Method and device for identifying thermodynamic property parameters of material

Abstract

The embodiment of the invention relates to the technical field of material performance testing, in particular to a method and device for identifying thermodynamic performance parameters of a material. The method comprises the following steps: uniformly heating a target material from a current temperature to a target temperature, and obtaining target temperature field data and first target displacement field data of the target material in a heating process; a preset displacement load is applied to the target material at the target temperature, and second target displacement field data of the target material in the displacement load applying process are obtained; inputting the target temperature field data, the first target displacement field data and the second target displacement field data into a trained thermodynamic performance parameter identification model to obtain an output target elastic modulus and a target constant parameter; and obtaining a target thermal expansion coefficient of the target material based on the target constant parameter and the target temperature field data. According to the technical scheme, the identification precision of the thermal expansion coefficient and the elastic modulus can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of material performance testing, in particular to a method and device for identifying thermodynamic performance parameters of a material. BACKGROUND

[0002] In the structural design of hypersonic vehicles, the thermal expansion coefficient and elastic modulus of ceramic-based composite materials in an ultrahigh-temperature environment (usually higher than 1500℃) are key thermodynamic performance parameters for thermal stress analysis, structural matching design, and service life prediction. Therefore, it is necessary to measure accurate thermodynamic performance parameters in the above extreme environments such as ultrahigh temperature.

[0003] In the prior art, the thermodynamic performance parameters of a material are measured by simulating an ultrahigh-temperature environment and applying a displacement load to the material, and using a thermal dilatometer. However, in a high-temperature environment, the thermal deformation (dominated by the thermal expansion coefficient) and the mechanical deformation (dominated by the elastic modulus) of the material will affect each other, resulting in low identification accuracy of the thermal expansion coefficient and the elastic modulus.

[0004] Therefore, there is an urgent need for a new technical solution to solve the above technical problems. SUMMARY

[0005] The present application provides a method and device for identifying thermodynamic performance parameters of a material, which can improve the identification accuracy of the thermal expansion coefficient and the elastic modulus.

[0006] In a first aspect, the present application provides a method for identifying thermodynamic performance parameters of a material, comprising: uniformly heating a target material from a current temperature to a target temperature, and obtaining target temperature field data and first target displacement field data of the target material during the heating process; applying a preset displacement load to the target material at the target temperature, and obtaining second target displacement field data of the target material during the application of the displacement load; inputting the target temperature field data, the first target displacement field data, and the second target displacement field data into a trained thermodynamic performance parameter identification model, to obtain an output target elastic modulus and a target constant parameter, the target constant parameter being a constant parameter of a one-variable quadratic equation between the thermal expansion coefficient and the temperature value; obtaining a target thermal expansion coefficient of the target material based on the target constant parameter and the target temperature field data.

[0007] In a second aspect, the present application provides a device for identifying thermodynamic performance parameters of a material, comprising: a heating experiment module, which uniformly heats a target material from a current temperature to a target temperature, and obtains target temperature field data and first target displacement field data of the target material during the heating process; a tensile experiment module, connected with the heating experiment module, configured to apply a preset displacement load to the target material at the target temperature to obtain second target displacement field data of the target material during application of the displacement load; a parameter inversion module, connected with the tensile experiment module, configured to input the target temperature field data, the first target displacement field data and the second target displacement field data into the trained thermodynamic performance parameter identification model to obtain output target elastic modulus and target constant parameter, the target constant parameter being a constant parameter of a quadratic equation between a thermal expansion coefficient and a temperature value; an expansion coefficient calculation module, connected with the parameter inversion module, configured to obtain the target thermal expansion coefficient of the target material based on the target constant parameter and the target temperature field data.

[0008] In a third aspect, the present application provides an electronic device including a memory and a processor, the memory storing a computer program, and the processor implementing the method of the first aspect of the present application when executing the computer program.

[0009] In a fourth aspect, the present application provides a computer readable storage medium storing a computer program, which, when executed in a computer, causes the computer to perform the method of the first aspect of the present application.

