Reliability Prediction Method for Gas Turbine Blades Based on Stress Information Data Assimilation

Abstract

The present application proposes a reliability prediction method, device, electronic device and storage medium for gas turbine blades based on stress information data assimilation. Among them, the method includes: establishing a theoretical calculation prediction model for gas turbine blades based on the mapping relationship between different operating condition parameters and material parameters of gas turbine blades and multiple maximum stress values in the corresponding stress information data set under different operating condition parameters and material parameters, and obtaining a test correction prediction model after updating the theoretical calculation prediction model according to the target stress information data set obtained by assimilating the test stress information data set of the gas turbine blades, so as to predict the maximum stress value corresponding to the operating condition parameters and material parameters to be predicted of the gas turbine blades, thereby determining the reliability of the gas turbine blades. Thus, the accuracy of the reliability prediction model of the gas turbine blades is improved, and the reliability of the gas turbine blades under different operating conditions and materials is accurately determined.

Description

Technical Field

[0001] This application relates to the technical field of gas turbines, and in particular, to a method, device, electronic device, and storage medium for predicting the reliability of gas turbine blades based on stress information data assimilation. Background Technique

[0002] The reliability of gas turbines has always been an important indicator concerned by design engineers, manufacturers, and users. With the continuous expansion of the application scope of gas turbines in the engineering field, especially in the marine and aviation fields, the reliability of gas turbines has become a top priority, which reflects the continuous working ability of the unit.

[0003] In particular, the turbine is the core component of the gas turbine, and the blade is one of the most important components in the turbine. Since the gas turbine often needs to change its operating conditions during operation, its flow rate and others have a large range of changes. Therefore, the reliability prediction of the blade has gradually become one of the main research directions.

[0004] In related technologies, the finite element method is usually used for strength characteristic analysis, a surrogate model is established, or experimental methods are used to test one by one to obtain the stress distribution of the blade during operation, so as to predict the reliability of the blade. This will inevitably lead to technical problems such as long time consumption, low efficiency, and poor reliability in the reliability prediction process of gas turbine blades. Therefore, how to improve the efficiency and reliability in the reliability prediction process of gas turbine blades has become an urgent problem to be solved. Summary of the Invention

[0005] This application proposes a method, device, electronic device, and storage medium for predicting the reliability of gas turbine blades based on stress information data assimilation.

[0006] The first aspect embodiment of the present application proposes a reliability prediction method for gas turbine blades based on stress information data assimilation. The method includes: obtaining a stress information data set corresponding to the gas turbine blades under different working condition parameters and material parameters, where the stress data information set includes multiple stress maximum values; establishing a mapping relationship between different working condition parameters and material parameters and the multiple stress maximum values, and establishing a theoretical calculation prediction model of the gas turbine blades according to the mapping relationship; obtaining a test stress information data set corresponding to different working condition parameters and material parameters during the research and development of the gas turbine blades; assimilating the stress information data set according to the test stress information data set to obtain an assimilated target stress information data set; updating the theoretical calculation prediction model according to the target stress information data set to obtain an updated test correction prediction model; inputting the working condition parameters and material parameters to be predicted of the gas turbine blades into the test correction prediction model to obtain the predicted stress maximum value of the gas turbine blades; determining the reliability of the gas turbine blades according to the comparison result between the predicted stress maximum value of the gas turbine blades and a preset maximum allowable stress value.

[0007] In an embodiment of the present application, the assimilating the stress information data set according to the test stress information data set to obtain an assimilated target stress information data set includes: obtaining a first maximum stress correction coefficient corresponding to the gas turbine blades during fatigue damage accumulation effect; determining a second maximum stress correction coefficient corresponding to the gas turbine blades when considering the test stress data information set based on the stress information data set of the gas turbine blades and the test stress data information set; adding the multiple stress maximum values in the stress information data set to the first maximum stress correction coefficient and the second maximum stress correction coefficient respectively to obtain an assimilated target stress information data set.

