Method for optimizing thickness of intelligent self-repairing thermal barrier coating with multilayer structure

Abstract

The invention belongs to the technical field of coating optimization, and particularly relates to a multilayer structure intelligent self-repairing thermal barrier coating thickness optimization method which comprises the steps that a plurality of self-repairing thermal barrier coatings with different layer thicknesses serve as optimization test groups respectively; establishing and carrying out residual stress analysis and heat insulation analysis on the self-repairing thermal barrier coatings of the different optimization test groups on the basis of a finite element model to respectively obtain residual stress and effective heat conductivity corresponding to the different optimization test groups under the preset working condition; performing standardization processing on the residual stress and the effective heat conductivity coefficient corresponding to the different optimization test groups under the preset working condition to obtain a target value; and the minimum distance between the ideal solutions corresponding to the different optimization test groups is obtained based on the target value, and the thickness contained in the optimization test group corresponding to the minimum distance is the optimized thickness of the self-repairing thermal barrier coating. The service life of the thermal barrier coating can be prolonged.

Description

Technical Field

[0001] The present invention belongs to the technical field of coating optimization, and in particular relates to a method for optimizing the thickness of a multi-layered intelligent self-repairing thermal barrier coating. Background Art

[0002] As an important inorganic coating material, thermal barrier coatings are widely used in high-temperature alloy blades or other high-temperature hot end components of aircraft engines and ground heavy-duty gas turbines (referred to as "two engines"), and play the role of high-temperature heat insulation, anti-oxidation, anti-wear or corrosion for blades or other high-temperature hot end components. With the continuous increase in performance requirements for the "two engines", higher and higher requirements are also placed on thermal barrier coatings.

[0003] Long-life and highly reliable thermal barrier coatings have always been an urgent need for thermal barrier coatings for "two machines". To achieve the long life of thermal barrier coatings, it is necessary to try to extend the life of the coatings and extend and postpone the critical time point corresponding to their failure. The failure of thermal barrier coatings is usually caused by the initiation and expansion of cracks. To postpone the failure of the coating, it is necessary to delay or inhibit the expansion of cracks inside the coating.

[0004] Based on this, the basic idea of ​​material self-repair is used to achieve the effect of extending the life of the material. Self-repairing materials (also known as self-healing materials) are a type of smart materials (Smart Materials) that have structural self-healing capabilities. Materials with this ability can repair their internal damage caused by long-term external mechanical effects. If the material itself can complete the self-repair process under certain conditions or stimuli after being damaged and return to its undamaged state. This is an ideal self-repairing material, but it is difficult to achieve 100% perfect repair in reality. Therefore, materials that can self-repair to a certain extent are generally classified as self-repairing materials.

[0005] At present, the high-temperature service life extension of thermal barrier coatings mainly adopts purified granulated raw material powders, optimizes the coating spray feeding structure, optimizes the coating preparation process, and designs the internal defect structure of the coating, such as optimizing the pore structure arrangement, crack morphology, and size distribution. For example, for thick coatings, the purpose of extending the service life of the coating is usually achieved by controlling the density and length of the partitioned cracks. Since the initiation and expansion of cracks is the inherent cause of coating failure, this type of method does not fundamentally inhibit the dynamic process of crack expansion, and is a passive process, which cannot fundamentally achieve the extension of the coating life. Summary of the invention

[0006] The present invention proposes a method for optimizing the thickness of a multi-layered intelligent self-repairing thermal barrier coating, which can extend the service life of the thermal barrier coating.

[0007] The present invention provides a method for optimizing the thickness of a multi-layer structured intelligent self-repairing thermal barrier coating, comprising: Several self-healing thermal barrier coatings with different layer thicknesses were used as optimization test groups; The residual stress analysis and thermal insulation analysis of the sprayed coatings of the self-repairing thermal barrier coatings of different optimized test groups were established and carried out based on the finite element model, and the residual stress and effective thermal conductivity of the different optimized test groups under the preset working conditions were obtained respectively; The residual stress and effective thermal conductivity of different optimization test groups under preset working conditions are standardized to obtain target values; The minimum distance between the ideal solutions corresponding to different optimization test groups is obtained based on the target value. The thickness contained in the optimization test group corresponding to the minimum distance is the thickness of the optimized self-healing thermal barrier coating.

[0008] As an optional embodiment, the layers of different layer thicknesses include at least a bonding layer, a ceramic layer and a self-healing layer; The plurality of self-repairing thermal barrier coatings with different layer thicknesses are respectively used as optimization test groups, including: setting the bonding layer thickness to a predetermined bonding layer thickness; Setting the thickness of the ceramic layer within a first preset thickness range; Setting the thickness of the self-repairing layer within a second preset thickness range; The thickness of the bonding layer, the thickness of the ceramic layer and the thickness of the self-healing layer are combined according to a preset combination method to obtain different optimized test groups.

[0009] As an optional implementation manner, the first preset thickness range is 120 μm-280 μm; and / or the second preset thickness range is 20 μm-180 μm.

[0010] As an optional implementation, a residual stress analysis of the sprayed coating is performed on the self-repairing thermal barrier coatings of different optimization test groups based on a finite element model, including: Obtaining residual stresses corresponding to different optimization test groups based on the finite element model, wherein the residual stresses at least include a first principal stress and an axial stress; The residual stress is subjected to stress analysis and screening, and the first principal stress and axial stress obtained by screening are used as the residual stress corresponding to different optimization test groups under preset working conditions.