[0010] The embodiments of the present application provide a material thermodynamic performance parameter identification method and device, which can obtain temperature field and displacement field data in heating and tensile processes through one experiment, generate training samples in combination with finite element simulation, decouple and split the heating process and the tensile process, input the heating process and the tensile process into two branches of a training model respectively, obtain a trained thermodynamic performance parameter identification model, input the collected temperature field and displacement field data in the experiment into the thermodynamic performance parameter identification model for parameter inversion, and obtain the thermodynamic performance parameters of the material, thereby improving the identification accuracy of the thermal expansion coefficient and the modulus and reducing the identification cost. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0012] Figure 1 is a material thermodynamic performance parameter identification method flowchart provided by an embodiment of the present application; Figure 2 is a hardware architecture diagram of an electronic device provided by an embodiment of the present application; Figure 3 is a material thermodynamic performance parameter identification device structure diagram provided by an embodiment of the present application; Figure 4 is a schematic diagram of an experimental device during heating and displacement load application; Figure 5 is a schematic diagram of temperature and displacement load changes during the experiment; Figure 6 is a framework diagram during the training of the material thermodynamic performance parameter identification model. DETAILED DESCRIPTION

[0013] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0014] For reference Figure 1 The embodiments of the present application provide a material thermodynamic performance parameter identification method, which comprises the following steps: Step 100: uniformly heating a target material from a current temperature to a target temperature to obtain target temperature field data and first target displacement field data of the target material during the heating process; Step 102: applying a preset displacement load to the target material at the target temperature to obtain second target displacement field data of the target material during the displacement load application process; Step 104: inputting the target temperature field data, the first target displacement field data and the second target displacement field data into a trained thermodynamic performance parameter identification model to obtain output target elastic modulus and target constant parameters; The target constant parameters are constant parameters of a monomial quadratic equation between the thermal expansion coefficient and the temperature value. Step 106: obtaining a target thermal expansion coefficient of the target material based on the target constant parameters and the target temperature field data.

[0015] In the embodiment of the present application, the heating experiment and the stretching experiment of the material are carried out step by step, and the target temperature field data and the first target displacement field data in the heating process and the second target displacement field data in the displacement load process are collected respectively. The data obtained through the above experiments are used to establish a high-fidelity finite element model. The sample data set of the thermodynamic performance parameter identification model is obtained by simulating the experiment process with the finite element model. In the training stage, the sample data set is divided into the sample data set of the heating stage and the sample data set of the stretching stage, so as to train the thermodynamic performance parameter identification model. The experimental observation response (target temperature field data, first target displacement field data and second target displacement field data) is input into the thermodynamic performance parameter identification model as prior information, and the target thermodynamic performance parameters (target thermal expansion coefficient and target elastic modulus) of the target material are obtained. The multi-source information such as temperature field and displacement field is fused, and the simulation data of the finite element model is used to train the thermodynamic performance parameter identification model, so that the inversion of the thermal expansion coefficient and the elastic modulus is realized end to end.

[0016] It should be noted that the thermal expansion coefficient is a variable parameter that changes with temperature. In the embodiment of the present application, a quadratic equation between the thermal expansion coefficient and the temperature value is established, the constant parameter in the quadratic equation is taken as the output of the thermodynamic performance parameter identification model, and then the thermal expansion coefficient corresponding to each temperature value of the material is calculated according to the constant parameter and the temperature field data. The elastic modulus is directly taken as the output of the thermodynamic performance parameter identification model.

[0017] In an embodiment of the present application, the sample data set of the thermodynamic performance parameter identification model is obtained by the following method: The simulation constant parameter and the simulation elastic modulus are extracted within the preset parameter range conforming to the physical constraint of the category to which the target material belongs; The simulation constant parameter and the simulation elastic modulus are taken as the output data set of the sample data set; The heating process and the displacement load process of the target material are simulated by the finite element model, and the simulation temperature field data and the simulation displacement field data corresponding to each group of simulation constant parameter and simulation elastic modulus are obtained; The simulation temperature field data and the simulation displacement field data are taken as the input data set of the sample data set.

[0018] In the embodiment, a finite element model of the target material is established, the same conditions (consistent with the experimental conditions and environmental conditions of the heating stage and the displacement load application stage) are set, the heating stage and the displacement load application stage are simulated by the finite element model simulation, and simulation temperature field data and simulation displacement field data are obtained. The simulation temperature field data and the simulation displacement field data are compared with the target temperature field data, the first target displacement field data and the second target displacement field data. If all parameter errors are less than 10%, it is considered that the finite element model precision meets the requirements, and the subsequent steps are continued to be executed. If the error is greater than or equal to 10%, the condition parameters of the finite element model are adjusted and re-simulated until the precision meets the requirements. The sample data set is obtained by the finite element model: the thermal expansion coefficient and the elastic modulus are set as variable parameters, a plurality of different simulation constant parameters and simulation elastic moduli are selected, and the simulation temperature field data and the simulation displacement field data corresponding to the finite element model simulation are obtained.

