A digital-physical fusion test method for aerospace equipment under extreme working conditions

Abstract

The present invention discloses a digital-physical fusion test method for aerospace equipment under extreme working conditions, which belongs to the fields of electronic engineering and computer science. It includes: designing a digital modeling module for aerospace equipment, extracting the actual characteristics of aerospace equipment, constructing a digital model using a digital modeling method, and completing the iteration and verification of the digital model of aerospace equipment based on the relevant characteristics and historical data of the aerospace equipment; designing a digital-physical fusion test module under extreme working conditions, performing dynamic generalization in the digital model of the existing aerospace equipment, and secondly, optimizing the dynamic model using the test data under extreme working conditions of the aerospace equipment, and finally performing actual physical verification on the optimized model to form a digital model control system under extreme working conditions, introduce digital twin technology, establish closed-loop feedback between the model and the equipment, and adjust the operating conditions of the equipment. The present invention can ensure the safety of aerospace equipment, reduce the cost of physical testing, improve test efficiency, and adjust the operating conditions of the equipment in real time.

Description

Technical Field

[0001] The present invention belongs to the fields of electronic engineering and computer science, and specifically relates to a digital-physical fusion test method for aerospace equipment under extreme working conditions. Background Art

[0002] Aerospace equipment holds crucial strategic and technological significance in modern society. Not only does it play a key role in the military, it is also widely used in scientific research, civilian communications, and global security. Its application faces numerous challenges, including high and low temperatures, high and low pressures, vibration, shock waves, radiation, and vacuum. Simulating these extreme operating conditions in existing experimental environments is difficult, resource-intensive, and yields low returns. Furthermore, traditional testing methods often assume stable operating conditions. In reality, the operating environment of aerospace equipment can be subject to dynamic changes, making the results obtained using these methods unreliable. Summary of the Invention

[0003] The technical problem to be solved by the present invention is: to provide a method for digital-physical fusion testing of aerospace equipment under extreme working conditions. The method covers the design of a digital modeling module for aerospace equipment and a digital-physical fusion testing module under extreme working conditions. To a certain extent, the method can ensure the safety of aerospace equipment, reduce the cost of physical testing, improve the efficiency of testing, and adjust the operating conditions of the equipment in real time. To this end, the present invention discloses a method for digital-physical fusion testing of aerospace equipment under extreme working conditions. By designing a digital modeling module for aerospace equipment and a digital-physical fusion testing module under extreme working conditions, and with the help of the digital-physical fusion testing method, the method can ensure the safety of aerospace equipment, reduce the cost of physical testing, improve the efficiency of testing, and adjust the operating conditions of the equipment in real time.

[0004] The present invention solves the technical problem by adopting the following technical solution: a digital-physical fusion test method for aerospace equipment under extreme working conditions, comprising:

[0005] Step (1) constructing a digital modeling module for aerospace equipment, using a digital modeling method to construct a digital model of aerospace equipment, and using the relevant characteristics and historical data of aerospace equipment to complete the iteration and verification of the digital model of aerospace equipment;

[0006] Step (2) constructs a digital-physical fusion test module under extreme working conditions, which is used to dynamically generalize the constructed digital model, conduct preliminary verification using historical data, and verify the feasibility of the model using actual physics.

[0007] The advantages of the present invention compared with the prior art are:

[0008] Using digital-physical fusion experimental methods, the actual characteristics of aerospace equipment are extracted, and a digital model is constructed using digital modeling methods. Based on the relevant characteristics and historical data of aerospace equipment, the digital model of aerospace equipment is iterated and verified. On this premise, dynamic generalization is performed within the existing digital model of aerospace equipment, and preliminary data verification and actual physical verification of the optimized model are carried out to form a digital model control system under extreme operating conditions. Digital twin technology is introduced to establish a closed-loop feedback loop between the model and the equipment to adjust the equipment's operating conditions. This can, to a certain extent, ensure the safety of aerospace equipment, reduce physical testing costs, improve testing efficiency, and adjust the equipment's operating conditions in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is a flowchart of a digital-physical fusion test method for aerospace equipment under extreme working conditions according to the present invention. DETAILED DESCRIPTION

[0010] In order to make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate 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 may be combined with each other as long as they do not conflict with each other. To achieve the above-mentioned objectives, the present invention adopts the following technical solutions.

