CN116721720A - Composite structure reliability digital twin parameter updating process and method - Google Patents
Composite structure reliability digital twin parameter updating process and method Download PDFInfo
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Abstract
The invention discloses a composite material structure reliability digital twin parameter updating flow and a method, which are used for analyzing structure reliability parameters on the basis of a structure reliability model and determining the structure reliability parameter updating flow. Taking the strain as a non-probability interval variable, and giving a real-time strain non-probability interval by taking a prediction error into consideration in a digital twin model to realize real-time updating of the structural reliability parameter; the intensity parameter is considered as a tail-biting probability variable because a certain sample number is accumulated in the previous test, and the intensity value is added into the intensity sample to be re-fitted with the intensity distribution after the stretching test of a single sample is finished, so that the tail-biting probability variable is updated in real time; the non-probability interval of the elastic modulus is updated by considering calculation errors of stress caused by errors of the load sensor and finally transmitting the errors to the elastic modulus. Real-time updating of reliability parameters and real-time evaluation of structural reliability are realized in the digital twin model.
Description
Technical Field
The invention relates to the field of digital twin and composite structure reliability, in particular to a digital twin parameter updating flow and method for composite structure reliability.
Background
With the development of aerospace technology, the application proportion of various composite materials with excellent performances in aerospace structures is higher and higher. Because of the large discreteness of composite materials, complex failure modes are necessary for response prediction and reliability assessment of composite material structures. In recent years, a digital twin technology capable of realizing real-time mapping of a virtual digital space and a real physical space becomes a research hot spot, and real-time prediction of structural response and real-time evaluation of reliability can be realized by using the technology.
Compared with a metal material, the failure mode of the composite material is more various and complex, and the dispersion of the material performance parameters is larger. With the development of scientific technology, the requirements of equipment products on reliability are higher and higher, and in engineering, uncertainty parameters of the composite material are difficult to obtain, uncertainty data are insufficient, and if the reliability of the composite material structure is estimated by using a traditional probability statistical reliability model, larger errors are caused. The reliability evaluation of the composite material structure based on the digital twin technology can solve the problems of difficult acquisition of uncertainty parameter data, information lag and the like. Aiming at reliability data acquired in real time, if the sample data is sufficient, describing the sample data by adopting a traditional probability variable and considering related parameters in engineering, wherein the related parameters are usually bounded, and performing tail-cutting treatment on the traditional probability variable to obtain a corresponding tail-cutting probability variable; if the sample data is lacking, the probability distribution cannot be accurately fitted, and the non-probability variable is used for describing. Meanwhile, when facing the complex problem in engineering, the problem that the iteration of the mixed reliability index is not converged or can not be iterated to the global minimum is solved, and the research on the tail-biting probability-non-probability mixed reliability index solving algorithm is necessary.
In the digital twin model, uncertainty exists in the processes of data acquisition, data reduction and data prediction, so that the uncertainty in the digital twin process is fully considered, a structural reliability digital twin parameter updating flow is given, and therefore, the establishment of the reliability digital twin model is necessary. The digital twin model comprises various types of data of the whole life cycle of the whole structure, and the problems that the data is lack and cannot be obtained and information is lagged in the reliability analysis can be solved by applying the digital twin technology in the reliability analysis. The reliability digital twin model can achieve real-time reliability assessment, and the real-time performance and accuracy of the reliability assessment are greatly improved. At present, the structure reliability analysis and evaluation application digital twin technology is less studied, only a few students apply the digital twin technology to carry out reliability analysis and evaluation based on a probability reliability model, and the related study of actually applying the digital twin technology to the engineering for probability-non-probability hybrid reliability is less. In the modeling of the reliability digital twin of the composite material structure, the multi-scale uncertainty of the composite material structure is considered, and how the structure function is quickly and stably converged to obtain the global minimum value under the high nonlinearity degree, how the reliability data is updated and the like are all problems for limiting the development of the reliability digital twin model, and the researches on the problems are very few.