[0019] Taking the needled C / C composite material as an example, the needled C / C composite material is an anisotropic material, and the thermal expansion coefficients and the elastic moduli in the three directions are all different. In the embodiment, only the thermal expansion coefficients and the elastic moduli in the first and second directions are considered, and the normal parameter is not considered. Considering that the thermal expansion coefficient is a parameter that changes depending on the temperature, a quadratic equation is established: α 1 =a 0 +a 1 *T+a 2 *T 2 , α 2 =b 0 +b 1 *T+b 2 *T 2 , α 1 is the thermal expansion coefficient in the first direction, α 2 is the thermal expansion coefficient in the second direction, a 0 , a 1 , a 2 , b 0 , b 1 and b 2 is a constant parameter, T is a temperature value. The elastic modulus in the first direction isE 1 , the elastic modulus of the second direction is E 2 In a pre-set reasonable range, random sampling (extracted within the pre-set parameter range conforming to the physical constraints of the target material category), in order to make the parameters meet the physical constraints (the thermal expansion coefficient is greater than 3x10 6 and less than 10x10 6 , the absolute value of the difference between adjacent points is greater than 0.5x10 6 , usually there is no case of sharp fluctuation of the value, and the value is uniformly selected in the range). Wherein, a 0 The value is U (0.8x10 -6 , 3.0x10 -6 ), a 1 The value is U (0.5x10 -9 , 2.5x10 -9 ), a 2 The value is U (-0.1x10 -12 , 0.1x10 -12 ), b 0 The value is U (1.5x10 -6 , 6x10 -6 ), b 1 The value is U (0.8x10 -8 , 3.5x10 -8 ), b 2 The value is U (-0.15x10 -12 , 0.15x10 -12 ), E 1 The value is U (6x10 10 , 2x10 11 ), E 2 The value is U (4x10 10 , 1.2x10 11 ). UUniform Distribution, which means that the probability of any value within the specified interval is equal. After selecting the simulation constant parameters and simulation elastic modulus of the random sampling simulation, the simulation process under different parameter samples is calculated by writing python code. The strain field at 25℃, 200℃, 400℃, 600℃, 800℃, 1000℃, 1200℃, 1400℃, 1600℃ during the heating process and the strain field at five positions of 0.02%, 0.04%, 0.06%, 0.08%, 0.10% average strain in the stretching process (the temperature is usually kept at 1600℃ or needs to be kept at 1600℃ in the ideal state) are output. The average strain in the stretching process needs to be subtracted from the strain in the 0th frame to realize the decoupling of heat and force. The data is saved as a.npz format file, and the result is used as the sample data set (including the training set and the validation set) for the next step.

[0020] In an embodiment of the present application, the training process of the thermodynamic performance parameter identification model is as follows: The simulation temperature field data and simulation displacement data obtained by the simulation of the heating process are input into the heating stage branch of the double-channel convolutional neural network; The simulation displacement field data obtained by the simulation of the applied displacement load process are input into the stretching stage branch of the double-channel convolutional neural network; The feature data extracted by the heating stage branch and the stretching stage branch are obtained; The feature data of the heating stage branch and the stretching stage branch are coupled and fused through the feature fusion layer of the double-channel convolutional neural network to obtain a multi-source feature map; The predicted values of the change parameters and the elastic modulus are output based on the multi-source feature map, and the simulation change parameters and the simulation elastic modulus in the sample data set are used as labels to calculate the error between the predicted values and the labels; Iterative training is performed until the error meets the preset convergence condition, and a trained thermodynamic performance parameter identification model is obtained.

