Thermal barrier coating thermal shock life prediction method, device and medium based on digital twinning

Abstract

A method for thermal shock life prediction of thermal barrier coatings based on digital twinning is disclosed, including: extracting thermal barrier coating features of one or more different thermal barrier coating samples, establishing one or more thermal shock life simulation models of thermal barrier coatings having different microstructures based at least in part on the extracted thermal barrier coating features and in combination with specified conditions during a thermal shock test process on corresponding thermal barrier coatings, detecting crack parameters and stress states at the ceramic layer and bond layer interface of the corresponding thermal barrier coatings and generating a first data set during the simultaneously conducted thermal shock test process, updating a digital twin model based at least in part on the first data set and a second data set associated with the evolution of physical features of the corresponding thermal barrier coatings, and performing thermal shock life prediction of the thermal barrier coatings based on the updated digital twin model. Numerous other aspects are also provided.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of aero-engines, and more particularly to a thermal barrier coating thermal shock life prediction method, device and medium based on digital twinning. BACKGROUND

[0002] Aero-engines and gas turbine hot end components usually need to withstand severe and complex service conditions during service, high temperature thermal shock, corrosion, etc. These factors seriously threaten the service life of the hot end components. Therefore, it is of great significance to protect these metal-based hot end components. Thermal barrier coating (TBC) is widely used in the thermal protection field of aero-engines and gas turbines due to its excellent high-temperature thermal insulation performance and oxidation corrosion resistance. The application of thermal barrier coating can greatly improve the thermal efficiency of the engine and the performance and life limit of the hot end component. Among them, the plasma spraying technology is widely used in the preparation of thermal barrier coating due to its excellent process flexibility and application cost advantage. However, due to the severe service conditions, once the thermal barrier coating fails, the underlying metal components will be rapidly ablated and fail, causing devastating effects. Therefore, the life prediction of thermal barrier coating is the key to ensuring the safe service of aero-engine and gas turbine hot end components. Since the performance of thermal barrier coating is closely related to the parameters, microstructure, etc. during the preparation process, and the service conditions of thermal barrier coating are usually under multi-field coupling conditions, the life prediction and health management of thermal barrier coating is a challenging task.

[0003] Digital twinning technology is to map multiple data such as real physical model, sensor update, operation history, etc. to virtual twin model, and to dynamically evaluate the use and life of key components in real time through continuous interaction between physical entity and virtual model. At present, digital twinning technology has become a key technology in the field of industrial production and intelligent manufacturing. The existing technology mainly focuses on the fatigue life prediction of hot end component base metal materials (such as aero-engine turbine disk, turbine blade), and does not target the thermal shock life prediction of thermal barrier coating. As an important thermal insulation component on the surface of metal-based hot end components, the high-temperature thermal shock life of thermal barrier coating is of great significance to the structural integrity and safe operation of the component. Therefore, it is urgent to introduce the digital twinning idea to realize the interaction and feedback between physical entity and virtual model to accurately predict the thermal shock life of thermal barrier coating based on considering the real service conditions and the differences in coating structure caused by different thermal barrier coating preparation processes.

[0004] The present application proposes a thermal barrier coating thermal shock life prediction method, device and medium based on digital twinning, which can accurately predict the high-temperature thermal shock life of thermal barrier coating in real time, and provide a basis for the subsequent structure design of thermal barrier coating system. SUMMARY

[0005] The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.

[0006] To solve the above problems, aspects of the present application propose a method, device and medium for thermal barrier coating thermal shock life prediction based on digital twinning.

[0007] In an aspect of the present disclosure, a method for thermal barrier coating thermal shock life prediction based on digital twinning is disclosed, comprising: extracting thermal barrier coating features of one or more different thermal barrier coating samples, the thermal barrier coating features including microstructure and thermodynamic parameters of the corresponding thermal barrier coating; determining specified conditions during a thermal shock test process of the corresponding thermal barrier coating; establishing one or more thermal shock life simulation models of thermal barrier coatings with different microstructures based at least in part on the extracted thermal barrier coating features and in combination with the specified conditions; detecting crack parameters and stress states at the ceramic layer and bond layer interface of the corresponding thermal barrier coating during the simultaneously conducted thermal shock test process, and generating a first data set associated with the evolution of the crack parameters and stress states at the ceramic layer and bond layer interface of the corresponding thermal barrier coating; real-time interaction of the first data set with the corresponding thermal shock life simulation model in the one or more thermal shock life simulation models to generate a digital twin model of the corresponding thermal barrier coating; updating the digital twin model based at least in part on a second data set associated with the evolution of the physical characteristics of the corresponding thermal barrier coating at different time periods during the thermal shock test process; and performing the thermal shock life prediction of the thermal barrier coating based on the updated digital twin model.