[0008] In an embodiment of the present application, the obtaining the first maximum stress correction coefficient corresponding to the fatigue damage accumulation effect of the gas turbine blades includes: obtaining the minimum stress, temperature limit and start-stop times of the gas turbine blades; determining the first maximum stress correction coefficient corresponding to the fatigue damage accumulation effect of the gas turbine blades according to the minimum stress, temperature limit and start-stop times of the gas turbine blades.

[0009] In one embodiment of the present application, determining the second maximum stress correction factor corresponding to the gas turbine blade when considering the test stress data information set based on the stress information data set of the gas turbine blade and the test stress data information set includes: calculating the covariance of the values of the stress data information set and the test stress data information set in the gas turbine blade based on multiple stress maximum values and the mean value of multiple stress maximum values in the stress data information set, and multiple test stress maximum values and the mean value of multiple test stress maximum values in the test stress data information set; determining the second maximum stress correction factor corresponding to the gas turbine blade when considering the test stress data information set according to the covariance.

[0010] The present application proposes a reliability prediction method for gas turbine blades based on stress information data assimilation. Based on the mapping relationship between different operating condition parameters and material parameters of gas turbine blades and multiple stress maximum values in the corresponding stress information data set under different operating condition parameters and material parameters, a theoretical calculation prediction model of gas turbine blades is established. And according to the test stress information data set of gas turbine blades, an objective stress information data set after assimilating the stress information data set is obtained, and a test correction prediction model after updating the theoretical calculation prediction model is obtained to predict the maximum stress value corresponding to the operating condition parameters and material parameters to be predicted of the gas turbine blade, thereby determining the reliability of the gas turbine blade. Thus, the accuracy of the reliability prediction model of the gas turbine blade is improved, and the reliability of the gas turbine blade under different operating conditions and materials is accurately determined.

[0011] The second aspect embodiment of the present application proposes a reliability prediction device for a gas turbine blade based on stress information data assimilation. The device includes: a first acquisition module, configured to acquire a stress information data set corresponding to the gas turbine blade under different operating condition parameters and material parameters, wherein the stress data information set includes a plurality of stress maximum values; a establishment module, configured to establish a mapping relationship between different operating condition parameters and material parameters and the plurality of stress maximum values, and establish a theoretical calculation prediction model of the gas turbine blade according to the mapping relationship; a second acquisition module, configured to acquire a test stress information data set corresponding to different operating condition parameters and material parameters during the research and development of the gas turbine blade; an assimilation module, configured to assimilate the stress information data set according to the test stress information data set to obtain an assimilated target stress information data set; an update module, configured to update the theoretical calculation prediction model according to the target stress information data set to obtain an updated test correction prediction model; an input module, configured to input the to-be-predicted operating condition parameters and material parameters of the gas turbine blade into the test correction prediction model to obtain the predicted stress maximum value of the gas turbine blade; a determination module, configured to determine the reliability of the gas turbine blade according to the comparison result between the predicted stress maximum value of the gas turbine blade and a preset maximum allowable stress value.

[0012] In an embodiment of the present application, the assimilation module includes: an acquisition unit, configured to acquire a first maximum stress correction coefficient corresponding to the gas turbine blade under the fatigue damage accumulation effect; a determination unit, configured to determine a second maximum stress correction coefficient corresponding to the gas turbine blade when considering the test stress data information set based on the stress information data set of the gas turbine blade and the test stress data information set; a generation unit, configured to add the plurality of stress maximum values in the stress information data set to the first maximum stress correction coefficient and the second maximum stress correction coefficient respectively to obtain an assimilated target stress information data set.

[0013] In an embodiment of the present application, the acquisition unit is specifically configured to: acquire the minimum stress, temperature tolerance limit and start-stop times of the gas turbine blade; and determine the first maximum stress correction coefficient corresponding to the fatigue damage accumulation effect of the gas turbine blade according to the minimum stress, temperature tolerance limit and start-stop times of the gas turbine blade.

[0014] In one embodiment of the present application, the determining unit is specifically configured to: calculate the covariance of the values of the stress data information set and the test stress data information set in the gas turbine blade based on multiple stress maximum values and the mean value of multiple stress maximum values in the stress data information set, and multiple test stress maximum values and the mean value of multiple test stress maximum values in the test stress data information set; and determine a second maximum stress correction coefficient corresponding to the gas turbine blade when considering the test stress data information set according to the covariance.

