analyzed to determine a structure reliability parameter updating process; a strain is taken as a non-probability interval variable to consider a prediction error in a digital twinning model to realize real-time updating of the structure reliability parameter by giving a real-time strain non-probability interval; a strength parameter is taken as a truncated probability variable because a certain sample quantity is accumulated in previous tests, the strength value of this time is added to the strength sample to refit the strength distribution to realize real-time updating of the truncated probability variable after the single sample tensile test is finished; a non-probability interval of an elastic modulus is updated by considering that a calculation error of stress caused by an error of a load sensor finally transmits the error to the elastic modulus to realize updating of the non-probability interval of the elastic modulus; and the real-time updating of the reliability parameter and the real-time evaluation of the structure reliability are realized in the digital twinning model.

Description

Technical Field

[0001] This invention relates to the field of digital twins and the reliability of composite material structures, and in particular to a process and method for updating digital twin parameters of composite material structure reliability. Background Technology

[0002] With the development of aerospace technology, the application of various high-performance composite materials in aerospace structures is becoming increasingly common. Due to the high discreteness and complex failure modes of composite materials, response prediction and reliability assessment of composite structures are essential. In recent years, digital twin technology, which enables real-time mapping between virtual digital space and real physical space, has become a research hotspot. This technology allows for real-time prediction of structural response and real-time reliability assessment.

[0003] Compared to metallic materials, composite materials exhibit more diverse and complex failure modes, with greater dispersion in material performance parameters. As science and technology advance, the reliability requirements for equipment products are increasing. However, in engineering, obtaining uncertain parameters for composite materials is difficult, and insufficient uncertainty data exists. Using traditional probabilistic statistical reliability models to assess the reliability of composite material structures can lead to significant errors. Digital twin technology can solve the problems of difficulty in obtaining uncertain parameter data and information lag in reliability assessment of composite material structures. For real-time acquired reliability data, if the sample data is sufficient, traditional probabilistic variables can be used to describe the data. Considering that relevant parameters in engineering are usually bounded, the traditional probabilistic variables are truncated to obtain corresponding truncated probabilistic variables. If the sample data is insufficient and the probability distribution cannot be accurately fitted, non-probabilistic variables are used to describe the data. Furthermore, when facing complex engineering problems, iterative solutions to hybrid reliability indices often fail to converge or cannot iterate to the global minimum. Therefore, it is necessary to study algorithms for solving hybrid reliability indices using truncated probabilistic and non-probabilistic methods.

[0004] Because uncertainties exist in data acquisition, data reduction, and data prediction processes within digital twin models, it is necessary to fully consider these uncertainties and provide a parameter update process for structural reliability digital twins to establish a reliability digital twin model. The digital twin model includes various types of data throughout the entire lifecycle of the structure. Applying digital twin technology in reliability analysis can solve the problems of data scarcity, unavailability, and information lag in reliability analysis. Reliability digital twin models can achieve real-time reliability assessment, which will significantly improve the real-time performance and accuracy of reliability assessments. Currently, research on the application of digital twin technology in structural reliability analysis and assessment is limited. Only a few scholars have applied digital twin technology to reliability analysis and assessment based on probabilistic reliability models. Research on the practical application of digital twin technology in engineering for probabilistic-nonprobabilistic hybrid reliability is scarce. Considering the multi-scale uncertainties of composite material structures in reliability digital twin modeling, how to quickly and stably converge the structural function function to the global minimum under high nonlinearity, and how to update reliability data are all issues limiting the development of reliability digital twin models, and research on these issues is limited.

[0005] In summary, how to utilize digital twin technology combined with rapid reliability index calculation algorithms, fully consider reliability data, and achieve real-time parameter updates and real-time reliability assessment is a key issue that urgently needs to be addressed in the field of composite material structure reliability. Summary of the Invention

[0006] This invention provides a process and method for updating digital twin parameters of composite material structure reliability, which can realize the updating of structural reliability parameters and real-time reliability assessment.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A process and method for updating digital twin parameters of composite material structure reliability, including:

[0009] Step 1: Data Acquisition. Test data is collected using data acquisition equipment such as strain gauges and load sensors.

[0010] Step 2: Considering sensor error, fault physics model prediction error, and digital twin model prediction error, perform uncertainty analysis to determine the parameters of uncertainty variables;

[0011] Step 3: Store the relevant experimental data collected in Step 1 and the uncertainty variable parameters from Step 2 into the database for data management;

[0012] Step 4: Combining the failure physics model (multiple failure physics models for failure modes such as tensile fracture and fatigue damage) and the data-driven model, a reliability digital twin model is established within the database-based framework for predicting the response of composite material structures, and combined with the hybrid reliability model.