[0011] As an optional implementation, a finite element model is established and thermal insulation analysis of the sprayed coatings of the self-repairing thermal barrier coatings of different optimization test groups is performed respectively, including: Set the corresponding temperature field based on the finite element model; Based on the temperature field, the effective thermal conductivity is obtained.

[0012] As an optional implementation manner, obtaining the effective thermal conductivity based on the temperature field includes: Get the ambient temperature T a ; Get the model thickness d a ; Get the convective heat transfer coefficient λ between the bottom of the model and the environment of the finite element model b ; Get the top temperature T of the finite element model after a preset heating cycle c and bottom temperature T b ; At ambient temperature T a , model thickness d a , the convective heat transfer coefficient λ between the bottom of the model and the environment of the finite element model b , the top temperature T of the finite element model after the preset heating cycle c and bottom temperature T b As the temperature field, the effective thermal conductivity k is obtained eff , the effective thermal conductivity k eff As the effective thermal conductivity under preset working conditions for different optimized test groups:

[0013] As an optional implementation, the residual stress and effective thermal conductivity of different optimization test groups under preset working conditions are standardized to obtain target values, including: Constructing a four-variable data set based on the finite element model ('t TC '、'S 22 '、'S I ','k eff '), where 't TC 'Identifier for the optimized test group;'S 22 ' is the axial stress; 'S I ' is the first principal stress; 'k eff ' is the effective thermal conductivity of the coating; Each variable is standardized to obtain the distance D between the target value and the ideal point. The standardization method includes: Where D is the distance between the target value and the ideal point; f i is the i-th target value; f i max is the maximum value of the single objective optimization of the i-th objective function; f i minis the minimum value of the single objective optimization of the i-th objective function; n is the number of objective functions.

[0014] As an optional implementation, the minimum distance between the ideal solutions corresponding to different optimization test groups is obtained based on the target value, and the thickness contained in the optimization test group corresponding to the minimum distance is the optimized thickness of the self-healing thermal barrier coating, including: Based on the distance D between the target value of each optimization test group and the ideal point, the corresponding ideal solution is obtained, and the ideal solution is the minimum value of all normalized indicators, representing the point of the optimal state achieved on all performance indicators; Get the distance D between the target value and the ideal point and the minimum Euclidean distance between the ideal solution.

[0015] As an optional implementation manner, the preset working conditions include preset initial conditions and preset boundary conditions.

[0016] As an optional embodiment, the self-repairing thermal barrier coating includes a three-layer structure, which are, from top to bottom: a YSZ layer, a TAZ (10-20wt% TiC + 5-10wt% A12O3 + 70-85wt.% YSZ) layer and a NiCoCrA1Y layer, wherein the NiCoCrA1Y layer is NiCoCrA1Y modified with oxide rare earth.

[0017] Compared with the prior art, the present invention fundamentally inhibits the dynamic process of crack propagation and is a strategy for actively inhibiting crack propagation. When cracks occur inside the coating, the intelligent self-repairing mechanism is activated to inhibit crack propagation. The present invention is based on the collaborative optimization criterion of better thermal insulation of the coating and lower overall residual stress. The composition and thickness ratio of each layer of the intelligent self-repairing thermal barrier coating with a multi-layer structure are designed and optimized by using finite element simulation calculation combined with an ideal solution. The self-repairing thermal barrier coating can meet both low residual stress and high thermal insulation performance, which can provide an important theoretical basis for the thickness control of the actual coating prepared by thermal spraying. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for optimizing thickness of a multi-layered intelligent self-repairing thermal barrier coating in one embodiment of the present application is shown.

[0019] Figure 2 A schematic diagram of residual stress calculation results of a self-repairing thermal barrier coating in one embodiment of the present application is shown.

[0020] Figure 2 (a) shows the residual stress S in one embodiment of the present application. 11 Schematic diagram of calculation results.

[0021] Figure 2(b) shows the residual stress S in one embodiment of the present application. 22 Schematic diagram of calculation results.

[0022] Figure 2 (c) shows the residual stress S in one embodiment of the present application. 12 Schematic diagram of calculation results.

[0023] Figure 2 (d) shows the residual stress S in one embodiment of the present application. I Schematic diagram of calculation results.

[0024] Figure 3 The stress distribution diagram of different interfaces of the intelligent self-healing coating in one embodiment of the present application is shown.

[0025] Figure 3 (a) shows the stress distribution diagram of the YSZ / TAZ interface in one embodiment of the present application.

[0026] Figure 3 (b) shows the stress distribution diagram of the TAZ / BC interface in one embodiment of the present application.

[0027] Figure 3 (c) shows the stress distribution diagram of the BC / SUB interface in one embodiment of the present application.

[0028] Figure 4 A schematic diagram showing the calculation results of the thermal insulation behavior of a self-repairing thermal barrier coating in an embodiment of the present application is shown.

[0029] Figure 4 (a) shows a cloud diagram of the overall temperature distribution of the coating in one embodiment of the present application.

[0030] Figure 4 (b) shows a temperature-time variation curve of the coating surface and the back side of the substrate in one embodiment of the present application.

[0031] Figure 5 A schematic diagram showing the thickness of different layers in an embodiment of the present application is shown.