[0021] In this embodiment, the deep learning network model involved is a multi-input convolutional neural network (CNN) structure, and the network architecture is as shown in Figure 6 The simulation temperature field data and simulation displacement field data obtained in the previous step are preprocessed to obtain the response field of the heating stage and the stretching stage, and the corresponding labels (simulation constant parameters and simulation elastic modulus). The input X of the thermodynamic performance parameter identification model is defined (including two branches), the full-field strain distribution of the heating stage and the temperature distribution are X heating, and the full-field strain distribution of the stretching stage is X tensile. The output Y of the thermodynamic performance parameter identification model is defined as Y=[ a0 , a 1 , a 2 , b 0 , b 1 , b 2 , E 1 and E 2 The constructed complete sample dataset (X_heating, X_tensile, Y) is randomly divided into training and test sets in an 8:2 ratio. The training set is used for learning model parameters, and the test set is used to independently evaluate the model's generalization performance. Since the input data is full-field strain with a certain spatial structure, a dual-input CNN architecture is preferred. This dual-input CNN contains two independent convolutional encoder branches: a heating stage branch and a stretching stage branch. The input to the heating stage branch is X_heating, which in turn contains both the temperature field and the displacement field. It is further divided into two sub-branches to process these two types of data, each connected to three convolutional layers (each layer connected to a ReLU activation function and a 2x2 max-pooling layer). The features from these two sub-branches are then fused and concatenated to form the heating features. The input to the stretching stage branch is X_tensile, and its structure is similar to the heating branch, but without the feature fusion step. The thermal features and mechanical branch features are used for controlled information interaction through finite coupling layers to model the thermo-mechanical coupling effect in real materials. The network consists of two decoupled output heads. Thermal expansion coefficient output head: Primarily based on thermal characteristics, supplemented by coupling characteristics, it regresses the six parameters of thermal expansion coefficient through a fully connected layer; Modulus output head: Primarily based on mechanical characteristics, supplemented by coupling characteristics, it regresses Young's modulus through a fully connected layer. The final output layer corresponds to the eight parameters to be identified. a 0 , a 1 , a 2 , b 0 , b 1 , b 2 , E 1 and E 2 ].

[0022] In addition, through the Adam optimizer, the initial learning rate is set to 0.001, and the loss function is defined as the mean square error (MSE), that is, the sum of the squares of the differences between the predicted parameter values and the true parameter values. The training set data is input into the model according to a certain batch size (Batch Size = 32), forward propagation and back propagation are performed, network weights are iteratively optimized, and the loss function converges. After training, inference is performed on the test set, the average absolute percentage error between the predicted parameters and the true parameters is calculated, and the model is trained and evaluated.

[0023] In one embodiment of the present application, the target constant parameter is a one-dimensional quadratic equation between the thermal expansion coefficient and the temperature value: α 1 =a 0 +a 1 *T+a 2 *T 2 , α 2 =b 0 +b 1 *T+b 2 *T 2 , α 1 is the thermal expansion coefficient in the first direction, α 2 is the thermal expansion coefficient in the second direction, a 0 , a 1 , a 2 , b 0 , b 1 and b 2 is a constant parameter, T is a temperature value.

[0024] In this embodiment, the thermal expansion coefficient is a parameter that changes with temperature, and a one-dimensional quadratic equation is established: α 1 =a 0 +a 1 *T+a 2 *T 2 , α 2 =b0 +b 1 *T+b 2 *T 2 , α 1 is a thermal expansion coefficient in the first direction, α 2 is a thermal expansion coefficient in the second direction, a 0 , a 1 , a 2 , b 0 , b 1 and b 2 is a constant parameter, and T is a temperature value.

[0025] In an embodiment of the present application, based on a target constant parameter and target temperature field data, a target thermal expansion coefficient of a target material is obtained, comprising: based on the target constant parameter, a univariate quadratic equation in the first direction and a univariate quadratic equation in the second direction are obtained; based on the temperature field data, the univariate quadratic equation in the first direction and the univariate quadratic equation in the second direction, a target thermal expansion coefficient in the first direction and a target thermal expansion coefficient in the second direction are obtained.

[0026] In the present embodiment, the temperature field and displacement field data collected by experiments are input into the trained thermodynamic performance parameter identification model, and the target constant parameter and modulus are output, and then the target thermal expansion coefficient is obtained according to the target constant parameter and the temperature field data. The data collected by the high-speed camera in the actual experiment are preprocessed, and the full-field strain X'_heating in the heating stage and the full-field strain X'_tensile in the tensile stage corresponding to the actual sample are calculated. X'_heating and X'_tensile are input, and the thermodynamic performance parameter identification model is loaded, and the output of the model is the identification result of the material parameters of the sample: a 0 , a 1 , a 2 , b 0 , b 1 , b 2 , E 1 and E 2, and the target thermal expansion coefficient in the first direction and the target thermal expansion coefficient in the second direction are calculated α 1 、 α 2 ]。

[0027] In one embodiment of the present application, the target material is uniformly heated from the current temperature to the target temperature, and the target temperature field data and the first target displacement field data of the target material during the heating process are obtained, including: uniformly heating the target material after the pre-load is applied from the current temperature to the target temperature; obtaining temperature field data of the target material during the heating process; obtaining speckle change images of the target material during the heating process through an image acquisition device; obtaining first target displacement field data of the target material during the heating process based on the speckle change images.