[0013] Step 5: Determine the range of non-probability interval variables based on strain error analysis in the digital twin model, determine the range of elastic modulus based on sensor error analysis, and determine the strength truncated distribution based on test results;

[0014] Step Six: Perform reliability assessment calculations using a global plus local fast solution algorithm. and index;

[0015] Step 7: The data collected in real time is transmitted to the reduced-order model in real time to update the reduced-order model and the error correction strategy, and finally realize the update of the digital twin model;

[0016] Step 8: Obtain strain error data in real time through the digital twin model that is updated in real time, and update the strain parameters of the non-probability interval variables;

[0017] Step 9: Determine whether the experiment is over;

[0018] Step 10: If the current test is over, determine whether all tests are completed. If the next test is to be conducted, update the intensity distribution parameters of the truncated probability variable and the elastic modulus parameters of the non-probability interval variable.

[0019] Step 11: Repeat steps 6 through 11 until all tests are completed to end the reliability digital twin prediction program, complete the reliability digital twin process, and achieve real-time reliability assessment.

[0020] Furthermore, in step two, the variable parameters are described using truncated probability variables if there is sufficient sample data, and using non-probability interval variables if the sample size is insufficient.

[0021] Furthermore, in step four, the reliability digital twin model is established by considering the real-time prediction errors of various models, especially the real-time update of parameters, after the failure physical model and the data-driven model are fused and reduced in order.

[0022] Furthermore, in step six, the global plus local fast solution algorithm is based on the genetic algorithm and gradient descent method. According to the variables and the limit state equation, global optimization algorithms such as quasi-annealing algorithm and particle swarm optimization algorithm and local solution algorithms such as various iterative algorithms can be selected.

[0023] Furthermore, in step eight, strain is treated as a non-probability interval variable in the digital twin model to consider prediction errors and realize real-time updates of structural reliability parameters by providing a real-time non-probability interval for strain.

[0024] Furthermore, in step ten, the strength parameter, due to the accumulation of a certain number of samples in each test, is considered as a truncated probability variable. After the tensile test of a single specimen is completed, the strength value of this test is added to the strength sample to refit the strength distribution and realize the real-time update of the truncated probability variable. The non-probability interval of the elastic modulus is achieved by considering the calculation error of stress caused by the error of the load sensor and finally transmitting the error to the elastic modulus.

[0025] The advantages of this invention are:

[0026] This invention enables the updating of structural reliability parameters and real-time reliability assessment. The established reliability digital twin parameter updating process and the global plus local fast solution algorithm can meet the real-time requirements of online deployment of digital twins. It realizes real-time calculation of reliability indicators and performs real-time reliability assessment of dynamic structures. In the dynamic process, the real-time quantitative calculation of structural reliability indicators reveals that the structure gradually progresses from an absolutely safe, interference-free region to interference and eventual failure. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the modeling process in an embodiment of the present invention;

[0028] Figure 2 The above are the frequency distribution histograms and normal distribution curves of intensity (R) in embodiments of the present invention.

[0029] Figure 3 This is a probability distribution diagram of intensity (R) truncation in an embodiment of the present invention;

[0030] Figure 4 This refers to the reliability index of the composite material standard specimen test in the embodiments of the present invention. The time-series variation curve;

[0031] Figure 5 This refers to the reliability index of the composite material standard specimen test in the embodiments of the present invention. The time-series variation curve. Detailed Implementation

[0032] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to specific embodiments.

[0033] This invention provides a process and method for updating digital twin parameters of composite material structure reliability, as shown in the flowchart below. Figure 1 As shown, it includes the following steps:

[0034] Step 1: Data Acquisition. Test data is collected using data acquisition equipment such as strain gauges and load sensors.

[0035] Step 2: Considering sensor error, fault physics model prediction error, and digital twin model prediction error, perform uncertainty analysis to determine the parameters of uncertainty variables;

[0036] Step 3: Store the collected experimental data and uncertainty variable parameters into a database for data management;

[0037] Step 4: Combining the physical failure model (multiple physical failure models for failure modes such as tensile fracture and fatigue damage), and the data-driven model, a reliability digital twin model is established within the database-based framework for predicting the response of composite material structures, combined with a hybrid reliability model.

[0038] Step 5: Combining stress intensity interference theory, determine the range of non-probability interval variables based on strain error analysis in the digital twin model, determine the range of elastic modulus based on sensor error analysis, and determine the strength truncation distribution based on test results;

[0039] Stress intensity interference model:

[0040] G = RS = RE·s

[0041] In the formula: R is the strength (a truncated probability variable; since there are relatively many samples of R, and considering that R is bounded in engineering, it is considered a truncated probability variable), S is the stress, and the stress value is calculated using the elastic modulus E (an interval variable) and strain ε (an interval variable) (E and ε are unique in real-time calculations and cannot be accurately fitted to distributions, so they are considered interval variables). Where R∈[R il R ir ], R il R is the lower bound of the intensity during the i-th test. ir This is the upper bound of the intensity during the i-th test. E∈[E il E ir ], E il E represents the lower limit of the elastic modulus range during the i-th test. ir This represents the upper limit of the elastic modulus range during the i-th test. ε∈[ε il , ε ir ], ε il ε represents the lower limit of the strain range during the i-th test. ir This represents the upper limit of the strain range during the i-th test.

[0042] Step Six: Perform hybrid reliability assessment calculations using a global and local fast solution algorithm. and index;

[0043] Hybrid reliability model:

[0044]

[0045] In the formula: Let be the truncated probability variable, Q be the non-probability variable, Φ(·) be the standard normal distribution function, and κ be the mixed reliability index.