[0008] Preferably, the extraction of the microstructure of the thermal barrier coating comprises: converting a scanning electron microscope image reflecting the microstructure inside the ceramic layer into a digital image with mxn pixels using a computational micromechanics method; converting the digital image of the thermal barrier coating prepared under different preparation parameters into a vector graph using image tracking technology; and importing the digital image into modeling software to convert it into a planar solid.

[0009] Preferably, the specified conditions include one or more of the following: sample heating time, sample heating temperature, sample holding time, sample cooling time, sample heating and cooling medium type.

[0010] Preferably, during the thermal shock test process of the thermal barrier coating samples, the working conditions and / or environmental parameters of the thermal shock test process are kept consistent with the working conditions / environmental parameters inputted into the thermal shock life simulation model.

[0011] Preferably, generating the first data set associated with the corresponding thermal barrier coating comprises detecting, by a terahertz time-domain spectroscopy monitoring system in a reflection mode, the crack parameters and stress state at the ceramic layer and the bond layer interface of all thermal barrier coating samples during the thermal shock test process, and taking the detected data as the first data set, and wherein the first data set is associated with a time stamp when the data is generated.

[0012] Preferably, detecting the crack parameters and stress state at the ceramic layer and the bond layer interface of the corresponding thermal barrier coating comprises emitting a femtosecond pulsed laser from a femtosecond laser of a terahertz time-domain spectroscopy monitoring system, the femtosecond pulsed laser is split into a first light beam and a second light beam, wherein the first light beam is a pump light, which, after passing through an optical delay line module of the terahertz time-domain spectroscopy monitoring system, is incident on a terahertz emitter module of the terahertz time-domain spectroscopy monitoring system, a photoconductive antenna is excited by the pump light to radiate a terahertz pulse, the terahertz pulse enters a spatial light path transmission system of the terahertz time-domain spectroscopy monitoring system, is reflected on the surface of the thermal barrier coating sample, and wherein the second light beam is a probe light, the probe light is emitted from a beamsplitter of the terahertz time-domain spectroscopy monitoring system to drive a terahertz probe source; and a receiver module of the terahertz time-domain spectroscopy monitoring system decomposes the signal using a wavelet signal processing method to obtain the approximate coefficients and detail coefficients after signal decomposition, and then reconstructs and filters, and then performs frequency domain deconvolution through a Hanning window function, thereby detecting the first data set associated with the evolution of crack parameters and stress state at the interface of the ceramic layer and the bond layer with the execution of the thermal shock test cycle during the thermal shock test process.

[0013] Preferably, generating the digital twin model of the corresponding thermal barrier coating further comprises inputting the first data set into the thermal shock life simulation model to correct a first parameter set of the thermal shock life simulation model associated with the evolution of crack parameters and stress state.

[0014] Preferably, the digital twin model comprises one or more of a thermal-mechanical coupling analysis model, a stress field analysis model, and a structural damage evolution model.

[0015] Preferably, updating the digital twin model based on the second data set comprises inputting the actually measured second data set into the digital twin model to update a second parameter set of the digital twin model associated with the physical characteristics.

[0016] Preferably, the evolution of the physical characteristics of the thermal barrier coating comprises the evolution of the microstructure, the mechanical properties, the damage state, and further comprises: the evolution of the porosity of the ceramic layer, the evolution of the hardness, the elastic modulus, the fracture toughness of the ceramic layer, the evolution of the crack length within the ceramic layer, the evolution of the spallation area within the ceramic coating during the thermal shock process.

[0017] In yet another aspect of the disclosure, a device for thermal shock life prediction of thermal barrier coatings based on digital twinning is disclosed, comprising a processor and a memory, the memory storing program instructions; the processor running the program instructions to implement the method of any one of the above.

[0018] In another aspect of the disclosure, a non-transitory computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform the method for thermal shock life prediction of thermal barrier coatings based on digital twinning of any one of the above.

[0019] This summary is provided to introduce some concepts of the present application in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Other aspects, features, and / or advantages of the embodiments will be apparent from the description set forth herein, and from the description which follows. The novel features of the application are set forth with particularity in the claims that follow. A person skilled in the art will readily recognize from the following description that alternative aspects, examples, and details can be employed without departing from the scope of the application. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order that the above mentioned features of the application can be understood in detail, a more particular description of the application will be rendered by reference to the embodiments, some of which are shown in the drawings. It is to be understood that the drawings are not to scale and are merely schematic illustrations of some embodiments of the application. It should be noted that the figures are merely schematic and non-limiting. In the drawings, the size of some of the components can be exaggerated and not drawn on scale for illustrative purposes.