[0011] The present invention relates to a method for digital-physical fusion testing of aerospace equipment under extreme working conditions, including the design of a digital modeling module for aerospace equipment and a digital-physical fusion testing module under extreme working conditions. This method can ensure the safety of aerospace equipment to a certain extent, reduce physical testing costs, improve testing efficiency, and adjust the operating conditions of the equipment in real time.

[0012] The flow chart of the present invention is as follows Figure 1 As shown, taking the blade disk of an aerospace aircraft engine as an example, the specific implementation method is as follows:

[0013] Figure 1 The project includes the construction of aerospace equipment digital modeling module and a digital-physical fusion test module under extreme working conditions;

[0014] Step (1) Construct a digital-real fusion module for aerospace equipment, which is specifically implemented as follows;

[0015] The actual physical characteristics of the blisk are extracted, and a digital model of the blisk is constructed using digital modeling methods. The blisk module can be divided into three functional modules: the blade module, the disk module, and the connection module. The blade module is further subdivided into two submodules: the shroud and the blade airway surface. The disk module has one submodule, representing the main disk body. The connection module includes two submodules, representing the hub and blade root regions. Physical characteristics are extracted for each submodule, including material properties, thermodynamic properties, and dynamic properties. The physical characteristics of the digital model include the material properties of the blisk, which is made of materials such as titanium alloys and nickel-based superalloys; the vibration characteristics of the blisk under operating conditions; and the mechanical characteristics of the blisk, which is subjected to both high temperatures and mechanical loads during operation and exhibits significant coupling between thermal and mechanical stresses. Mathematical modeling is then performed on the physical characteristics of the modules, extracting the material strength equations, dynamic equations, vibration modal equations, stress distribution formulas, and governing equations during module operation. Finally, the physical and mathematical characteristics of each module are integrated to form a holistic digital model. Regarding digital modeling methods, we mainly adopt data-driven digital modeling methods, use data to quickly build models, and can handle nonlinear and multivariable coupling problems. We use neural network and principal component analysis methods to build digital models.

[0016] The digital model of the blisk is iterated and verified based on the inherent relevant characteristics and historical data of the blisk. The inherent relevant characteristics are the physical dimensions and material properties of the blisk. That is, the relevant characteristics of the blisk are used as the properties of the digital model of the blisk. The interfaces and data inputs of each device in the historical data are used as the input of the digital model. The output of the historical data is compared with the output of the digital model. If the average accuracy of the state curve corresponding to the above mathematical and physical characteristics simulated by the digital model at normal temperature and pressure is within the design allowable range, with an average error of less than 5% and a peak error of less than 10%, the correctness of the digital model can be determined. Otherwise, the digital model of the blisk is corrected.

[0017] Step (2) constructs a digital-physical fusion test module under extreme working conditions, which is specifically implemented as follows;

[0018] The constructed digital model of the blade disk is generalized and optimized. That is, the established digital model is generalized using causal learning, transfer learning, and adaptive algorithms to obtain a digital dynamic model. The specific algorithm implementation can be shown as follows.

[0019] First, the causal relationship between the model input features and output results is analyzed through causal learning methods, thereby eliminating irrelevant variables or interfering features, improving the model's generalization ability, and constructing a causal graph:

[0020] ,

[0021] Nodes V represent variables, and edges E represent causal relationships. Causal relationships can be expressed as temperature corresponding to thermal stress, rotational speed corresponding to centrifugal force, and pressure corresponding to mechanical stress. A causal equation is then constructed, and the instrumental variable method is used to eliminate spurious correlated variables:

[0022] ,

[0023] Among them, Y is the output variable, which is the stress distribution, and X is the input feature, which is temperature, pressure, and rotation speed. is the error term, is the causal effect coefficient. Only variables with a direct causal relationship with the target output are selected, and a digital model is trained through a deep learning neural network to obtain a model that satisfies causality.