[0015] The present application proposes a reliability prediction device for gas turbine blades based on stress information data assimilation. Based on the mapping relationship between different operating condition parameters and material parameters of gas turbine blades and multiple stress maximum values in the corresponding stress information data set under different operating condition parameters and material parameters, a theoretical calculation prediction model of gas turbine blades is established. And according to the test stress information data set of gas turbine blades, an objective stress information data set after assimilating the stress information data set is obtained, and a test correction prediction model after updating the theoretical calculation prediction model is obtained to predict the maximum stress value corresponding to the operating condition parameters and material parameters to be predicted of the gas turbine blade, thereby determining the reliability of the gas turbine blade. Thus, the accuracy of the reliability prediction model of the gas turbine blade is improved, and the reliability of the gas turbine blade under different operating conditions and materials is accurately determined.

[0016] A third aspect embodiment of the present application proposes an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the reliability prediction method for gas turbine blades based on stress information data assimilation in the embodiments of the present application is implemented.

[0017] A fourth aspect embodiment of the present application proposes a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the reliability prediction method for gas turbine blades based on stress information data assimilation in the embodiments of the present application is implemented.

[0018] Other effects of the above optional manners will be described in conjunction with specific embodiments below. Description of the Drawings

[0019] Figure 1 is a schematic flowchart of a reliability prediction method for gas turbine blades based on stress information data assimilation provided by an embodiment of the present application;

[0020] Figure 2 is a schematic flowchart of another reliability prediction method for gas turbine blades based on stress information data assimilation provided by an embodiment of the present application;

[0021] Figure 3It is a schematic structural diagram of a gas turbine blade reliability prediction device based on stress information data assimilation provided by an embodiment of the present application;

[0022] Figure 4 It is a schematic structural diagram of another gas turbine blade reliability prediction device based on stress information data assimilation provided by an embodiment of the present application;

[0023] Figure 5 It is a block diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0024] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as limiting the present application.

[0025] The gas turbine blade reliability prediction method, device and electronic device based on stress information data assimilation according to the embodiments of the present application will be described below with reference to the accompanying drawings.

[0026] Figure 1 It is a schematic flow diagram of a gas turbine blade reliability prediction method based on stress information data assimilation provided by an embodiment of the present application. It should be noted that the execution subject of the gas turbine blade reliability prediction method based on stress information data assimilation provided in this embodiment is a gas turbine blade reliability prediction device based on stress information data assimilation. The gas turbine blade reliability prediction device based on stress information data assimilation can be implemented in software and / or hardware. The gas turbine blade reliability prediction device based on stress information data assimilation in this embodiment can be configured in an electronic device. The electronic device in this embodiment can include a server, and this embodiment does not specifically limit the electronic device.

[0027] As Figure 1 shown, the gas turbine blade reliability prediction method based on stress information data assimilation may include:

[0028] Step 101: Obtain a stress information data set corresponding to the gas turbine blade under different operating condition parameters and material parameters, where the stress data information set includes multiple stress maximum values.

[0029] In some embodiments, the operating condition parameters corresponding to the gas turbine blade may include, but are not limited to, inlet total temperature and pressure, outlet pressure, rotational speed, and flow rate. This embodiment does not specifically limit this.

[0030] In some embodiments, the material parameters corresponding to the gas turbine blade may include, but are not limited to, the material density of the gas turbine blade metal, the thermal conductivity of the gas turbine blade at different temperatures, the elastic modulus of the gas turbine blade, the Poisson's ratio of the gas turbine blade, and the thermal expansion coefficient of the gas turbine blade. This embodiment does not make specific limitations on this.

[0031] In some embodiments, the stress information data set may further include the spatial position coordinates of multiple stress maximum positions. The position where fatigue fracture may occur can be accurately determined according to the spatial position coordinates, so as to adjust the parameters of the position where fatigue fracture may occur to protect the gas turbine blade and extend the service life of the gas turbine blade.

[0032] For example, since the root position of the gas turbine blade is prone to fatigue fracture, the root fillet can be enlarged to better protect the gas turbine blade.