[0032] Figure 5 (a) shows the relationship between the thickness of the ceramic layer and S in one embodiment of the present application. 22 and a schematic diagram of thermal conductivity.

[0033] Figure 5 (b) shows the relationship between the thickness of the ceramic layer and S in one embodiment of the present application. I and a schematic diagram of thermal conductivity.

[0034] Figure 5 (c) shows a ceramic layer thickness display diagram for optimal comprehensive new energy in one embodiment of the present application. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0036] The main function of thermal barrier coating is to reduce the heat transfer of high-temperature combustion gas to the hot end components of the engine, thereby reducing the operating temperature of the components, preventing high-temperature alloy blades from directly contacting high-temperature combustion gas, and improving the service life and reliability of the components. Thermal barrier coating is sprayed, and the coating prepared after spraying is called sprayed coating.

[0037] The structural optimization of multi-layered intelligent self-repairing coatings requires comprehensive consideration of multiple performance indicators, including thermal insulation performance and overall residual stress levels. Excellent thermal insulation performance can ensure the application of thermal barrier coatings under higher temperature conditions, which further prevents high-temperature alloy blades from being ablated by heat flow, increases the temperature bearing capacity of high-temperature blades, and will greatly improve the thrust-to-weight ratio and thermal engine efficiency of turbine engines. In addition, the influence of residual stress generated during the preparation process on the deformation and failure of ceramic coatings cannot be ignored. These two indicators are usually contradictory or restrictive.

[0038] Excellent thermal insulation performance can ensure the application of thermal barrier coatings under higher temperature conditions. In addition, the residual stress generated during the preparation process cannot be ignored in the deformation and failure of ceramic coatings. For a given coating material, the thermal insulation performance of the coating increases with the increase of coating thickness, but the thermal mismatch stress inside the coating (including the interface) may also increase. Therefore, a balance needs to be achieved between thermal insulation performance and overall residual stress level. Determining the appropriate coating thickness is a multi-objective optimization problem. Finding a coating thickness that can meet both low residual stress and high thermal insulation performance is a must for studying thermal barrier coatings.

[0039] In actual production, the control of the thickness of general thermal barrier coatings or self-healing thermal barrier coatings is based on experience, and the thickness control is completely artificial. The present invention provides a method for controlling the thickness of a self-healing thermal barrier coating. Although the results obtained are partially dependent on finite element calculation results and the ideal point method, it is precisely with the help of the combined use of these methods that data-driven and high-throughput material calculations are realized and ultimately the thickness optimization of the self-healing coating is achieved, which subverts the traditional experience-driven concept of coating preparation and provides theoretical support and reference for the preparation of high-performance coatings.

[0040] In view of this, if Figure 1 As shown, the present invention provides a method for optimizing the thickness of a multi-layer structured intelligent self-repairing thermal barrier coating, comprising the following steps.

[0041] Step S100: multiple self-repairing thermal barrier coatings with different layer thicknesses are respectively used as optimization test groups.

[0042] Specifically, the self-repairing thermal barrier coating includes a three-layer structure, which are, from top to bottom: a YSZ layer, a TAZ (10-20wt% TiC + 5-10%wt% A12O3 + 70-85wt.% YSZ) layer and a NiCoCrA1Y layer (NiCoCrA1Y modified by oxide rare earth (CeO2, HfO2, etc.)), that is, NiCoCrAIY layer → TAZ (10-20wt% TiC + 5-10%wt% A12O3 + 70-85wt.% YSZ) layer → YSZ layer are deposited on the high-temperature alloy substrate, i.e., the SUB layer, in sequence, wherein the YSZ layer is a ceramic layer (TC layer), and the (10-20wt% TiC + 5-10%wt% A12O3 + 70-85wt.% YSZ) layer is a self-repairing layer (TAZ layer).

[0043] In one embodiment of the present invention, the self-repairing thermal barrier coating includes a three-layer structure, which is sequentially from top to bottom: YSZ layer → TAZ (20wt% TiC + 10wt% Al2O3 + 70wt.% YSZ) layer → NiCoCrAlY layer, that is, NiCoCrAlY layer → TAZ (20wt% TiC + 10wt% Al2O3 + 70wt.% YSZ) layer → YSZ layer is sequentially deposited on the high-temperature alloy substrate, namely the SUB layer, wherein the YSZ layer is a ceramic layer (TC layer), the TAZ (20wt% TiC + 10wt% Al2O3 + 70wt.% YSZ) layer is a self-repairing layer (TAZ layer), and the NiCoCrAlY layer is a bonding layer also known as a BC layer. In addition, in the self-repairing layer, external oxygen reacts with the bonding layer through the pores to generate a TGO layer, and the TGO layer is between the bonding layer and the self-repairing layer.

[0044] Step S200: Establishing and performing residual stress analysis and thermal insulation analysis on the sprayed coatings of the self-repairing thermal barrier coatings of different optimized test groups based on the finite element model, respectively, and obtaining the residual stress and effective thermal conductivity corresponding to the different optimized test groups under preset working conditions;

[0045] The finite element model is specifically a finite element model based on a four-node linear axisymmetric heat transfer quadrilateral element (DCAX4), which can calculate the effective thermal conductivity to evaluate the thermal insulation capacity of the coating.

[0046] Step S300: normalizing the residual stress and effective thermal conductivity of different optimization test groups under preset working conditions to obtain target values.