[0028] In this embodiment, the experimental process is carried out through a temperature controller, a mechanical loading system and a vacuum chamber (as shown in *T+a ), which is used to control the heating process and the displacement load application process of the target material, and the measuring device includes an infrared thermal imager and a CCD camera, which are respectively used to obtain the temperature field and the displacement field (strain field) of the surface of the target material. As shown in *T+b , the temperature and displacement load change during the entire experimental process are shown, and the entire experiment is divided into three stages. In the first stage, a pre-load of 50N is applied, in the second stage, the pre-load is kept unchanged and is raised to 1200°C within 60s, and in the third stage, the temperature is kept unchanged and the displacement load is applied at a speed of 0.5mm / min. Specifically, before heating, a pre-load of 50N is applied to the sample, and then the sample is uniformly heated from room temperature to the target temperature of 1600°C by using the instrument with power heating, during which the infrared thermal imager is used to record the temperature field change of the sample, and the CCD high-speed camera is used to shoot the speckle change of the surface of the sample, and the first target displacement field data of the target material during the heating process is reflected through the speckle change.

[0029] In one embodiment of the present application, a pre-set displacement load is applied to the target material at the target temperature, and the second target displacement field data of the target material during the displacement load application process is obtained, including: applying a pre-set displacement load to the target material at the target temperature; obtaining speckle change images of the target material during the displacement load application process through an image acquisition device; obtaining second target displacement field data of the target material during the displacement load application process based on the speckle change images.

[0030] In this embodiment, a displacement load is applied to the sample at 1600°C, and the sample is stretched at a speed of 0.5 mm / min. During this process, the speckle pattern on the surface is recorded using a CCD high-speed camera, and the temperature field is recorded using an infrared thermal imager (in an ideal state, the temperature field remains constant). The speckle pattern reflects the second target displacement field data of the target material during the application of the displacement load.

[0031] like Figure 4 , Figure 5 As shown, this specification provides a device for identifying the thermodynamic properties of materials. The device can be implemented in software, hardware, or a combination of both. From a hardware perspective, such as... Figure 2 The diagram shown is a hardware architecture diagram of an electronic device containing a material thermodynamic property parameter identification device provided in an embodiment of this specification. Except for... Figure 3 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 2 As shown, a device in a logical sense is formed by the CPU of the electronic device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0032] like Figure 2 As shown, this embodiment provides a device for identifying the thermodynamic properties of materials, comprising: The heating experiment module 300 uniformly heats the target material from the current temperature to the target temperature, and acquires the target temperature field data and the first target displacement field data of the target material during the heating process. Tensile test module 302 is connected to the heating test module. It applies a preset displacement load to the target material at the target temperature and obtains the second target displacement field data of the target material during the application of the displacement load. The parameter inversion module 304 is connected to the tensile test module. It inputs the target temperature field data, the first target displacement field data, and the second target displacement field data into the trained thermodynamic performance parameter identification model to obtain the output target elastic modulus and target constant parameters. The target constant parameters are the constant parameters of the quadratic equation between the thermal expansion coefficient and the temperature value. The thermal expansion coefficient calculation module 306 is connected to the parameter inversion module and obtains the target thermal expansion coefficient of the target material based on the target constant parameter and the target temperature field data.

[0033] In this embodiment of the specification, the heating test module 300 can be used to perform step 100 in the above method embodiment, the tensile test module 302 can be used to perform step 102 in the above method embodiment, the parameter inversion module 304 can be used to perform step 104 in the above method embodiment, and the expansion coefficient calculation module 306 can be used to perform step 106 in the above method embodiment.

[0034] In one embodiment of this specification, the sample dataset of the thermodynamic performance parameter identification model is obtained in the following manner: Simulation constant parameters and simulation elastic modulus are extracted within a preset parameter range that conforms to the physical constraints of the target material category; The simulation constant parameters and the simulation elastic modulus are used as the output dataset of the sample dataset; The heating process and displacement load application process of the target material are simulated by finite element model to obtain simulated temperature field data and simulated displacement field data that correspond one-to-one with each set of simulated constant parameters and simulated elastic modulus. The simulated temperature field data and simulated displacement field data are used as the input dataset for the sample dataset.