[0033] Step 102: Establish a mapping relationship between different operating condition parameters and material parameters and multiple stress maxima, and establish a theoretical calculation prediction model of the gas turbine blade according to the mapping relationship.

[0034] In some embodiments, based on the different operating condition parameters and material parameters of the gas turbine blade, and the multiple stress maxima of the gas turbine blade under different operating condition parameters and material parameters, the mapping relationship between different operating condition parameters and material parameters and multiple stress maxima can be established by, but not limited to, the response surface method, the Kriging model, the artificial neural network, and the support vector machine, and a theoretical calculation prediction model of the gas turbine blade can be established according to the mapping relationship, so as to preliminarily determine the theoretical calculation prediction model of the gas turbine blade.

[0035] Step 103: Obtain the test stress information data set corresponding to different operating condition parameters and material parameters during the development of the gas turbine blade.

[0036] In some embodiments, the test stress information data set may include multiple test stress maxima corresponding to different operating condition parameters and material parameters on the stress test bench during the development of the gas turbine blade, but is not limited thereto.

[0037] Step 104: According to the test stress information data set, assimilate the stress information data set to obtain the assimilated target stress information data set.

[0038] In some embodiments, according to the test stress information data set, the data assimilation method, but not limited to the ensemble Kalman filter, can be used to assimilate the stress information data set, and at the same time, combined with the fatigue damage accumulation effect of the gas turbine blade, so as to dynamically update the stress information data set to obtain the updated target stress information data set.

[0039] Step 105: Update the theoretical calculation prediction model according to the target stress information dataset to obtain an updated test correction prediction model.

[0040] In some embodiments, the theoretical calculation prediction model can be dynamically updated according to the mapping relationship between different working condition parameters and material parameters in the target stress information dataset and multiple stress maximum values, so as to obtain an updated test correction prediction model, thereby effectively improving the prediction accuracy of the reliability prediction model of the gas turbine blade.

[0041] Step 106: Input the working condition parameters and material parameters to be predicted of the gas turbine blade into the test correction prediction model to obtain the predicted maximum stress value of the gas turbine blade.

[0042] In some embodiments, after inputting the working condition parameters and material parameters to be predicted of the gas turbine blade into the test correction prediction model, the predicted maximum stress value of the gas turbine blade under the predicted working condition parameters and material parameters can be determined according to the mapping relationship between different working condition parameters and material parameters in the test correction prediction model and multiple stress maximum values.

[0043] Step 107: Determine the reliability of the gas turbine blade according to the comparison result between the predicted maximum stress value of the gas turbine blade and the preset maximum allowable stress value.

[0044] In some embodiments, the preset maximum allowable stress value can be obtained by conducting experiments on the gas turbine blade, but it is not limited thereto.

[0045] In some embodiments, when the comparison result shows that the predicted maximum stress value of the gas turbine blade is less than or equal to the preset maximum allowable stress value, it indicates that the gas turbine blade of this material can operate normally under this working condition.

[0046] In some other embodiments, when the comparison result shows that the predicted maximum stress value of the gas turbine blade is greater than the preset maximum allowable stress value, it indicates that the gas turbine blade of this material cannot operate normally under this working condition, and the gas turbine blade needs to be adjusted to prevent it from being damaged.

[0047] The present application proposes a reliability prediction method for gas turbine blades based on stress information data assimilation. Based on different operating condition parameters and material parameters of gas turbine blades, and the mapping relationship between the corresponding stress information data sets at different operating condition parameters and material parameters and multiple maximum stress values, a theoretical calculation prediction model of gas turbine blades is established. And according to the test stress information data set of gas turbine blades, the target stress information data set after assimilating the stress information data set is obtained, and a test correction prediction model after updating the theoretical calculation prediction model is obtained to predict the maximum stress value corresponding to the operating condition parameters and material parameters to be predicted of gas turbine blades, so as to determine the reliability of gas turbine blades. Thus, the accuracy of the reliability prediction model of gas turbine blades is improved, and the reliability of gas turbine blades under different operating conditions and materials is accurately determined.

[0048] Figure 2 It is a schematic flow chart of another reliability prediction method for gas turbine blades based on stress information data assimilation provided by an embodiment of the present application.