In summary, how to utilize the digital twin technology in combination with the reliability index rapid solving algorithm to fully consider the reliability data, so as to achieve real-time update of parameters and real-time evaluation of reliability is a key problem to be solved in the field of the reliability of the composite structure at present.
Disclosure of Invention
The invention provides a composite material structure reliability digital twin parameter updating flow and method, which can realize structure reliability parameter updating and real-time reliability evaluation.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a composite structure reliability digital twin parameter updating flow and method comprises the following steps:
step one: acquiring data, namely acquiring test data by using data acquisition equipment through strain gauges, load sensors and the like;
step two: taking sensor errors, fault physical model prediction errors and digital twin model prediction errors into consideration, carrying out uncertainty analysis, and determining uncertainty variable parameters;
step three: storing the related test data acquired in the first step and the uncertainty variable parameters in the second step into a database for data management;
step four: combining a fault physical model (multiple fault physical models of failure modes such as tensile fracture and fatigue damage) and a data driving model, and combining a hybrid reliability model under a composite material structure response prediction digital twin framework established based on a database to establish a reliability digital twin model;
step five: determining the interval range of a non-probability interval variable based on strain error analysis in the digital twin model, determining the interval range of elastic modulus based on sensor error analysis, and determining the intensity truncated distribution according to test results;
step six: reliability evaluation calculation using global and local quick solving algorithmAnd->An index;
step seven: the data acquired in real time are transmitted to the reduced order model in real time, so that the updating of the reduced order model and the error correction strategy is realized, and finally, the updating of the digital twin model is realized;
step eight: the strain error data is obtained in real time through a digital twin model updated in real time, and the variable strain parameters of the non-probability interval are updated;
step nine: judging whether the test is finished;
step ten: if the test is finished, judging whether all the tests are finished, if the next test is continued to be carried out, updating the intensity distribution parameters of the truncated probability variable and the elastic modulus parameters of the non-probability interval variable;
step eleven: and repeating the steps six to eleven until the reliability digital twin prediction program is ended after all the tests are completed, and completing the reliability digital twin process, thereby realizing the real-time reliability evaluation.
Further, in step two, the variable parameter is described using a truncated probability variable if the sample data is sufficient, and a non-probability interval variable if the number of samples is lacking.
In the fourth step, the reliability digital twin model is built by considering real-time prediction errors of various models, especially considering real-time parameter updating after the fault physical model and the data driving model are fused and reduced.
In the sixth step, the global and local rapid solving algorithm is based on a genetic algorithm and a gradient descent method, and a global optimization algorithm such as an annealing algorithm and a particle swarm algorithm and a local solving algorithm such as various iterative algorithms can be selected according to a variable and a limit state equation.
Further, in step eight, the strain is used as a non-probability interval variable to give a real-time strain non-probability interval in consideration of the prediction error in the digital twin model to realize real-time update of the structural reliability parameter.
Further, in step ten, the intensity parameter is considered as a tail-biting probability variable because it accumulates a certain number of samples in the past test, and after the tensile test of a single sample is finished, the intensity value is added into the intensity samples to re-fit the intensity distribution, so as to realize the real-time update of the tail-biting probability variable; the non-probability interval of the elastic modulus is updated by considering calculation errors of stress caused by errors of the load sensor and finally transmitting the errors to the elastic modulus.
The invention has the advantages that:
the invention can realize the update of the structural reliability parameter and the real-time reliability evaluation; the established reliable digital twin parameter updating flow and the global and local quick solving algorithm can meet the real-time requirement of digital twin online deployment; the reliability index is calculated in real time, the reliability of the dynamic structure is evaluated in real time, and the structure is found from the real-time quantitative calculation of the reliability index of the structure in the dynamic process, and the structure gradually goes from an absolute safe area without interference to interference until failure.
Drawings
FIG. 1 is a modeling flow chart in an embodiment of the invention;
FIG. 2 is a histogram of intensity (R) frequency distribution and a normal distribution curve according to an embodiment of the present invention;
FIG. 3 is a graph showing the probability distribution of intensity (R) tail-biting in an embodiment of the present invention;
FIG. 4 is a graph showing the reliability index of the composite standard test sample in the embodiment of the inventionA time-series change curve of (2);
FIG. 5 is a graph showing the reliability index of the composite standard test sample in the embodiment of the inventionTime-series change curves of (2).