[0024] Next, we construct a feature map based on the variables selected by causal learning, and align the source and target domain data distributions using the Maximum Mean Difference (MMD) method. We then train the migration model and finally fine-tune the parameters according to the following formula:

[0025] ,

[0026] is the parameter set of the model, k is the current number of iterations, Represents the learning rate, which controls the step size of each gradient update. Represents the model parameters The calculated loss function gradient is used to update the parameters.

[0027] Finally, the migrated model is dynamically optimized based on the real-time error function and adjusted according to the following formula:

[0028] ,

[0029] is the real-time error function. In addition, for non-neural network parameters, Kalman filtering is used for optimization. First, state prediction is performed. The following is the state prediction function:

[0030] ,

[0031] According to the current status The predicted state at the next moment represents the parameter estimate at time k+1. A is the state transition matrix, which represents the evolution of the parameter state from time k to time k+1. B is the control input matrix, which describes the impact of external control on the state. It is the control input, which represents the adjustment of the state by external effects (such as the influence of sensor data or feedback data).

[0032] Then the state is updated according to the prediction function. The state update function is as follows:

[0033] ,

[0034] The updated model parameters are corrected by combining the predicted state and measurement errors. is the Kalman gain, which weighs the influence of the predicted value and the measured value. The larger it is, the greater the impact on the measured value. is the real observation value in the target domain (such as the real-time stress value measured by the sensor). C is the measurement matrix, which is used to map the predicted state to the observation space.

[0035] Dynamically adjust the variable relationships in the causal graph based on environmental data to respond to real-time environmental changes and ensure the validity of transfer learning results in the target domain. For example, when high temperatures cause a decrease in the elastic modulus of a material, thermal and mechanical stresses are recalculated.

[0036] A preliminary data verification is performed on the digital dynamic model, that is, the input of the historical data based on extreme working conditions in the original blade disk is used as the input of the digital dynamic model, and the output data under extreme working conditions is compared with the output data of the digital dynamic model. If the accuracy of the output of the digital dynamic model meets the allowable range of the blade disk design, the overall average error is less than 5%, and the peak error is less than 10%, then the correctness of the digital dynamic model is confirmed. Otherwise, the digital dynamic model needs to be recalibrated.

[0037] The digital dynamic model is physically validated by introducing an intelligent detection method on the blade disk. This intelligent detection method achieves comprehensive real-time monitoring and data processing of the blade disk's operating status by building a multimodal sensor network and edge computing methods. The multimodal sensor network combines multiple sensing technologies, using environmental sensors to monitor external conditions, including wind speed, humidity, temperature, and pressure, and vibration sensors to measure the mechanical vibration characteristics of the aerospace equipment and detect anomalies. These sensors are networked using the high-bandwidth, low-latency TSN (Time-Sensitive Networking) communication protocol to achieve data interconnection and synchronous acquisition. Edge computing methods are used to perform real-time data processing directly at the sensor end. Spectral analysis of high-frequency data, such as vibration, temperature, and acceleration, is performed using low-latency Fast Fourier Transforms (FFTs) to remove noise and outliers, reduce latency, and improve feedback speed. Dynamic data is fed into the trained model, and the output data is compared in real time to mitigate the randomness and inaccuracies of historical data. If the output data accuracy is within the design tolerances for the aerospace equipment, with an overall average error of less than 5% and a peak error of less than 10%, the digital dynamic model is validated.