[0035] In one embodiment of this specification, the training process of the thermodynamic performance parameter identification model is as follows: The simulated temperature field data and simulated displacement data obtained from the simulated heating process are input into the heating stage branch of the dual-channel convolutional neural network. The simulated displacement field data obtained by simulating the process of applying displacement load is input into the stretching stage branch of the dual-channel convolutional neural network. The feature data extracted from the heating stage branch and the stretching stage branch are obtained; The feature data of the heating stage branch and the stretching stage branch are coupled and fused by the feature fusion layer of a dual-channel convolutional neural network to obtain a multi-source feature map. Based on the predicted values ​​of the output variation parameters and elastic modulus of the multi-source feature map, and using the corresponding simulated variation parameters and simulated elastic modulus in the sample dataset as labels, the error between the predicted values ​​and the labels is calculated. Perform iterative training until the error meets the preset convergence condition to obtain the trained thermodynamic performance parameter identification model.

[0036] In one embodiment of this specification, the target constant parameter is the quadratic equation relating the coefficient of thermal expansion to the temperature value: α 1 =a 0 +a 1Figure 3 2 *T 2 , α 2 =b 0 +b 1 Figure 3 *T+a *T+b 2 *T 2 , α 1 The coefficient of thermal expansion is in the first direction. α 2 This is the coefficient of thermal expansion in the second direction. a 0 , a 1 , a 2 , b 0 , b 1 and b 2 is a constant parameter, and T is the temperature value.

[0037] In one embodiment of this specification, obtaining the target thermal expansion coefficient of the target material based on the target constant parameter and the target temperature field data includes: Based on the target constant parameters, a quadratic equation in the first direction and a quadratic equation in the second direction are obtained; Based on the temperature field data, the quadratic equation in the first direction, and the quadratic equation in the second direction, the target thermal expansion coefficient in the first direction and the target thermal expansion coefficient in the second direction are obtained.

[0038] In one embodiment of this specification, the step of uniformly heating the target material from its current temperature to a target temperature and acquiring target temperature field data and first target displacement field data of the target material during the heating process includes: The target material, after being preloaded, is uniformly heated from its current temperature to the target temperature. Acquire temperature field data of the target material during the heating process; Images of the speckle pattern changes of the target material during the heating process are acquired using an image acquisition device; Based on the speckle variation image, the first target displacement field data of the target material during the heating process are obtained.

[0039] In one embodiment of this specification, applying a preset displacement load to the target material at the target temperature and obtaining second target displacement field data of the target material during the application of the displacement load includes: A preset displacement load is applied to the target material at the target temperature; Images of speckle changes in the target material during the application of displacement load are acquired using an image acquisition device; Based on the speckle variation image, the second target displacement field data of the target material during the application of displacement load is obtained.

[0040] It is understood that the structures illustrated in the embodiments of this specification do not constitute a specific limitation on a health status assessment device for spacecraft electromechanical components. In other embodiments of this specification, a health status assessment device for spacecraft electromechanical components may include more or fewer components than illustrated, or combine certain components, or split certain components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0041] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiments in this specification, and the specific details can be found in the descriptions in the method embodiments in this specification, so they will not be repeated here.

[0042] This specification also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a method for assessing the health status of a spacecraft electromechanical component according to any embodiment of this specification.

[0043] This specification also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform a health status assessment method for a spacecraft electromechanical component according to any embodiment of this specification.

[0044] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0045] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute a part of this specification.

[0046] Storage media embodiments for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0047] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0048] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0049] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0050] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this specification, and are not intended to limit them. Although this specification has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this specification.

Claims

1. A method for identifying thermodynamic property parameters of a material, characterized in that, include: The target material is uniformly heated from the current temperature to the target temperature, and the target temperature field data and the first target displacement field data of the target material are obtained during the heating process. A preset displacement load is applied to the target material at the target temperature, and second target displacement field data of the target material during the application of the displacement load are obtained; The target temperature field data, the first target displacement field data, and the second target displacement field data are input into the trained thermodynamic performance parameter identification model to obtain the output target elastic modulus and target constant parameters. The target constant parameters are the constant parameters of the quadratic equation between the thermal expansion coefficient and the temperature value. Based on the target constant parameters and the target temperature field data, the target thermal expansion coefficient of the target material is obtained.