[0047] Specifically, the preset working conditions include preset initial conditions and preset boundary conditions.

[0048] When standardizing the residual stress and effective thermal conductivity of different optimized test groups under preset working conditions, the identifier of each optimized test group, such as the thickness of the ceramic layer, will also be considered. The standardization process is called normalization. In order to make different performance indicators have the same weight, the value of each performance indicator is normalized.

[0049] Step S400: based on the target value, the minimum distance between the ideal solutions corresponding to different optimization test groups is obtained, and the thickness contained in the optimization test group corresponding to the minimum distance is the optimized thickness of the self-healing thermal barrier coating.

[0050] Specifically, the ideal solution, also called the ideal point, is the minimum value of all normalized indicators and represents the point of optimal state achieved on all performance indicators. Based on the target values ​​of each optimization test group, the corresponding ideal solution is obtained, which means that the coating thickness corresponding to the target value has the best comprehensive performance.

[0051] The present invention solves the problem that various solutions in the coating thickness optimization problem often cannot simultaneously meet the optimal values ​​of all objectives. By calculating the distance between each solution and the ideal point and selecting the solution with the smallest distance as the optimal solution, multiple conflicting objectives can be effectively balanced to find the solution with the best comprehensive performance.

[0052] In one embodiment of the present invention, the self-repairing agent in the self-repairing thermal barrier coating is TiC.

[0053] Specifically, the Al2O3 in the self-repairing layer can effectively reduce the diffusion of external oxygen into the interface and reduce the formation rate of the TGO layer. The YSZ layer is added to reduce the mismatch of the thermal expansion coefficient and reduce thermal stress. The self-repairing effect of the intelligent self-repairing thermal barrier coating is mainly achieved by the TiC particles in the TAZ layer.

[0054] Specifically, in the prior art, there are many materials used for self-repair of thermal barrier coatings, in addition to TiC, there are also SiC, MoSi2, etc., but the introduction of silicon elements will damage other properties of the coating, such as promoting sintering, etc. On the whole, TiC is the best self-repairing agent with better self-repairing effect, and is a very good self-repairing agent material.

[0055] When the coating is exposed to a high-temperature oxidizing environment, TiC will react with oxygen in the air to form TiO2 particles, which can fill the crack gaps. Because the density of TiO2 particles is lower than that of TiC, a volume expansion effect and a compressive stress field will occur around the cracks, which will help to close the cracks. At the same time, the formation of TiO2 can also reduce the partial pressure of oxygen at the interface between the bonding layer and the YSZ layer, further slowing down the growth of the TGO layer. Among them, when the thermal barrier coating is in service (in a high-temperature environment), the oxygen in the air will react with the Al in the bonding layer through the pores in the ceramic layer to form aluminum oxide, which is the main component of the TGO layer.

[0056] In one embodiment of the present invention, the layers of different layer thicknesses at least include a bonding layer, a ceramic layer and a self-repairing layer; and the plurality of self-repairing thermal barrier coatings with different layer thicknesses are respectively used as an optimization test group, including: setting the bonding layer thickness to a predetermined bonding layer thickness; Setting the thickness of the ceramic layer within a first preset thickness range; Setting the thickness of the self-repairing layer within a second preset thickness range; The thickness of the bonding layer, the thickness of the ceramic layer and the thickness of the self-healing layer are combined according to a preset combination method to obtain different optimized test groups.

[0057] In the present invention, the thickness optimization of the multi-layer structure intelligent self-repairing thermal barrier coating is mainly focused on the TC layer and the TAZ layer. Since there are many studies on the bonding layer NiCoCrAlY layer in thermal barrier coatings, the present invention can select the same bonding layer thickness based on the experience of actual coating preparation combined with literature data, and establish a model by combining different ceramic layer thicknesses and different self-repairing layer thicknesses.

[0058] In an application scenario of the present invention, the predetermined bonding layer thickness is 100 μm, the first preset thickness range is 120 μm-280 μm; and / or the second preset thickness range is 20 μm-180 μm. A total of 9 structurally optimized test comparison groups are set, and the stress calculation and thermal insulation of the self-repairing thermal barrier coating of each thickness optimized experimental group are respectively performed on the sprayed coating for high-throughput calculation and analysis.

[0059] Exemplarily, Table 1 shows the thickness information of each layer of a thickness optimization test group.

[0060] Table 1

[0061] In one embodiment of the present invention, a finite element model is established and the residual stress analysis of the sprayed coatings of the self-repairing thermal barrier coatings of different optimization test groups is performed respectively, including: Obtaining residual stresses corresponding to different optimization test groups based on the finite element model, wherein the residual stresses at least include a first principal stress and an axial stress; The residual stress is subjected to stress analysis and screening, and the first principal stress and axial stress obtained by screening are used as the residual stress corresponding to different optimization test groups under preset working conditions.

[0062] Specifically, in the present invention, the residual stress also includes the normal stress parallel to the interface on the YSZ / TAZ interface, that is, the radial stress S 11 and the normal stress S perpendicular to the YSZ / TAZ interface 12 Preferably, in the residual stress analysis performed in the present invention, only the first principal stress S I and axial stress S 22 .