Detailed Description
The present invention will be described in further detail with reference to specific examples for better understanding of the technical scheme of the present invention by those skilled in the art.
The invention provides a composite material structure reliability digital twin parameter updating flow and method, wherein the flow chart is shown in figure 1, and comprises the following steps:
step one: acquiring data, namely acquiring test data by using data acquisition equipment through a strain gauge load sensor and the like;
step two: taking sensor errors, fault physical model prediction errors and digital twin model prediction errors into consideration, carrying out uncertainty analysis, and determining uncertainty variable parameters;
step three: storing the collected related test data and uncertainty variable parameters into a database for data management;
step four: combining a fault physical model (multiple fault physical models of failure modes such as tensile fracture and fatigue damage), a data driving model, and a mixed reliability model under a composite material structure response prediction digital twin framework established based on a database;
step five: combining with a stress intensity interference theory, determining the interval range of a non-probability interval variable based on strain error analysis in a digital twin model, determining the interval range of elastic modulus based on sensor error analysis, and determining intensity truncated distribution according to test results;
stress intensity interference model:
G＝R-S＝R-E·s
wherein: r is strength (the tail-biting probability variable, the samples of R are relatively more, R is bounded in the engineering and then considered as the tail-biting probability variable), S is stress, and the stress value is calculated by the elastic modulus E (interval variable) and the strain epsilon (interval variable) (E and epsilon are unique in real-time calculated values and cannot be fit to accurate distribution, and the stress value is considered as the interval variable). Wherein R is E [ R ] il ，R ir ]，R il For the lower intensity bound at the ith test, R ir Is the upper bound of the intensity at the ith test. E [ E ] il ，E ir ]，E il For the lower limit of the elastic modulus interval at the ith test, E ir The upper limit of the elastic modulus interval at the ith test. Epsilon il ，ε ir ]，ε il Is the lower limit of the strain interval epsilon in the ith test ir The upper limit of the strain interval at the ith test.
Step six: hybrid reliability assessment computation using global plus local quick solution algorithmAnd->An index;
hybrid reliability model:
wherein:for the truncated probability variable, Q is a non-probability variable, Φ (·) is a standard normal distribution function, and κ is a hybrid reliability index.
Step seven: the data acquired in real time are transmitted to the reduced order model in real time, so that the updating of the reduced order model and the error correction strategy is realized, and finally, the updating of the digital twin model is realized;
step eight: the strain error data is obtained in real time through a digital twin model updated in real time, and the variable strain parameters of the non-probability interval are updated;
step nine: judging whether the test is finished;
step ten: if the test is finished, judging whether all the tests are finished, if the next test is continued to be carried out, updating the intensity distribution parameters of the truncated probability variable and the elastic modulus parameters of the non-probability interval variable;
uncertainty description of intensity: and (3) carrying out normal fitting on the test data to obtain an intensity normal distribution curve, and considering the upper limit and the lower limit of the intensity to consider the intensity as a tail-biting probability variable. The real-time update of the tail-biting probability variable is realized by adding the intensity value to the intensity sample to be re-fitted after the stretching test of the single sample is finished, wherein the histogram of the intensity (R) frequency distribution measured by the stretching test of the first 22 composite material standard samples is shown in fig. 2, and the normal distribution curve is shown in fig. 3, and the tail-biting probability distribution of the intensity (R) measured by the stretching test of the first 22 composite material standard samples is shown in fig. 3.
Uncertainty description of elastic modulus: the non-probability interval of the elastic modulus is updated by considering calculation errors of stress caused by errors of the load sensor and finally transmitting the errors to the elastic modulus.
Step eleven: and repeating the steps six to eleven until the reliability digital twin prediction program is ended after all the tests are completed, and completing the reliability digital twin process, thereby realizing the real-time reliability evaluation. FIG. 4 shows the reliability index of 23 rd composite standard specimen in the example of the present inventionTime-series change curves of (2). FIG. 5 shows the reliability index +.>Time-series change curves of (2).