[0021] Figure 1 An example of a flowchart illustrating a method of thermal shock life prediction of plasma sprayed thermal barrier coatings based on digital twinning according to an embodiment of the application is shown.

[0022] Figure 2 An example of a roadmap illustrating a method of thermal shock life prediction of plasma sprayed thermal barrier coatings based on digital twinning according to an embodiment of the application is shown.

[0023] Figure 3An example of a microstructure of a plasma sprayed thermal barrier coating is illustrated according to an embodiment of the present application.

[0024] Figure 4 An example of a flow chart of a thermal shock test of a plasma sprayed thermal barrier coating is illustrated according to an embodiment of the present application.

[0025] Figure 5 An example of a working schematic of a terahertz time-domain spectroscopy monitoring system is illustrated according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] For the purpose of clarity, technical solutions and advantages of the present application, further detailed description will be made to the present application with specific embodiments and with reference to the drawings. In the following detailed description, a number of specific details are set forth in order to provide a thorough understanding of the described exemplary embodiments. However, it will be apparent to one skilled in the art that the described embodiments can be practiced without some or all of these specific details. In other exemplary embodiments, well-known structures or process steps have not been described in detail in order to avoid unnecessarily obscuring the concepts of the present disclosure.

[0027] In this specification, unless otherwise specified, the term "A or B" used in this specification means "A and B" and "A or B", but does not mean that A and B are exclusive.

[0028] Aero-engine and gas turbine hot end components usually need to withstand severe and complex service conditions during service, high temperature thermal shock, corrosion, etc. These factors seriously threaten the service life of the hot end components. Therefore, it is of great significance to protect these metal-based hot end components. Thermal barrier coating has excellent high-temperature thermal insulation performance and oxidation corrosion resistance, and is widely used in the thermal protection field of aero-engine and gas turbine. The application of thermal barrier coating can greatly improve the thermal efficiency of the engine and the performance and life limit of the hot end components. Among them, the plasma spraying technology is widely used in the preparation of thermal barrier coating due to its excellent process flexibility and application cost advantage. However, due to the severe service conditions, once the thermal barrier coating fails, the underlying metal components will be rapidly ablated and fail, causing devastating effects. Therefore, the life prediction of thermal barrier coating is the key to guarantee the safe service of aero-engine and gas turbine hot end components. Since the performance of thermal barrier coating is closely related to the parameters, microstructure, etc. during the preparation process, and the service conditions of thermal barrier coating are usually under the condition of multi-field coupling, the life prediction and health management is a challenging task.

[0029] Digital twin technology maps diverse data, including real-world physical models, sensor updates, and operational history, onto a virtual twin model. Through continuous interaction between the physical entity and the virtual model, it dynamically assesses the usage and lifespan of critical components in real time. Currently, digital twin technology has become a key technology in industrial production and intelligent manufacturing. Existing technologies primarily focus on predicting the fatigue life of the base metal materials of hot-end components (such as turbine disks and blades in aero-engines), neglecting the thermal shock life prediction of thermal barrier coatings. However, as crucial heat insulation components on the surface of hot-end metal substrates, the high-temperature thermal shock life of thermal barrier coatings is vital to the structural integrity and safe operation of the components. Therefore, there is an urgent need to propose a method that, considering real-world service conditions and the structural differences in thermal barrier coatings due to varying manufacturing processes, incorporates the concept of digital twins to achieve interactive feedback between the physical entity and the virtual model, thereby accurately predicting the thermal shock life of thermal barrier coatings.

[0030] This application proposes a method, device, and medium for predicting the thermal shock lifetime of thermal barrier coatings based on digital twins. This method can predict the high-temperature thermal shock lifetime of thermal barrier coatings in real time and accurately, providing a basis for the subsequent structural design of thermal barrier coating systems. The following is combined with... Figures 1 to 5 This paper explains a method, apparatus, and medium for predicting the thermal shock lifetime of plasma-sprayed thermal barrier coatings based on digital twins, according to an embodiment of the present invention. Figure 1 An example flowchart of a method for predicting the thermal shock lifetime of plasma-sprayed thermal barrier coatings based on digital twins according to an embodiment of the present invention is provided; Figure 2 An example of a technical roadmap for a method for predicting the thermal shock lifetime of plasma-sprayed thermal barrier coatings based on digital twins according to an embodiment of the present invention is explained; Figure 3 An example of the microstructure of a thermal barrier coating prepared by plasma spraying according to an embodiment of the present invention has been explained; Figure 4 An example of a flowchart for actual thermal shock testing of plasma-sprayed thermal barrier coatings according to an embodiment of the present invention has been explained; Figure 5 An example of a schematic diagram of a terahertz time-domain spectral monitoring system according to an embodiment of the present invention is provided.