[0063] like Figure 2 As shown, Figure 2 The residual stress calculation results of the self-repairing thermal barrier coating are shown when the thickness of the TC layer is set to 240 μm and the thickness of the TAZ layer is set to 60 μm. 11 is the radial stress, such as Figure 2 The ceramic layer shown in (a) is mainly compressive stress, the interface between the ceramic layer and the self-repairing layer is compressive stress, the interface between the self-repairing layer and the bonding layer is also compressive stress, and the stress distribution at the substrate near the bonding layer is mainly tensile stress. During the cooling process of the coating, since the thermal expansion coefficient of the TAZ layer is smaller than that of the bonding layer, during the cooling process, the thermal strain of the TAZ layer is smaller than that of the bonding layer. The TAZ layer is compressed by the influence of the bonding layer, and the bonding layer is tensile by the influence of the TAZ layer. Since the thermal expansion coefficients of the TC layer and the TAZ layer are similar, the stress at the interface between the bonding layer and the substrate is consistent. The maximum tensile stress at the interface between the substrate and the bonding layer is 161.95MPa, and the maximum compressive stress at the interface between the self-repairing layer and the bonding layer is -293.84MPa. Figure 2 (b) S 22 S is the axial stress perpendicular to the coating surface. The maximum tensile stress appears in the bonding layer, which is 71.48MPa. 12 Specifically, shear stress, such as Figure 2As shown in (c), the maximum tensile stress exists between the bonding layer and the substrate, and the maximum tensile stress is 71.2MPa. At the same time, the maximum compressive stress also appears near the interface between the bonding layer and the substrate, which is -22.02MPa. Due to the edge effect, the residual stress will be severely concentrated at the edge of the coating, especially near the interface between the coating and the substrate. Although the bonding layer can reduce the residual stress in the coating, the concentration of residual stress mainly exists at the right edge of the finite element model. This phenomenon of alternating tensile and compressive stress concentration in local areas will promote the generation of cracks and propagate along the interface. However, since the surface of the substrate has been sandblasted, the interface stress between the substrate and the bonding layer is generally difficult to cause cracks to initiate and propagate at this interface. Figure 2 (d) is the first principal stress S of the self-repairing coating system I Distribution cloud map, from Figure 2 In (d), it can be seen that the maximum first principal stress is at the BC / SUB interface, which is 181.4 MPa.

[0064] Specifically, Figure 3 As shown, Figure 3 The stress distribution diagrams of the smart self-repairing coating TC / TAZ, TAZ / BC, and BC / SUB interfaces are shown. Figure 3 As shown in (a), on the YSZ / TAZ interface, the normal stress S 11 It shows a relatively uniform distribution, reflecting the uniformity of the compressive stress of the material in this direction. However, the normal stress S perpendicular to the interface 22 The end of the interface near the edge of the coating shows a complex stress change. 22 There is a negative peak first, that is, the compressive stress concentration occurs, and then the stress quickly turns to a positive value and reaches the maximum tensile stress, which indicates that the interface material has experienced a significant thermal contraction mismatch during the cooling process. When the tensile stress near the edge of the interface exceeds the tensile strength of the YSZ coating, it is likely to cause the formation of microcracks, which may expand into macrocracks in continuous thermal cycles, thereby endangering the structural integrity of the coating. 12 A large shear stress concentration appears near the edge of the interface end, which is the main driving force for interface debonding. I A sharp peak appears near the edge at the end of the interface, which may become the starting point of crack formation. Figure 3 As shown in (b), at the TAZ / BC interface, S 11 , S 22 , S 12 , S I The change trend of is consistent with the stress change trend on the YSZ / TAZ interface. Figure 3 As shown in (c), at the BC / SUB interface, S 11The stress shows a relatively constant trend, which is different from the other two interfaces. 22 The peak position of stress moves further toward the end of the interface and close to the edge of the coating, where the level is higher. Therefore, the thickness optimization of the self-healing thermal barrier coating focuses on minimizing S 22 and S I and its peak values ​​to reduce the risk of crack formation and propagation.

[0065] Here, the present invention can improve the structural stability of the coating and extend its service life by reducing the peak values ​​of these key stress parameters.

[0066] In one embodiment of the present invention, a finite element model is established and thermal insulation analysis of the sprayed coatings of the self-repairing thermal barrier coatings of different optimization test groups is performed respectively, including: Set the corresponding temperature field based on the finite element model; Based on the temperature field, the effective thermal conductivity k is obtained. eff .

[0067] Wherein, based on the temperature field, the effective thermal conductivity k is obtained eff ,include: Get the ambient temperature T a ; Get the model thickness d a ; Get the convective heat transfer coefficient λ between the bottom of the model and the environment of the finite element model b ; Get the top temperature T of the finite element model after a preset heating cycle c and bottom temperature T b ; At ambient temperature T a , model thickness d a , the convective heat transfer coefficient λ between the bottom of the model and the environment of the finite element model b , the top temperature T of the finite element model after the preset heating cycle c and bottom temperature T b As the temperature field, the effective thermal conductivity k is obtained eff , the effective thermal conductivity k eff As the effective thermal conductivity under preset working conditions for different optimized test groups:

[0068] Specifically, the present invention uses ABAQUS software to perform finite element simulation on the entire cooling process of the self-repairing thermal barrier coating after it is deposited on the substrate surface. It is assumed that the coating rapidly spreads and shrinks after being deposited on the substrate surface until the two reach an equilibrium temperature. At this time, the entire coating is set to a high temperature of 427°C (simulating the coating preparation temperature, assuming that the coating system is in a zero stress state), and the coating system undergoes natural heat convection with room temperature air (the convection heat transfer coefficient is 10W / (m 2 ·K), the atmospheric temperature was set to 25°C, and the sample was cooled for 18000s after preparation.