[0049] Step 201, obtain the stress information data set corresponding to the gas turbine blade under different operating condition parameters and material parameters, where the stress data information set includes multiple maximum stress values.

[0050] Step 202, establish the mapping relationship between different operating condition parameters and material parameters and multiple maximum stress values, and establish a theoretical calculation prediction model of the gas turbine blade according to the mapping relationship.

[0051] Step 203, obtain the test stress information data set corresponding to different operating condition parameters and material parameters during the development of the gas turbine blade.

[0052] It should be noted that for the specific implementation manners of steps 201 to 203, reference can be made to the relevant descriptions in the above embodiments.

[0053] Step 204, obtain the first maximum stress correction coefficient corresponding to the gas turbine blade during the fatigue damage accumulation effect.

[0054] In some embodiments, an implementation manner of obtaining the first maximum stress correction coefficient corresponding to the gas turbine blade during the fatigue damage accumulation effect may be to obtain the minimum stress, the temperature limit that can be withstood, and the number of start-stop cycles of the gas turbine blade, and determine the first maximum stress correction coefficient corresponding to the fatigue damage accumulation effect of the gas turbine blade according to the minimum stress, the temperature limit that can be withstood, and the number of start-stop cycles of the gas turbine blade.

[0055] Specifically, the calculation formula for calculating the first maximum stress correction coefficient corresponding to the fatigue damage accumulation effect of the gas turbine blade according to the minimum stress, the temperature limit that can be withstood, and the number of start-stop cycles of the gas turbine blade can be:

[0056]

[0057] Among them, Δσ ac represents the first maximum stress correction coefficient corresponding to the gas turbine blade when considering the fatigue damage accumulation effect, and σ min is the minimum stress on the gas turbine blade, and T max is the highest temperature borne by the gas turbine blade, and T min is the lowest temperature borne by the gas turbine blade, and γ is the number of start-stop cycles experienced by the gas turbine blade.

[0058] Step 205: Based on the stress information data set and the test stress data information set of the gas turbine blade, determine the second maximum stress correction coefficient corresponding to the gas turbine blade when considering the test stress data information set.

[0059] In some embodiments, an implementation manner of determining the second maximum stress correction coefficient corresponding to the gas turbine blade when considering the test stress data information set based on the stress information data set and the test stress data information set of the gas turbine blade may be to calculate the covariance of the stress data information set and the test stress data information set values in the gas turbine blade based on multiple stress maximum values and the mean of multiple stress maximum values in the stress data information set, as well as multiple test stress maximum values and the mean of multiple test stress maximum values in the test stress data information set, and determine the second maximum stress correction coefficient corresponding to the gas turbine blade when considering the test stress data information set according to the covariance.

[0060] Specifically, the calculation formula for the covariance of the stress information data set and the test stress data information set of the gas turbine blade may be:

[0061]

[0062] Among them, cov(σ c , σ ex ) is the covariance of the stress information data set and the test stress data information set of the gas turbine blade, and σ ex is the multiple test stress maximum values of each measurement point in the test stress data information set during the research and development of the gas turbine blade, is the mean of the multiple test stress maximum values of each measurement point in the test stress data information set, and σ c is the multiple stress maximum values of each measurement point in the stress data information set of the gas turbine blade, is the stress mean of the multiple stress maximum values of each measurement point in the stress data information set.

[0063] In some embodiments, the covariance cov(σ c , σ ex)As the second largest stress correction factor Δσ ex , for example, Δσ ex = cov(σ c , σ ex ).

[0064] Step 206: Add the multiple maximum stress values in the stress information dataset to the first largest stress correction factor and the second largest stress correction factor respectively to obtain the assimilated target stress information dataset.

[0065] In some embodiments, the calculation formula for adding the multiple maximum stress values in the stress information dataset to the first largest stress correction factor and the second largest stress correction factor respectively can be:

[0066] σ′ i = σ i + σ ac + σ ex

[0067] where, σ i represents the multiple maximum stress values in the stress information dataset, and σ′ i represents the multiple maximum stress values in the target stress information dataset.