[0031] Figure 1 The flowchart of the method for predicting the thermal shock lifetime of plasma-sprayed thermal barrier coatings based on digital twins provided by the present invention specifically includes the following steps:

[0032] S1: Extract thermal barrier coating features from one or more different thermal barrier coating samples. These features include the microstructure and thermodynamic parameters of the corresponding thermal barrier coating. In embodiments of this application, for example, information on the microstructure and mechanical and thermal parameters of plasma-sprayed thermal barrier coatings prepared under different parameters can be obtained based on a large number of actual samples.

[0033] Specifically, the thermal barrier coating samples are obtained based on a large number of different preparation process parameters, and the samples are processed by a standard metallographic process and the internal microstructure of the ceramic layer prepared under different parameters is obtained by a scanning electron microscope (such as S1-1 in Figure 2

[0034] Specifically, as shown in Figure 3 , a plasma sprayed thermal barrier coating sample is formed by fixing the ceramic layer 31 on the metal substrate 33 by the adhesive layer 32. The ceramic layer 31 plays a major role in thermal insulation and is prepared by plasma spraying, with a thickness of 50-500 μm. The metal substrate 33 is preferably a nickel-based superalloy or a single crystal substrate, with a thickness of 3-6 mm. The metal adhesive layer 32 is located between the metal substrate 33 and the ceramic layer 31, and plays a role in increasing the adhesion between the ceramic layer and the metal substrate and increasing the high-temperature oxidation resistance of the substrate.

[0035] Preferably, the preparation method of the metal adhesive layer includes but is not limited to plasma spraying and / or supersonic flame spraying, with a thickness of 50-200 μm.

[0036] Further, the scanning electron microscope image reflecting the internal microstructure of the ceramic layer is converted into a digital image with m x n pixels by using a computational micromechanics method (CMM), which can be represented as a matrix A:

[0037]

[0038] Specifically, the digital image representing the coating prepared under different preparation parameters is converted into a vector graph by using image tracking technology (such as S1-2 in Figure 2

[0039] Specifically, the digital image is imported into modeling software (including but not limited to CAD / SolidWorks, etc.) to be converted into a planar solid, thereby realizing the extraction of the microstructure features of the thermal barrier coating.

[0040] At least the elastic modulus, thermal diffusivity, thermal diffusivity, and density of the thermal barrier coating samples with different microstructures are measured and recorded by using the indentation method, laser flash method, linear thermal expansion coefficient method, and drainage method, respectively.

[0041] S2: Determine the specified conditions during the thermal shock test of the corresponding thermal barrier coating. In the embodiments of the present application, for example, the temperature and time related conditions during the thermal shock test of the thermal barrier coating can be introduced.

[0042] ​​Specifically, considering the working condition during the actual thermal barrier coating thermal shock test process, the actual working condition parameters in the thermal shock test process are introduced, including sample heating time, heating temperature, sample holding time, sample cooling time, and heating and cooling medium types.

[0043] Preferably, the heating medium includes but is not limited to oxygen, propane / aviation kerosene, and the cooling medium includes but is not limited to compressed air.

[0044] As shown in Figure 4 , in the actual sample thermal shock performance test process, the test device achieves the effect of heating the sample through 41 oxygen / propane, detects the temperature change of the sample surface in the thermal shock test period in real time through 43 infrared temperature sensor, and performs 44 thermal shock test when the sample surface temperature meets the set temperature condition. When the sample temperature does not meet the set temperature, the heating medium flow is adjusted through 45 to make the actual temperature of the sample meet the set condition.

[0045] S3: establishing one or more thermal shock life simulation models of thermal barrier coatings with different microstructures based at least in part on the extracted thermal barrier coating features and in combination with the specified conditions. In embodiments of the present application, for example, thermal shock life simulation models of thermal barrier coatings with different microstructure features can be established according to the obtained thermal barrier coating models with different microstructure features, thermodynamic parameter information, and in combination with thermal shock test conditions (such as S3 in Figure 2 , simulation models of the first thermal barrier coating TBC1 and the second thermal barrier coating TBC2).

[0046] In particular, the simulation software used here includes but is not limited to finite element simulation software such as Ansys or Abaqus.

[0047] S4: detecting the crack parameters and stress state at the interface between the ceramic layer and the bond layer of the corresponding thermal barrier coating during the simultaneously performed thermal shock test process, and generating a first data set associated with the evolution of the crack parameters and stress state at the interface between the ceramic layer and the bond layer of the corresponding thermal barrier coating. In embodiments of the present application, for example, the terahertz time-domain spectroscopy monitoring system (as shown in S4 in Figure 2 ) can be used to detect the evolution of the crack parameters (such as crack length, width, depth, etc.) and stress state at the interface between the ceramic layer and the bond layer of the sample during the thermal shock test process during the simultaneously performed actual thermal shock test process. The detected data can be referred to as the first data set. Additionally or alternatively, the first data set can be associated with a time stamp when the data is generated, and accordingly, can be stored in a storage module.