[0069] In terms of performance, thermal insulation is one of the most important requirements for thermal barrier coatings. The use of high thermal insulation coatings can reduce the temperature of the substrate and extend its service life. The finite element model based on the four-node linear axisymmetric heat transfer quadrilateral unit (DCAX4) can calculate the effective thermal conductivity to evaluate the thermal insulation capacity of the coating. The heat transfer behavior of the self-healing thermal barrier coating is mainly heat conduction heat transfer and convection heat transfer. Radiation heat transfer occurs above 1300°C and can be ignored.

[0070] Therefore, the finite element model established in the present invention only considers heat conduction and heat convection. The upper surface temperature (T s ) is specified as 1150°C, the ambient temperature (T a ) is 25℃. The total time of the insulation model is 260s, including a 60s heating cycle and a 200s insulation cycle. The convection heat transfer coefficient between the bottom and the environment is 300W / (m 2 ·K)(λ b ). All nodes on the right boundary are set to adiabatic boundary conditions. According to the obtained temperature field, the effective thermal conductivity is the effective thermal conductivity k eff Calculated according to the following formula: Among them, λ b is the convective heat transfer coefficient between the bottom of the model and the environment, T b is the bottom temperature of the model after the heating cycle, T a is the ambient temperature, d a is the thickness of the model, T c is the top temperature of the model after the heating cycle.

[0071] like Figure 4 As shown, Figure 4 The figure shows a temperature distribution cloud of the whole coating and a curve of the temperature change of the coating surface and the back of the substrate over time. The calculation results of the thermal insulation behavior of the self-repairing thermal barrier coating when the thickness of TC is set to 240μm and the thickness of TAZ is 60μm are shown. The thermal insulation analysis of the self-repairing coating is obtained. Figure 4 (a) The overall temperature distribution cloud diagram of the coating and Figure 4(b) shows a curve of the temperature variation of the coating surface and the back side of the substrate over time. According to the calculation formula of effective thermal conductivity, the effective thermal conductivity of the self-healing thermal barrier coating can be calculated to be 2.35 W / (m·K).

[0072] In one embodiment of the present invention, the residual stress and effective thermal conductivity of different optimization test groups under preset working conditions are standardized to obtain target values, including: Constructing a four-variable data set based on the finite element model ('t TC '、'S 22 '、'S I ','k eff '), where 't TC 'Identifier for the optimized test group;'S 22 ' is the axial stress; 'S I ' is the first principal stress; 'k eff ' is the effective thermal conductivity of the coating; Each variable is standardized to obtain the distance between the target value and the ideal point. The standardization method includes: Where D is the distance between the target value and the ideal point; f i is the i-th target value; f i max is the maximum value of the single objective optimization of the i-th objective function; f i min is the minimum value of the single objective optimization of the i-th objective function; n is the number of objective functions.

[0073] In one embodiment of the present invention, the method of obtaining the minimum distance between the ideal solutions corresponding to different optimization test groups based on the target value, wherein the thickness contained in the optimization test group corresponding to the minimum distance is the thickness of the optimized self-repairing thermal barrier coating, includes: obtaining the corresponding ideal solution based on the distance D between the target value and the ideal point of each optimization test group, wherein the ideal solution is the minimum value of all normalized indicators, and represents the point of the optimal state achieved on all performance indicators; Get the distance D between the target value and the ideal point and the minimum Euclidean distance between the ideal solution.

[0074] As mentioned above, the thickness optimization of the multi-layer structure intelligent self-repairing thermal barrier coating requires comprehensive consideration of multiple performance indicators. The ideal point method used in the present invention is a method for solving multi-objective problems, and its ultimate optimization goal is to achieve a local balance between thermal insulation performance and overall residual stress level. In the present invention, the application object of the ideal point method is the thermal insulation and overall residual stress of the self-repairing thermal barrier coating, and the present invention takes into account the high-temperature oxidation resistance and self-repairing performance of the coating, and is implemented by Python programming. In combination with the Inp file of the finite element simulation program, it is convenient to encapsulate all codes into a software interface, which is convenient for the actual thickness design guidance of the thermal spraying self-repairing thermal barrier coating, and produces large-scale practical applications.

[0075] The present invention calculates the distance D between different intelligent self-repairing thermal barrier coating optimization test groups and the ideal performance point, i.e., the ideal solution, and finds an optimization test group closest to the ideal solution, i.e., the one corresponding to the minimum distance D. min The optimized test group is used to obtain the coating thickness with the best comprehensive performance.

[0076] The present invention can effectively find the optimal coating thickness that can meet both low overall residual stress and high thermal insulation performance by comprehensively considering multiple performance indicators. By calculating the distance D between different intelligent self-repairing thermal barrier coating optimization test group schemes and the ideal performance point, an optimization test group closest to the ideal point is found. In an application scenario of the present invention, Python coding is used to calculate the D values ​​of different optimization test groups, so as to obtain the optimal coating thickness and its corresponding minimum ideal point distance.