[0068] Step 207: Update the theoretical calculation prediction model according to the target stress information dataset to obtain the updated test correction prediction model.

[0069] Step 208: Input the working condition parameters and material parameters to be predicted of the gas turbine blade into the test correction prediction model to obtain the predicted maximum stress value of the gas turbine blade.

[0070] Step 209: Determine the reliability of the gas turbine blade according to the comparison result between the predicted maximum stress value of the gas turbine blade and the preset maximum allowable stress value.

[0071] The present application proposes a reliability prediction method for gas turbine blades based on stress information data assimilation. Based on different operating condition parameters and material parameters of gas turbine blades, and the mapping relationship between multiple stress maximum values in the stress information dataset corresponding to different operating condition parameters and material parameters, a theoretical calculation prediction model of gas turbine blades is established, and the first maximum stress correction coefficient corresponding to the fatigue damage accumulation effect of gas turbine blades is obtained. And based on the stress information dataset and the test stress data information set of gas turbine blades, the second maximum stress correction coefficient corresponding to the gas turbine blades when considering the test stress data information set is determined. Then, multiple stress maximum values in the stress information dataset are respectively added to the first maximum stress correction coefficient and the second maximum stress correction coefficient to obtain an assimilated target stress information dataset, and the theoretical calculation prediction model is updated according to the target stress information dataset to obtain a test correction prediction model, so as to predict the maximum stress value corresponding to the operating condition parameters and material parameters to be predicted of gas turbine blades, thereby determining the reliability of gas turbine blades. Thus, through the correction coefficients corresponding to the fatigue damage accumulation effect and the test stress data information set respectively, the stress information dataset is assimilated, and the theoretical calculation prediction model is updated according to the assimilated target stress information dataset, thereby improving the accuracy of the reliability prediction model of gas turbine blades and perfecting the reliability design system of gas turbine blades.

[0072] In order to implement the above embodiments, the present embodiment provides a reliability prediction device for gas turbine blades based on stress information data assimilation. Figure 3 It is a schematic structural diagram of a reliability prediction device for gas turbine blades based on stress information data assimilation provided by an embodiment of the present application.

[0073] As Figure 3 shown, the reliability prediction device 300 for gas turbine blades based on stress information data assimilation includes:

[0074] A first acquisition module 301, configured to acquire a stress information dataset corresponding to a gas turbine blade under different operating condition parameters and material parameters, wherein the stress data information dataset includes multiple stress maximum values.

[0075] A building module 302, configured to establish a mapping relationship between different operating condition parameters and material parameters and multiple stress maximum values, and establish a theoretical calculation prediction model of the gas turbine blade according to the mapping relationship.

[0076] A second acquisition module 303, configured to acquire a test stress information dataset corresponding to different operating condition parameters and material parameters during the research and development of the gas turbine blade.

[0077] The assimilation module 304 is configured to assimilate the stress information data set according to the test stress information data set to obtain an assimilated target stress information data set.

[0078] The update module 305 is configured to update the theoretical calculation prediction model according to the target stress information data set to obtain an updated test correction prediction model.

[0079] The input module 306 is configured to input the working condition parameters and material parameters to be predicted of the gas turbine blade into the test correction prediction model to obtain the maximum predicted stress of the gas turbine blade.

[0080] The determination module 307 is configured to determine the reliability of the gas turbine blade according to the comparison result between the maximum predicted stress of the gas turbine blade and a preset maximum allowable stress value.

[0081] In some embodiments, as Figure 4 shown, the assimilation module 304 includes:

[0082] The acquisition unit 3041 is configured to acquire a first maximum stress correction coefficient corresponding to the gas turbine blade when there is a fatigue damage accumulation effect.

[0083] The determination unit 3042 is configured to determine a second maximum stress correction coefficient corresponding to the gas turbine blade when considering the test stress data information set based on the stress information data set and the test stress data information set of the gas turbine blade.

[0084] The generation unit 3043 is configured to add the maximum stress values in the stress information data set to the first maximum stress correction coefficient and the second maximum stress correction coefficient respectively to obtain an assimilated target stress information data set.