[0048] Specifically, in the actual high-temperature thermal shock test of all the thermal barrier coating samples in S4, the test conditions / environmental parameters are consistent with the input conditions / environmental parameters in the simulation model. Further, the crack parameters (e.g., crack length) and stress state at the interface between the ceramic layer and the bond coat layer of all the thermal barrier coating samples in the actual thermal shock test are detected in real time using the terahertz time-domain spectroscopy technology in the reflection mode to obtain a real-time data set.

[0049] In particular, as shown in Figure 5 , the terahertz time-domain spectroscopy monitoring system includes a femtosecond laser module 51, a terahertz time-domain spectroscopy (THz-TDS) module 52, a delay line module 53, a transmitter module 54, and a receiver module 55. The femtosecond pulse laser emitted by the femtosecond laser 51 is divided into two beams, one of which is the pump light, which is incident on the terahertz transmitter module 54 after passing through the optical delay line module 53. The photoconductive antenna radiates terahertz pulses after being excited by the pump light, and the terahertz pulses enter the spatial light path transmission system and are reflected on the sample surface. The other beam is the probe light, which is emitted from the beam splitter to drive the terahertz probe source. In the receiver module 55, the signal is decomposed using the wavelet signal processing method to obtain the approximate coefficients and detail coefficients after signal decomposition, which are reconstructed and filtered, and then the Hanning window function is used for frequency domain deconvolution to extract the evolution of the crack length and stress state at the interface between the ceramic layer and the bond coat layer during the actual thermal shock test with the thermal shock cycle.

[0050] In particular, the femtosecond laser pulse width, center wavelength, repetition frequency, spectral full width at half maximum, and output power include but are not limited to 80 fs, 800 nm, 76 MHz, 11 nm, and 1.1 W, respectively. To ensure the reliability of the test, each sample is tested at 5 non-overlapping positions, and the data set at each position is obtained by 256 scans, and the average value is finally taken. It should be understood that by using the terahertz time-domain spectroscopy technology to detect the crack parameters (e.g., crack length) and stress state at the interface between the ceramic layer and the bond coat layer of the thermal barrier coating sample, the effect of not damaging the thermal barrier coating sample can be achieved.

[0051] S5: Real-time interaction of the first data set with a corresponding thermal shock life simulation model in one or more thermal shock life simulation models to generate a corresponding digital twin model of the thermal barrier coating. In the embodiments of the present application, for example, the evolution of the crack parameters and stress state at the interface between the ceramic layer and the bond coat layer during the actual thermal shock test detected by the terahertz time-domain spectroscopy monitoring system can be interacted with the simulation model in real time to obtain a thermal barrier coating digital twin model (as shown in S5 in Figure 2 ).

[0052] Specifically, the data of the crack parameters and stress evolution at the interface between the ceramic layer and the bond layer monitored based on terahertz in S4 is input into the simulation model obtained in S3, so as to correct the first parameter set of the simulation parameter model associated with the evolution of the crack parameters and the stress state. In the embodiments of the present application, the first parameter set of the digital twin model can be replaced by the crack parameters and stress evolution results monitored by terahertz. Thus, the stress state of the thermal barrier coating sample during the thermal shock test process (S5-2 in Figure 2 ) and the crack evolution (S5-1 in Figure 2 ) are obtained, and a corrected digital twin model is generated. Additionally or alternatively, the digital twin model is a model with real-time data attributes. Additionally or alternatively, the digital twin model is periodically or non-periodically updated at different time periods during the actual thermal shock test process.

[0053] Preferably, in the embodiments of the present application, the digital twin model described in S5 includes but is not limited to a thermal-mechanical coupling analysis model, a stress field analysis model, a structural damage evolution model, or a combination thereof.

[0054] S6: updating the digital twin model based at least in part on a second data set associated with the evolution of the physical characteristics of the corresponding thermal barrier coating at different time periods during the thermal shock test process. In the embodiments of the present application, for example, the digital twin model can be updated according to the physical characteristics of the thermal barrier coating detected during the actual test process, such as microstructure evolution, mechanical property evolution data and coating spalling damage evolution data.

[0055] Specifically, using a thermal shock simulation test device, the thermal shock test working condition is set according to the conditions described in S2, and the sample is repeatedly subjected to high-temperature thermal shock cycle test until the coating spalling failure, and the failure condition threshold is that the spalling area is greater than 20% of the total area of the coating. It should be understood that other numerical values of the failure condition threshold can also be used. By sampling and detecting the thermal barrier coating actual sample at different time periods during the high-temperature thermal shock test, the microstructure evolution, mechanical property evolution, damage state evolution data of the thermal barrier coating sample during the high-temperature thermal shock process can be obtained, which can be referred to as a second data set.