[0077] In an application scenario of the present invention, the best comprehensive performance of coating thickness is obtained by the following method, including:

[0078] (1) Data preprocessing: Construct a dataset containing four variables ('t TC '、'S 22 '、'S I ','k eff ') Initial data set, which can be obtained from the previous finite element calculation, where 't TC ' represents the thickness of the ceramic layer, the identifier of each optimized test group; 'S 22 ' is the axial stress, which has an important influence on the failure of the coating; 'S I ' is the first principal stress, which is usually the main stress driving force for coating failure; 'k eff ' is the effective thermal conductivity of the coating, which is the key indicator of the thermal insulation performance of the coating; 'S 22 '、'S I ','k eff'The three variables are evaluation indicators. Referring to Table 1, a ceramic layer thickness represents a thickness combination. The performance includes stress and thermal conductivity. The first parameter of the quaternion is the identifier, which can be understood as the serial number of each test group. The following three parameters are the performance of the test group. Import the data into a Pandas DataFrame data structure to facilitate numerical operations and analysis. (2) Data standardization: In order to make different performance indicators have the same weight, the value of each performance indicator is normalized. Each indicator value f i Press Scaling is performed to ensure that in the subsequent analysis, the weight of each indicator on the result is equal. (3) Calculation of D: Calculate the Euclidean distance between each optimized test group and the ideal solution. The ideal solution is the minimum value of all normalized indicators and represents the point where all performance indicators reach the optimal state. (4) Identification and analysis of the optimal solution: Based on the D value of each optimized test group, obtain D min It is believed that the coating thickness corresponding to this D value has the best comprehensive performance.

[0079] It should be pointed out that the present invention not only considers the two separate properties of thermal insulation and stress, but also takes into account the high-temperature oxidation resistance and high-temperature self-repairing performance of the coating. In the self-repairing layer, TiC is introduced as a self-repairing agent. When the coating is in service under high temperature conditions, TiC reacts with O2 and diffuses into the cracks, and its TiO2 particles fill the cracks. In addition, through finite element calculations, we found that greater residual compressive stress is generated around the crack surface. In addition, due to TiO2 (4.26g / cm 3 ) than TiC (4.93g / cm 3 ) has a low density. After the reaction occurs, the volume of the coating will expand by about 53%, squeezing the cracks, thereby further accelerating the healing of the cracks. Al2O3 is added to the self-healing layer. The purpose is that the melting point of Al2O3 (2072°C) is lower than the melting point of TiC (3160°C) and the melting point of YSZ (2700°C), and it acts as a low-melting-point crystalline phase to bond TiC and YSZ. Secondly, Al2O3 is oxygen-impermeable, which reduces the growth rate of TGO at the interface and improves its antioxidant properties. The purpose of adding YSZ is to further reduce the thermal mismatch with the top YSZ layer, and to reflect the role of component optimization through finite element simulation calculations. The present invention further combines the collaborative calculation of finite element calculations and ideal point methods on the basis of component design innovation, and introduces thickness optimization design, which further provides theoretical guidance for the actual preparation of thermal spray self-healing thermal barrier coatings.

[0080] In order to better express the technical solution of the present invention, Figure 5 As shown, in an application scenario of the present invention, the S of different test groups are obtained through the preliminary finite element analysis. 22Stress, first principal stress S I and thermal conductivity k eff data, such as Figure 5 (a) Figure 5 (b) as shown. Figure 5 (a) and (b) reveal two key mechanical indicators S for predicting crack initiation and coating peeling risk. 22 Stress and first principal stress S I and thermal conductivity k eff As can be seen from the figure, S 22 and k eff The TC layer thickness changes monotonically, and there is a nonlinear relationship between the TC layer thickness and the maximum first principal stress. When the TC layer thickness is less than 240 μm, the maximum first principal stress decreases with the increase of TC thickness, but when the TC layer thickness is greater than 240 μm, the maximum first principal stress increases with the increase of TC thickness. Thickness optimization target S 22 Stress, first principal stress S I With k eff There is an inherent contradiction between the thermal insulation performance of TC coating and the thermal insulation performance of S. 22 The stress also increases.

[0081] Through the method of multi-objective optimization analysis of the thickness of the TC coating shown in the present invention, multiple objectives are converted into a single objective form. Each performance indicator is standardized so that it can be compared on the same scale. The core of the optimization is to compare the performance of all test groups (different thickness combinations) with the ideal solution. The ideal solution represents the optimal state of each indicator (lowest stress and best thermal insulation effect). By calculating the Euclidean distance D between each test group and the ideal point, the comprehensive performance of each combination can be measured, and the distance D from each test optimization group to the ideal point is obtained. As shown in Figure 5 (c) shows that, considering minimizing S 22 Stress, first principal stress S I and thermal conductivity k eff Based on the performance, it is found that the TC coating thickness of 0.26 mm, i.e. 260 μm, has the smallest distance from the ideal point in each evaluation index, and its corresponding self-healing layer is test group 2 in Table 1, which provides the best comprehensive performance.

[0082] Here, through the thickness design in this way, not only the residual stress is effectively reduced, but also excellent thermal insulation performance is ensured. It has significant practical application value, helps to optimize the structural integrity and thermal insulation effect of the coating, and prolongs its service life.