[0038] A digital model control system for the bladed disk was constructed, specifically an extreme operating condition system for the bladed disk. The digital twin approach was introduced. The corresponding bladed disk was scanned using a 3D scanner. The resulting point cloud modeling data was converted into FBX format and imported into Unity3D software to construct a simulation model. The model's data interfaces were bound to the corresponding data interfaces of the actual aerospace equipment to enable data transmission, including real-world temperature, humidity, and equipment timing data, establishing a data connection between the virtual model and the actual equipment. The model's output data and command interfaces were bound to those of the actual equipment, establishing a data and command connection between the virtual model and the actual equipment. The equipment's operating state can be controlled by controlling the model's output commands. The specific control process can be described as follows: The digital dynamic model serves as the core algorithm module. Through real-time data updates, a closed-loop feedback system is established between the virtual model and the actual equipment. Real-time data from the aerospace equipment under extreme operating conditions is fed into the dynamic digital model for predictive analysis. The model's consistency with the actual equipment timing data is compared, ensuring that the average error of the curve is less than 5%. If the predicted time series data error is greater than 5%, and provided the previous test error is less than 5%, the actual time series error and corresponding environmental data are re-input into the digital dynamic model, and the algorithm parameters, including hyperparameters related to the learning rate and number of neural network layers, are readjusted to ensure that the error of the existing data is back within the 5% range. The model is then used to predict the operating curve of the blade disk under the corresponding high temperature and high pressure conditions. If the operating curve can ensure that the decline in the blade disk fatigue characteristic curve under long-term operation is less than 20% of the normal curve, no data needs to be changed and the instruction is directly output. If the fatigue characteristic curve declines faster than 20% at normal temperature and pressure, the model's blade disk speed and other parameters are readjusted to predict the fatigue curve until it is less than 20%. The corresponding speed is then output as an instruction to the corresponding actual equipment, completing the construction of the digital model control system for aerospace equipment.

[0039] In summary, the present invention discloses a digital-physical fusion test method for aerospace equipment under extreme working conditions, including the design of a digital modeling module for aerospace equipment and a digital-physical fusion test module under extreme working conditions. It can ensure the safety of aerospace equipment to a certain extent, reduce the cost of physical testing, improve test efficiency, and adjust the operating conditions of the equipment in real time.

[0040] The contents not described in detail in the specification of the present invention belong to the prior art known to those skilled in the art.