[0085] In some embodiments, the acquisition unit 3041 is specifically configured to:

[0086] Acquire the minimum stress, the temperature limit that can be withstood, and the number of start-stop cycles of the gas turbine blade.

[0087] Determine the first maximum stress correction coefficient corresponding to the fatigue damage accumulation effect of the gas turbine blade according to the minimum stress, the temperature limit that can be withstood, and the number of start-stop cycles of the gas turbine blade.

[0088] In some embodiments, the determination unit 3042 is specifically configured to:

[0089] Calculate the covariance of the stress data information set and the test stress data information set values in the gas turbine blade based on the maximum stress values in the stress data information set and the mean value of the maximum stress values, and the maximum test stress values and the mean value of the maximum test stress values in the test stress data information set.

[0090] Based on the covariance, determine the second maximum stress correction factor corresponding to the gas turbine blade when considering the test stress data information set.

[0091] This application proposes a reliability prediction method for gas turbine blades based on stress information data assimilation. Based on the mapping relationship between different operating condition parameters and material parameters of the gas turbine blade and multiple stress maximum values in the corresponding stress information data set under different operating condition parameters and material parameters, a theoretical calculation prediction model of the gas turbine blade is established. And according to the test stress information data set of the gas turbine blade, the target stress information data set after assimilating the stress information data set is obtained, and the updated test correction prediction model of the theoretical calculation prediction model is obtained to predict the maximum stress value corresponding to the operating condition parameters and material parameters to be predicted of the gas turbine blade, so as to determine the reliability of the gas turbine blade. Thus, the accuracy of the reliability prediction model of the gas turbine blade is improved, and the reliability of the gas turbine blade under different operating conditions and materials is accurately determined.

[0092] As Figure 5 shown, it is a block diagram of an electronic device according to an embodiment of the present application.

[0093] As Figure 5 shown, the electronic device includes:

[0094] A memory 501, a processor 502, and computer instructions stored on the memory 501 and executable on the processor 502.

[0095] When the processor 502 executes the instructions, it implements the reliability prediction method for gas turbine blades based on stress information data assimilation provided in the above embodiment.

[0096] Further, the electronic device further includes:

[0097] A communication interface 503 for communication between the memory 501 and the processor 502.

[0098] The memory 501 is used to store computer instructions executable on the processor 502.

[0099] The memory 501 may include a high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0100] The processor 502 is used to implement the reliability prediction method for gas turbine blades based on stress information data assimilation in the above embodiment when executing the program.

[0101] If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 only a thick line is used in Figure 5 , but it does not mean that there is only one bus or one type of bus.

[0102] Optionally, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a single chip, the memory 501, the processor 502, and the communication interface 503 can communicate with each other via an internal interface.

[0103] The processor 502 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0104] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0105] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0106] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A reliability prediction method for gas turbine blades based on stress information data assimilation, characterized in that, The method includes: Obtaining a stress information data set corresponding to the gas turbine blade under different operating condition parameters and material parameters, wherein the stress data information set includes a plurality of maximum stress values; Establishing a mapping relationship between different operating condition parameters and material parameters and the plurality of maximum stress values, and establishing a theoretical calculation prediction model of the gas turbine blade according to the mapping relationship; Obtaining a test stress information data set corresponding to different operating condition parameters and material parameters during the research and development of the gas turbine blade; Assimilating the stress information data set according to the test stress information data set to obtain an assimilated target stress information data set; Updating the theoretical calculation prediction model according to the target stress information data set to obtain an updated test correction prediction model; Inputting the to-be-predicted operating condition parameters and material parameters of the gas turbine blade into the test correction prediction model to obtain the predicted maximum stress value of the gas turbine blade; Determining the reliability of the gas turbine blade according to the comparison result between the predicted maximum stress value of the gas turbine blade and a preset maximum allowable stress value; The assimilating the stress information data set according to the test stress information data set to obtain an assimilated target stress information data set includes: Obtaining a first maximum stress correction coefficient corresponding to the gas turbine blade under the fatigue damage accumulation effect; Determining a second maximum stress correction coefficient corresponding to the gas turbine blade when considering the test stress data information set based on the stress information data set of the gas turbine blade and the test stress data information set; Adding the plurality of maximum stress values in the stress information data set to the first maximum stress correction coefficient and the second maximum stress correction coefficient respectively to obtain an assimilated target stress information data set.