[0083] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0084] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0085] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0086] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the thickness of a multi-layered intelligent self-repairing thermal barrier coating, characterized in that: include: Several self-healing thermal barrier coatings with different layer thicknesses were used as optimization test groups; The residual stress analysis and thermal insulation analysis of the sprayed coatings of the self-repairing thermal barrier coatings of different optimized test groups were established and carried out based on the finite element model, and the residual stress and effective thermal conductivity of the different optimized test groups under the preset working conditions were obtained respectively; The residual stress and effective thermal conductivity of different optimization test groups under preset working conditions are standardized to obtain target values; The minimum distance between the ideal solutions corresponding to different optimization test groups is obtained based on the target value. The thickness contained in the optimization test group corresponding to the minimum distance is the thickness of the optimized self-healing thermal barrier coating.

2. The thickness optimization method of multi-layer structure intelligent self-repairing thermal barrier coating according to claim 1 is characterized in that: The layers of different layer thicknesses include at least a bonding layer, a ceramic layer and a self-healing layer; The plurality of self-repairing thermal barrier coatings with different layer thicknesses are respectively used as optimization test groups, including: setting the bonding layer thickness to a predetermined bonding layer thickness; Setting the thickness of the ceramic layer within a first preset thickness range; Setting the thickness of the self-repairing layer within a second preset thickness range; The thickness of the bonding layer, the thickness of the ceramic layer and the thickness of the self-healing layer are combined according to a preset combination method to obtain different optimized test groups.

3. The thickness optimization method of the multi-layer structure intelligent self-repairing thermal barrier coating according to claim 2 is characterized in that: The first preset thickness ranges from 120 μm to 280 μm; and / or the second preset thickness ranges from 20 μm to 180 μm.

4. The thickness optimization method of multi-layer structure intelligent self-repairing thermal barrier coating according to claim 1 is characterized in that: The residual stress analysis of the sprayed coatings of the self-repairing thermal barrier coatings of different optimization test groups was established and carried out based on the finite element model, including: Obtaining residual stresses corresponding to different optimization test groups based on the finite element model, wherein the residual stresses at least include a first principal stress and an axial stress; The residual stress is subjected to stress analysis and screening, and the first principal stress and axial stress obtained by screening are used as the residual stress corresponding to different optimization test groups under preset working conditions.

5. The thickness optimization method of multi-layered intelligent self-repairing thermal barrier coating according to claim 1 is characterized in that: Establish and conduct thermal insulation analysis of the sprayed coatings of the self-repairing thermal barrier coatings of different optimization test groups based on the finite element model, including: setting the corresponding temperature field based on the finite element model; Based on the temperature field, the effective thermal conductivity is obtained.

6. The thickness optimization method of multi-layered intelligent self-repairing thermal barrier coating according to claim 5 is characterized in that: The obtaining of effective thermal conductivity based on the temperature field includes: Get the ambient temperature T a ; Get the model thickness d a ; Get the convective heat transfer coefficient λ between the bottom of the model and the environment of the finite element model b ; Get the top temperature T of the finite element model after a preset heating cycle c and bottom temperature T b ; At ambient temperature T a , model thickness d a , the convective heat transfer coefficient λ between the bottom of the model and the environment of the finite element model b , the top temperature T of the finite element model after the preset heating cycle c and bottom temperature T b As the temperature field, the effective thermal conductivity k is obtained eff , the effective thermal conductivity k eff As the effective thermal conductivity under preset working conditions for different optimized test groups:

7. The thickness optimization method of multi-layered intelligent self-repairing thermal barrier coating according to claim 1 is characterized in that: The residual stress and effective thermal conductivity of the different optimization test groups under the preset working conditions are standardized to obtain target values, including: Constructing a four-variable data set based on the finite element model ('t TC '、'S 22 '、'S I ','k eff '), where 't TC 'Identifier for the optimized test group;'S 22 ' is the axial stress; 'S I ' is the first principal stress; 'k eff ' is the effective thermal conductivity of the coating; Each variable is standardized to obtain the distance D between the target value and the ideal point. The standardization method includes: Where D is the distance between the target value and the ideal point; f i is the i-th target value; f i max is the maximum value of the single objective optimization of the i-th objective function; f i min is the minimum value of the single objective optimization of the i-th objective function; n is the number of objective functions.

8. The thickness optimization method of the multi-layer structure intelligent self-repairing thermal barrier coating according to claim 7 is characterized in that: The method of obtaining the minimum distance between the ideal solutions corresponding to different optimization test groups based on the target value, wherein the thickness contained in the optimization test group corresponding to the minimum distance is the optimized thickness of the self-repairing thermal barrier coating, comprises: Based on the distance D between the target value of each optimization test group and the ideal point, the corresponding ideal solution is obtained, and the ideal solution is the minimum value of all normalized indicators, representing the point of the optimal state achieved on all performance indicators; Get the distance D between the target value and the ideal point and the minimum Euclidean distance between the ideal solution.

9. The thickness optimization method of multi-layered intelligent self-repairing thermal barrier coating according to claim 1, characterized in that: The preset working conditions include preset initial conditions and preset boundary conditions.

10. The thickness optimization method of multi-layered intelligent self-repairing thermal barrier coating according to claim 1, characterized in that: The self-repairing thermal barrier coating includes a three-layer structure, which are, from top to bottom: a YSZ layer, a TAZ (10-20wt% TiC + 5-10wt% A12O3 + 70-85wt.% YSZ) layer and a NiCoCrA1Y layer, wherein the NiCoCrA1Y layer is NiCoCrA1Y modified with oxide rare earth.

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