The foregoing is only a specific example of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present invention, and the changes and substitutions are intended to be covered by the scope of the present invention. The scope of the invention should therefore be determined by the following claims.
Claims (6)
1. The digital twin parameter updating process and method for the reliability of the composite material structure are characterized by comprising the following steps:
step one: acquiring data, namely acquiring test data by using data acquisition equipment;
step two: taking sensor errors, fault physical model prediction errors and digital twin model prediction errors into consideration, carrying out uncertainty analysis, and determining uncertainty variable parameters;
step three: storing the test data acquired in the first step and the uncertainty variable parameters in the second step into a database for data management;
step four: combining the fault physical model and the data driving model, and establishing a reliability digital twin model by combining the hybrid reliability model under a structural response prediction digital twin frame established based on a database;
step five: determining the interval range of a non-probability interval variable based on strain error analysis in the digital twin model, determining the interval range of elastic modulus based on sensor error analysis, and determining the intensity truncated distribution according to test results;
step six: performing reliability evaluation calculation and indexes by using a global and local quick solving algorithm;
step seven: the data acquired in real time are transmitted to the reduced order model in real time, so that the updating of the reduced order model and the error correction strategy is realized, and finally, the updating of the digital twin model is realized;
step eight: acquiring strain error data in real time through a digital twin model updated in real time, and updating non-probability interval variable strain parameters;
step nine: judging whether the test is finished;
step ten: if the test is finished, judging whether all the tests are finished, if the next test is continued to be carried out, updating the intensity distribution parameters of the truncated probability variable and the elastic modulus parameters of the non-probability interval variable;
step eleven: and repeating the steps six to eleven until the reliability digital twin prediction program is ended after all the tests are completed, and completing the reliability digital twin process, thereby realizing the real-time reliability evaluation.
2. A composite structure reliability digital twin parameter update procedure and method according to claim 1, in which in step two, variable parameters are described using truncated probability variables if the sample data is sufficient and non-probability interval variables if the number of samples is lacking.
3. The process and method for updating the digital twin parameters of the reliability of the composite structure according to claim 1, wherein in the fourth step, the digital twin model of the reliability is built by considering real-time prediction errors of various models, particularly by considering real-time updating of parameters, after the integration and reduction of the physical model of the fault and the data-driven model.
4. The method and process for updating digital twin parameters for structural reliability of composite material according to claim 1, wherein in the sixth step, the global and local rapid solution algorithm is based on a genetic algorithm and a gradient descent method, and a global optimization algorithm or a local solution algorithm can be selected according to variables and a limit state equation.
5. The process and method for updating digital twin parameters of structural reliability of composite material according to claim 1, wherein in the step eight, the strain is used as a non-probability interval variable to implement real-time updating of structural reliability parameters in a digital twin model in consideration of a prediction error given real-time strain non-probability interval.
6. The process and method for updating digital twin parameters of structural reliability of composite material according to claim 1, wherein in the step ten, the strength parameter is considered as a tail-biting probability variable because it accumulates a certain number of samples in the past test, and the real-time updating of the tail-biting probability variable is realized by adding the strength value to the distribution of re-fitting strength in the strength samples after the end of the tensile test of a single sample; the non-probability interval of the elastic modulus is updated by considering calculation errors of stress caused by errors of the load sensor and finally transmitting the errors to the elastic modulus.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117665221A (en) * | 2024-02-01 | 2024-03-08 | 江苏镨赛精工科技有限公司 | Performance detection method and system for composite material product |
| CN117665221B (en) * | 2024-02-01 | 2024-05-24 | 江苏镨赛精工科技有限公司 | Performance detection method and system for composite material product |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117665221A (en) * | 2024-02-01 | 2024-03-08 | 江苏镨赛精工科技有限公司 | Performance detection method and system for composite material product |
| CN117665221B (en) * | 2024-02-01 | 2024-05-24 | 江苏镨赛精工科技有限公司 | Performance detection method and system for composite material product |
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