[0046] Step 7: The data collected in real time is transmitted to the reduced-order model in real time to update the reduced-order model and the error correction strategy, and finally realize the update of the digital twin model;

[0047] Step 8: Obtain strain error data in real time through the digital twin model that is updated in real time, and update the strain parameters of the non-probability interval variables;

[0048] Step 9: Determine whether the experiment is over;

[0049] Step 10: If the current test is over, determine whether all tests are completed. If the next test is to be conducted, update the intensity distribution parameters of the truncated probability variable and the elastic modulus parameters of the non-probability interval variable.

[0050] Description of strength uncertainty: A normal distribution curve for strength is obtained by fitting the experimental data to a normal distribution. Considering the upper and lower limits of strength, strength is treated as a truncated probability variable. After the tensile test of a single specimen is completed, the strength value is added to the strength sample to refit the strength distribution, thereby achieving real-time updates of the truncated probability variable. Figure 2 The figure shows the frequency distribution histogram and normal distribution curve of the strength (R) measured by tensile tests on the first 22 standard composite material specimens. Figure 3 The figure shown is a truncated probability distribution of the strength (R) measured by tensile tests on the first 22 standard composite material specimens.

[0051] Uncertainty description of elastic modulus: The non-probabilistic interval of elastic modulus is updated by considering the calculation error of stress caused by the error of load sensor and finally passing the error to elastic modulus.

[0052] Step 11: Repeat steps six through eleven until all tests are completed, ending the reliability digital twin prediction program and completing the reliability digital twin process, achieving real-time reliability assessment. Figure 4 The figure shows the reliability index of the 23rd composite material standard specimen tensile test in this embodiment of the invention. The time-series variation curve. For example... Figure 5This is the reliability indicator for the tensile test of the 23rd composite material standard specimen in the embodiments of the present invention. The time-series variation curve.

[0053] The above description is merely a specific example of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A composite structure reliability digital twin parameter updating process and method, characterized in that, The method comprises the following steps: Step one: data acquisition, collecting test data by using data acquisition equipment; Step two: considering sensor error, fault physical model prediction error and digital twin model prediction error, performing uncertainty analysis to determine uncertainty variable parameters; Step three: storing the test data collected in step one and the uncertainty variable parameters in step two into a database for data management; Step four: combining the fault physical model and the data-driven model, establishing a reliability digital twin model under the structure response prediction digital twin framework based on the database; Step five: determining the interval range of the non-probabilistic interval variable based on the strain error analysis in the digital twin model, determining the interval range of the elastic modulus based on the sensor error analysis, and determining the strength truncated distribution according to the test results; Step six: Reliability evaluation calculation is performed using a global plus local fast solution algorithm With Indicators; Step seven: real-time data acquisition, real-time transmission to the reduced-order model, updating the reduced-order model and error correction strategy, and finally updating the digital twin model; Step eight: real-time acquisition of strain error data by the real-time updated digital twin model, and updating the non-probabilistic interval variable strain parameter; Step nine: judging whether the current test is completed; Step ten: if the current test is completed, judging whether all tests are completed, if the next test is continued, updating the truncated probability variable strength distribution parameter and the non-probabilistic interval variable elastic modulus parameter; Step eleven: repeating steps six to ten until the reliability digital twin prediction program is completed after all tests are completed, completing the reliability digital twin process, and realizing real-time reliability evaluation.

2. The composite structure reliability digital twin parameter updating process and method according to claim 1, wherein, In the step two, if the sample data is sufficient, the truncated probability variable is used for description, and if the sample quantity is insufficient, the non-probabilistic interval variable is used for description.

3. The composite structure reliability digital twin parameter updating process and method of claim 1, wherein, In the step four, the reliability digital twin model is obtained by fusing the reduced-order model of the fault physical model and the data-driven model, considering real-time prediction errors and parameter real-time updates.

4. The composite structure reliability digital twin parameter updating process and method of claim 1, wherein, In the step six, the global plus local fast solving algorithm is based on genetic algorithm and gradient descent method, and the global optimization algorithm or local solving algorithm is selected according to the variable and limit state equation.

5. The composite structure reliability digital twin parameter updating process and method of claim 1, wherein, In the step eight, the strain as a non-probabilistic interval variable considers the prediction error in the digital twin model to realize real-time update of the structure reliability parameter of the real-time strain non-probabilistic interval.

6. The composite structure reliability digital twin parameter updating process and method of claim 1, wherein, In the step ten, the strength parameter is considered as a truncated probability variable because it has accumulated a certain sample quantity in the previous tests; after the single sample tensile test is completed, the strength value obtained by the single sample tensile test is added to the strength sample to refit the strength distribution and realize real-time update of the truncated probability variable; the non-probabilistic interval of the elastic modulus is obtained by considering the calculation error of the stress caused by the error of the load sensor, and finally the error is transmitted to the elastic modulus, thereby realizing the non-probabilistic interval update of the elastic modulus.

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