[0056] In particular, the evolution of these structure and performance parameters includes at least the evolution of the porosity of the ceramic layer, the evolution of the hardness, the elastic modulus and the fracture toughness of the ceramic layer, the evolution of the crack length in the ceramic layer, and the evolution of the spalling area in the ceramic coating during the high-temperature thermal shock process.

[0057] Further, the measured results are input into the digital twin model, thereby further updating the second parameter set associated with the physical characteristics of the digital twin model, and a more accurate and real-time synchronized digital twin model is obtained. In embodiments of the present application, the measured results can be used to replace the second parameter set associated with the physical characteristics of the digital twin model. It should be understood that the modification and update of S5 and S6 described above can be performed synchronously or in any order.

[0058] S7: performing the thermal shock life prediction of the thermal barrier coating based on the updated digital twin model. For example, the thermal shock life prediction can be associated with the preparation process parameters of the thermal barrier coating. In embodiments of the present application, for example, a digital twin life prediction model from the plasma sprayed thermal barrier coating preparation process parameters to the thermal shock life can be established according to the modified digital twin model, and the model is used for thermal shock life prediction.

[0059] Specifically, as shown in Figure 2 The digital twin model is further modified and updated by the performance evolution data during the sample actual thermal shock test, thereby being able to more accurately perform the thermal shock life prediction of the thermal barrier coating.

[0060] In particular, the above method can be used for health monitoring of the engine thermal barrier coating test or service.

[0061] The present application provides a digital twin-based plasma sprayed thermal barrier coating thermal shock life prediction method. The crack length and stress state evolution of the ceramic layer and the bonding layer interface of the thermal barrier coating sample with different microstructures under different process plasma spraying parameters during the thermal shock test are detected by terahertz time domain spectroscopy. The detection data are used to modify the finite element simulation model, thereby obtaining a digital twin model with real-time characteristics. Moreover, the digital twin model is continuously updated and modified by the measured data of the sample microstructure, mechanical properties, etc. during the thermal shock test, thereby obtaining a plasma sprayed thermal barrier coating thermal shock life prediction method. A full-cycle thermal barrier coating life prediction method from the plasma sprayed thermal barrier coating process parameters to the thermal shock life is established, thereby guiding the preparation of the actual thermal barrier coating.

[0062] Furthermore, embodiments of the present application also disclose a computer-readable storage medium including computer executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method of the embodiments herein.

[0063] In addition, embodiments of the present application also disclose a device including a processor and a memory storing computer executable instructions, which, when executed by the processor, cause the processor to perform the method of the embodiments herein.

[0064] In addition, the embodiments of the present application also disclose a device for thermal shock life prediction of thermal barrier coatings based on digital twinning, which comprises: means for extracting thermal barrier coating features of one or more different thermal barrier coating samples, the thermal barrier coating features comprising microstructures and thermodynamic parameters of corresponding thermal barrier coatings; means for determining specified conditions during a thermal shock test of the corresponding thermal barrier coatings; means for establishing one or more thermal shock life simulation models of thermal barrier coatings with different microstructures based at least in part on the extracted thermal barrier coating features and in combination with the specified conditions; means for detecting crack parameters and stress states at a ceramic layer and a bond layer interface of a corresponding thermal barrier coating during a synchronously performed thermal shock test and generating a first data set associated with evolution of the crack parameters and stress states at the ceramic layer and the bond layer interface of the corresponding thermal barrier coating; means for real-time interaction of the first data set with a corresponding thermal shock life simulation model in the one or more thermal shock life simulation models to generate a digital twinning model of the corresponding thermal barrier coating; means for updating the digital twinning model based at least in part on a second data set associated with evolution of physical features of the corresponding thermal barrier coating at different time periods during the thermal shock test; and means for performing the thermal shock life prediction of the thermal barrier coating based on the updated digital twinning model.

[0065] The above describes a method, device and medium for thermal shock life prediction of thermal barrier coatings based on digital twinning according to the present application, which has at least the following advantages over the prior art:

[0066] (1) The influence of microstructure differences of coatings caused by different preparation process parameters on high-temperature thermal shock life of thermal barrier coating systems is considered;

[0067] (2) A digital twinning model considering actual thermal shock working conditions, actual microstructures and performance evolution is established by using a terahertz time-domain spectroscopy monitoring technology;

[0068] (3) The digital twinning model is updated and corrected by comparison and interaction with measured data, and complex factors such as microstructure, material performance and crack evolution on the thermal shock life of thermal barrier coatings can be comprehensively considered;

[0069] (4) The high-temperature thermal shock life of thermal barrier coatings is predicted in real time and accurately, and a basis is provided for subsequent structure design of thermal barrier coating systems.