[0041] The above 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 principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A digital-realistic fusion test method for aerospace equipment under extreme working conditions, characterized by: include: Step (1) constructing a digital modeling module for aerospace equipment, using a digital modeling method to construct a digital model of aerospace equipment, and using the relevant characteristics and historical data of aerospace equipment to complete the iteration and verification of the digital model of aerospace equipment; Step (2): construct a digital-physical fusion test module under extreme working conditions, which is used to dynamically generalize the constructed digital model, conduct preliminary verification using historical data, and verify the feasibility of the model using actual physics; The specific implementation of the digital-physical fusion test module under extreme working conditions in step (2) is as follows: Step (2.1) Generalize and optimize the digital model of aerospace equipment. That is, use causal learning, transfer learning, and adaptive algorithms to generalize the established digital model to obtain a digital dynamic model. The specific algorithm is implemented as follows: First, the causal relationship between the model input features and output results is analyzed through causal learning methods, thereby eliminating irrelevant variables or interfering features, improving the model's generalization ability, and constructing a causal graph; ， Among them, the node V represents the variable, the edge E represents the causal relationship, and then the causal equation is constructed, and the instrumental variable method is used to eliminate the pseudo-correlated variables; ， Among them, Y is the output variable, X is the input feature, is the error term, is the causal effect coefficient; only variables with a direct causal relationship with the target output are selected, and the digital model is trained through a deep learning neural network to obtain a model that satisfies the causal relationship; Next, we construct a feature map based on the variables selected by causal learning. We align the data distributions of the source and target domains using the maximum mean difference method. We then train the migration model and fine-tune the parameters according to the following formula: ， is the parameter set of the model, k is the current number of iterations, Represents the learning rate, which controls the step size of each gradient update. Represents the model parameters The calculated loss function gradient is used to update the parameters; Finally, the migrated model is dynamically optimized based on the real-time error function and adjusted according to the following formula: ， is the real-time error function; for non-neural network parameters, Kalman filtering is used for optimization. First, state prediction is performed. The following formula is the state prediction function: According to the current status The predicted state at the next moment represents the parameter estimation at the k+1th moment. A is the state transfer matrix, which represents the evolution law of the parameter state from moment k to moment k+1. B is the control input matrix, which is used to describe the influence of external control on the state. is the control input, which represents the adjustment of the state by external action; Then the state is updated according to the prediction function; the state update function is shown as follows: ， The updated model parameters are corrected by combining the predicted state and measurement error. is the Kalman gain, is the true observation value in the target domain, and C is the measurement matrix used to map the predicted state to the observation space; Dynamically adjust the variable relationships in the causal graph based on environmental data to respond to real-time environmental changes and ensure the effectiveness of transfer learning results in the target domain; Step (2.2) performs preliminary data verification on the digital dynamic model. This involves using the original historical data of the aerospace equipment based on extreme working conditions as the input of the digital dynamic model. The output data under extreme working conditions is compared with the output data of the digital dynamic model. If the accuracy of the output of the digital dynamic model is within the allowable range of the aerospace equipment design, the overall average error is less than m%, and the peak error is less than n%, where n>m>0, then the correctness of the digital dynamic model is confirmed. Otherwise, the digital dynamic model is recalibrated. Step (2.3) Conduct actual physical verification of the digital dynamic model, that is, introduce intelligent detection methods on aerospace equipment. This intelligent detection method realizes all-round real-time monitoring and data processing functions of the operating status of aerospace equipment by constructing a multimodal sensor network and edge computing methods. The multimodal sensor network combines multiple sensing methods and forms a network of these sensors through the high-bandwidth, low-latency TSN time-sensitive network communication protocol to achieve data interconnection and synchronous collection. Through edge computing methods, real-time data processing is performed directly on the sensor end. The low-latency fast Fourier transform (FFT) is used to perform spectral analysis on high-frequency data such as vibration, temperature, and acceleration to eliminate noise or outliers, reduce delays and improve feedback speed. The dynamic data is input into the trained digital dynamic model, and the output data is compared in real time to avoid the randomness and inaccuracy of historical data. If the accuracy of the output data is within the allowable range of the aerospace equipment design, the overall average error is less than m%, and the peak error is less than n%, where n>m>0, the correctness of the digital dynamic model is verified. Step (2.4) builds a digital model system for aerospace equipment, that is, builds an extreme working condition system for aerospace equipment, introduces the digital twin method, uses a 3D scanner to scan the corresponding aerospace equipment, and uses the scanned point cloud modeling data to build a simulation model. The data interface of the simulation model is bound to the corresponding data interface of the actual aerospace equipment to realize data transmission, including temperature and humidity under real conditions, as well as the timing working data of the equipment, to realize the data connection between the virtual model and the actual equipment, and bind the output data and command interface of the simulation model to the data and command interface of the actual equipment to realize the data and command connection between the virtual model and the actual equipment. The digital dynamic model is used as the algorithm module of the digital model system, and the working status of the equipment is controlled by controlling the output command of the simulation model. Through real-time data update, a closed-loop feedback system is established between the virtual model and the actual equipment to complete the construction of the digital model control system of aerospace equipment.

2. The method for digital-physical fusion testing of aerospace equipment under extreme working conditions according to claim 1 is characterized in that: The specific implementation of the aerospace equipment digital modeling module in step (1) is as follows: Step (1.1) Extract the actual physical characteristics of the aerospace equipment and use the digital modeling method to build a digital model of the aerospace equipment; in the digital modeling, first divide the aerospace equipment into n functional modules, and each functional module is further divided into sub-modules, extract the physical characteristics of each sub-module, conduct mathematical modeling based on the physical characteristics of each sub-module, and finally form an overall digital model by integrating the physical characteristics and mathematical characteristics of each module. For digital modeling methods, a data-driven digital modeling method is adopted, using data to build models, dealing with nonlinear and multivariable coupling problems, and using neural network and principal component analysis methods to build digital models; Step (1.2) Complete the iteration and verification of the digital model of aerospace equipment based on the relevant characteristics and historical data of aerospace equipment, that is, use the relevant characteristics of aerospace equipment as the attributes of the digital model of aerospace equipment; use the interface and data input of each equipment in the historical data as the input of the digital model, and compare the output of each aerospace equipment in the historical data with the output of the digital model. If the average accuracy of the state curve corresponding to the mathematical and physical characteristics simulated by the digital model at normal temperature and pressure is within the design allowable range, the average error is less than m%, and the peak error is less than n%, where n>m>0, then the correctness of the digital model can be determined. Otherwise, the digital model of the aerospace equipment is corrected.

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