2. The method according to claim 1, characterized in that, The sample dataset for the thermodynamic performance parameter identification model was obtained in the following way: Simulation constant parameters and simulation elastic modulus are extracted within a preset parameter range that conforms to the physical constraints of the target material category; The simulation constant parameters and the simulation elastic modulus are used as the output dataset of the sample dataset; The heating process and displacement load application process of the target material are simulated by finite element model to obtain simulated temperature field data and simulated displacement field data that correspond one-to-one with each set of simulated constant parameters and simulated elastic modulus. The simulated temperature field data and simulated displacement field data are used as the input dataset for the sample dataset.

3. The method according to claim 2, characterized in that, The training process for the thermodynamic performance parameter identification model is as follows: The simulated temperature field data and simulated displacement data obtained from the simulated heating process are input into the heating stage branch of the dual-channel convolutional neural network. The simulated displacement field data obtained by simulating the process of applying displacement load is input into the stretching stage branch of the dual-channel convolutional neural network. The feature data extracted from the heating stage branch and the stretching stage branch are obtained; The feature data of the heating stage branch and the stretching stage branch are coupled and fused by the feature fusion layer of a dual-channel convolutional neural network to obtain a multi-source feature map. Based on the predicted values ​​of the output variation parameters and elastic modulus of the multi-source feature map, and using the corresponding simulated variation parameters and simulated elastic modulus in the sample dataset as labels, the error between the predicted values ​​and the labels is calculated. Perform iterative training until the error meets the preset convergence condition to obtain the trained thermodynamic performance parameter identification model.

4. The method according to claim 1, characterized in that, The target constant parameter is the coefficient of thermal expansion, and the quadratic equation relating it to temperature is: α 1 =a 0 +a 1 *T+a 2 *T 2 , α 2 =b 0 +b 1 *T+b 2 *T 2 , α 1 The coefficient of thermal expansion is in the first direction. α 2 This is the coefficient of thermal expansion in the second direction. a 0 , a 1 , a 2 , b 0 , b 1 and b 2 is a constant parameter, and T is the temperature value.

5. The method according to claim 4, characterized in that, The process of obtaining the target thermal expansion coefficient of the target material based on the target constant parameter and the target temperature field data includes: Based on the target constant parameters, a quadratic equation in the first direction and a quadratic equation in the second direction are obtained; Based on the temperature field data, the quadratic equation in the first direction, and the quadratic equation in the second direction, the target thermal expansion coefficient in the first direction and the target thermal expansion coefficient in the second direction are obtained.

6. The method according to claim 1, characterized in that, The step of uniformly heating the target material from the current temperature to the target temperature and acquiring the target temperature field data and the first target displacement field data of the target material during the heating process includes: The target material, after being preloaded, is uniformly heated from its current temperature to the target temperature. Acquire temperature field data of the target material during the heating process; Images of the speckle pattern changes of the target material during the heating process are acquired using an image acquisition device; Based on the speckle variation image, the first target displacement field data of the target material during the heating process are obtained.

7. The method according to claim 1, characterized in that, The step of applying a preset displacement load to the target material at the target temperature and obtaining second target displacement field data of the target material during the application of the displacement load includes: A preset displacement load is applied to the target material at the target temperature; Images of speckle changes in the target material during the application of displacement load are acquired using an image acquisition device; Based on the speckle variation image, the second target displacement field data of the target material during the application of displacement load is obtained.

8. A device for identifying the thermodynamic properties of a material, characterized in that, include: The heating experiment module uniformly heats the target material from the current temperature to the target temperature, and acquires the target temperature field data and the first target displacement field data of the target material during the heating process. A tensile testing module, connected to the heating testing module, applies a preset displacement load to the target material at the target temperature and acquires second target displacement field data of the target material during the application of the displacement load. The parameter inversion module, connected to the tensile test module, inputs the target temperature field data, the first target displacement field data, and the second target displacement field data into the trained thermodynamic performance parameter identification model to obtain the output target elastic modulus and target constant parameters. The target constant parameters are the constant parameters of the quadratic equation between the thermal expansion coefficient and the temperature value. The thermal expansion coefficient calculation module, connected to the parameter inversion module, obtains the target thermal expansion coefficient of the target material based on the target constant parameters and the target temperature field data.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method of any one of claims 1-7.

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