[0070] Throughout this specification particular formulations can have been referred to as "examples." Accordingly, the application as can be embodied and / or performed has significant utilities in all. Moreover, it should be noted that references to particular examples are only to demonstrative the examples are included within at least one example. Thus, use of these phrases therein has not only to refer to one example. Also, the described feature, structure, or characteristic in one or more examples can be combined in any suitable manner.

[0071] The various steps and modules of the methods and apparatus described above can be implemented in hardware, software, or a combination thereof. If implemented in hardware, the various illustrative blocks, modules, and circuits described in connection with the present disclosure can be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic component, hardware component, or any combination thereof designed to perform the functions described herein. The general purpose processor can be a microprocessor, a controller, a controller with a state machine, etc. If implemented in software, the various illustrative blocks, modules, and circuits described in connection with the present disclosure can be stored in, or transmitted over, as one or more instructions or code on a computer-readable medium. The software module(s) of the present disclosure can be a computer program, a piece of code, an instruction, or some combination thereof, for embodying a solution to a problem. A computer-readable medium can include a computer program product apparatus having a computer program code thereon for execution by the computer or processor. The media and data can be those specially adapted to serve a function described herein. Moreover, the software can be transmitted over a communication network using a transmission medium. Examples of a transmission medium include a wire, cable, or other optical medium, a wireless medium, or the like. Examples of a communication network include the Internet, a telephone network, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a wireless network, a public switched telephone network (PSTN), or the like.

[0072] The numerical values given in the various embodiments are only examples and do not limit the scope of the application. Moreover, as a whole technical solution, there are other components or steps that are not listed in the claims or the specification of the application. Moreover, a single name of a component does not exclude other names of the component.

[0073] It should also be noted that the embodiments can be described as a process which is depicted as a flowchart, a flow diagram, a structure diagram, or a block diagram. Although a flowchart can describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations can be re-arranged.

[0074] The disclosed methods, apparatus, and systems should not be limited in any way by the above description. Rather, the disclosure covers all novel and non-obvious features and aspects of the various disclosed embodiments alone and in various combinations and sub-combinations thereof. The disclosed methods, apparatus, and systems are not limited to any specific aspect or feature or combination thereof, nor do the any disclosed embodiments require the presence of any particular advantage or solve a particular or all technical problems.

[0075] The present application is not limited to the specific embodiments described above, which are merely illustrative of only a few of the many embodiments made by the inventors. Numerous modifications can be made by those skilled in the art without departing from the true spirit and scope of the application described herein and claimed by the appended claims.

[0076] Those skilled in the relevant art can recognize, however, that the examples can be practiced without one or more of the specific details, or with other methods, resources, materials, etc. In other instances, well known structures, resources, or operations have not been shown or described in detail merely to observe obscuring aspects of the examples.

[0077] While embodiments and applications have been illustrated and described, it is understood that the embodiments are not limited to the precise configuration and resources described above. Various modifications, substitutions, and alterations can be made by those skilled in the art without departing from the scope of the disclosed embodiments.

[0078] The terms "and", "or", and "and / or", as used herein, can include a variety of meanings that also are expected to depend at least in part upon the context in which such terms are used. Typically, "or" if used to associate a list, such as A, B, or C, is intended to mean A, B, and C, here used in the inclusive sense, as well as A, B or C, here used in the exclusive sense. In addition, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular or can be used to describe a feature, structure, or characteristic in the plural, depending on the context in which such term is used. It is also noted that the singular forms "a", "an", and "the" include their plural aspects and vice versa, unless the context clearly dictates otherwise.

[0079] While there have been illustrated and described what are presently considered to be example features, it will be understood by those skilled in the art that various other modifications can be made, and equivalents employed, without departing from the claimed subject matter. Additionally, many modifications can be made to adapt a particular situation to the teachings of the claimed subject matter without departing from the central concept described herein.