2. The method according to claim 1, wherein The obtaining a first maximum stress correction coefficient corresponding to the fatigue damage accumulation effect of the gas turbine blade includes: Obtaining the minimum stress, the temperature limit it can withstand, and the number of start-stop cycles of the gas turbine blade; Determining a first maximum stress correction coefficient corresponding to the fatigue damage accumulation effect of the gas turbine blade according to the minimum stress, the temperature limit it can withstand, and the number of start-stop cycles of the gas turbine blade.

3. The method according to claim 1, wherein The determining a second maximum stress correction coefficient corresponding to the gas turbine blade when considering the test stress data information set based on the stress information data set of the gas turbine blade and the test stress data information set includes: Calculating the covariance of the values of the stress data information set and the test stress data information set in the gas turbine blade based on the plurality of maximum stress values and the mean value of the plurality of maximum stress values in the stress data information set, and the plurality of test maximum stress values and the mean value of the plurality of test maximum stress values in the test stress data information set; Determining a second maximum stress correction coefficient corresponding to the gas turbine blade when considering the test stress data information set according to the covariance.

4. A reliability prediction device for gas turbine blades based on stress information data assimilation, characterized in that, The device includes: A first acquisition module, configured to acquire a stress information data set corresponding to the gas turbine blade under different operating condition parameters and material parameters, wherein the stress data information set includes a plurality of maximum stress values; A building module, configured to establish a mapping relationship between different operating condition parameters and material parameters and the plurality of maximum stress values, and establish a theoretical calculation prediction model of the gas turbine blade according to the mapping relationship; A second acquisition module, configured to acquire a test stress information data set corresponding to different operating condition parameters and material parameters during the research and development of the gas turbine blade; An assimilation module, configured to assimilate the stress information data set according to the test stress information data set to obtain an assimilated target stress information data set; An update module, configured to update the theoretical calculation prediction model according to the target stress information data set to obtain an updated test correction prediction model; An input module, configured to input the to-be-predicted operating condition parameters and material parameters of the gas turbine blade into the test correction prediction model to obtain the predicted maximum stress value of the gas turbine blade; A determination module, configured to determine the reliability of the gas turbine blade according to the comparison result between the predicted maximum stress value of the gas turbine blade and a preset maximum allowable stress value; The assimilation module includes: An acquisition unit, configured to acquire a first maximum stress correction coefficient corresponding to the gas turbine blade under the effect of fatigue damage accumulation; A determination unit, configured to determine a second maximum stress correction coefficient corresponding to the gas turbine blade when considering the test stress data information set based on the stress information data set of the gas turbine blade and the test stress data information set; A generation unit, configured to add the plurality of maximum stress values in the stress information data set to the first maximum stress correction coefficient and the second maximum stress correction coefficient respectively to obtain an assimilated target stress information data set.

5. The device according to claim 4, characterized in that, The acquisition unit is specifically configured to: Acquire the minimum stress, the temperature limit that can be withstood, and the number of start-stop cycles of the gas turbine blade; Determine a first maximum stress correction coefficient corresponding to the fatigue damage accumulation effect of the gas turbine blade according to the minimum stress, the temperature limit that can be withstood, and the number of start-stop cycles of the gas turbine blade.

6. The device according to claim 4, characterized in that The determination unit is specifically configured to: Calculate the covariance of the values of the stress data information set and the test stress data information set in the gas turbine blade based on the plurality of maximum stress values and the mean value of the plurality of maximum stress values in the stress data information set, and the plurality of test maximum stress values and the mean value of the plurality of test maximum stress values in the test stress data information set; Determine a second maximum stress correction coefficient corresponding to the gas turbine blade when considering the test stress data information set according to the covariance.

7. An electronic device, characterized in that, It includes: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the reliability prediction method of the gas turbine blade based on stress information data assimilation as described in any one of claims 1-3 when executing the program.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the reliability prediction method for gas turbine blades based on stress information data assimilation as described in any one of claims 1-3.

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