Claims

1. A method for predicting the thermal shock lifetime of thermal barrier coatings based on digital twins, comprising: Extract thermal barrier coating features from one or more different thermal barrier coating samples, wherein the thermal barrier coating features include the microstructure and thermodynamic parameters of the corresponding thermal barrier coating; Determine the specified conditions during the thermal shock testing process of the corresponding thermal barrier coating; At least in part, based on the extracted thermal barrier coating features and in combination with the specified conditions, one or more thermal shock lifetime simulation models of thermal barrier coatings with different microstructures are established. During the concurrent thermal shock test, crack parameters and stress states at the interface between the ceramic layer and the adhesive layer of the corresponding thermal barrier coating are detected, and a first set of data is generated that is associated with the evolution of crack parameters and stress states at the interface between the ceramic layer and the adhesive layer of the corresponding thermal barrier coating. The first data set is interacted in real time with the corresponding thermal shock life simulation model in one or more thermal shock life simulation models to generate a digital twin model of the corresponding thermal barrier coating. During different time periods of the thermal shock assessment process, the digital twin model is updated at least in part based on a second set of data associated with the evolution of the physical characteristics of the corresponding thermal barrier coating. as well as The thermal shock lifetime prediction of the thermal barrier coating is performed based on the updated digital twin model.

2. The method as described in claim 1, characterized in that, Extracting the microstructure of the thermal barrier coating includes: The scanning electron microscope image reflecting the microstructure inside the ceramic layer is converted into a digital image with m×n pixels using computational micromechanics methods. The digital images of the thermal barrier coatings prepared under different preparation parameters are converted into vector images using image tracking technology; The digital image is imported into modeling software and converted into a planar entity.

3. The method as described in claim 1, characterized in that, The specified conditions include one or more of the following: Sample heating time, sample heating temperature, sample holding time, sample cooling time, and types of sample heating and cooling media.

4. The method as described in claim 1, characterized in that, During the thermal shock testing process of the thermal barrier coating sample, the operating conditions and / or environmental parameters of the thermal shock testing process are kept consistent with the operating conditions / environmental parameters input into the thermal shock life simulation model.

5. The method as described in claim 1, characterized in that, Generating the first data set associated with the corresponding thermal barrier coating includes: The crack parameters and stress state at the interface between the ceramic layer and the adhesive layer of all thermal barrier coating samples during the thermal shock assessment process were detected using a terahertz time-domain spectroscopy monitoring system in reflection mode. The detected data is used as the first data set, and the first data set is associated with the timestamp when the data was generated.

6. The method as described in claim 5, characterized in that, The detection of crack parameters and stress state at the interface between the ceramic layer and the adhesive layer of the corresponding thermal barrier coating includes: Femtosecond pulsed lasers are emitted from the femtosecond laser of the terahertz time-domain spectroscopy monitoring system. The femtosecond pulsed laser is split into a first beam and a second beam. The first beam is a pump beam, which, after passing through the optical delay line module of the terahertz time-domain spectroscopy monitoring system, is incident on the terahertz emitter module of the system. The photoconductive antenna, excited by the pump beam, radiates a terahertz pulse. This pulse enters the spatial optical path transmission system of the terahertz time-domain spectroscopy monitoring system and is reflected from the surface of the thermal barrier coating sample. The second beam is a probe beam, which exits from the beam splitter of the terahertz time-domain spectroscopy monitoring system and drives the terahertz detector source. The receiver module of the terahertz time-domain spectral monitoring system decomposes the signal using wavelet signal processing to obtain approximate coefficients and detail coefficients after signal decomposition. These are then reconstructed and filtered, and the signal is deconvolved in the frequency domain using the Hanning window function. This allows the detection of the first set of data related to the evolution of crack parameters and stress state at the interface between the ceramic layer and the adhesive layer during the thermal shock test process, as the thermal shock test cycle continues.

7. The method as described in claim 1, characterized in that, The generation of the corresponding digital twin model of the thermal barrier coating further includes: The first data set is input into the thermal shock life simulation model to correct the first parameter set of the thermal shock life simulation model associated with the evolution of crack parameters and stress state.

8. The method as described in claim 7, characterized in that, The digital twin model includes one or more of the following: a thermo-mechanical coupling analysis model, a stress field analysis model, and a structural damage evolution model.

9. The method as described in claim 1, characterized in that, Updating the digital twin model based on the second dataset includes: The second set of data actually measured is input into the digital twin model to update the second set of parameters of the digital twin model associated with the physical feature.

10. The method as described in claim 9, characterized in that, The evolution of the physical characteristics of the thermal barrier coating includes the evolution of its microstructure, mechanical properties, and damage state, and further includes one or more of the following: During the thermal shock process, the ceramic layer porosity evolves, as do the ceramic layer hardness, elastic modulus, fracture toughness, crack length within the ceramic layer, and spalling area within the ceramic coating.

11. An apparatus for predicting the thermal shock lifetime of a thermal barrier coating based on a digital twin, comprising a processor and a memory, the memory storing program instructions; the processor executing the program instructions to implement the method of any one of claims 1 to 10.

12. A non-transitory computer-readable storage medium for storing instructions, which, when executed by a computer, The computer is made to perform a method for predicting the thermal shock lifetime of thermal barrier coatings based on digital twins, as described in any one of claims 1 to 10.

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