Optimization method and device for pore structure of thermal barrier coating and storage medium

Abstract

The invention discloses an optimization method and device for a pore structure of a thermal barrier coating and a storage medium, and the method comprises the following steps: carrying out the numerical simulation of the pore structure of the thermal barrier coating, and generating a corresponding pore structure numerical model; performing simulation calculation on the pore structure numerical model, and determining an initial thermal radiation characteristic parameter value of the pore structure; establishing an optimization model of the thermal radiation characteristic parameters; and based on the optimization model, determining the pore structure with the optimal thermal radiation characteristic. According to the method, the pore structure of the coating can be predicted and optimized according to actual working conditions, the influence of the pore structure on the thermal radiation performance of the coating is accurately evaluated, a reliable basis is provided for an optimization result of the optimization model, and finally prediction and optimization of the pore structure of the thermal barrier coating are achieved in combination with the optimization model.

Description

Technical Field

[0001] The present invention relates to the technical field of porous media, and particularly to an optimization method, device and storage medium for the pore structure of a thermal barrier coating. Background Art

[0002] Aeroengines and gas turbines, as the core power plant systems, are one of the most important guarantees for the development of aerospace and marine technologies, as well as major national demands such as energy and power. Due to their excellent properties such as low thermal conductivity, corrosion resistance, and good high-temperature phase stability, the development of thermal barrier coatings has effectively promoted the progress of power equipment such as gas turbines and aeroengines. A thermal barrier coating is a low-porosity anisotropic porous material, usually composed of a ceramic top layer, an oxidation-resistant bonding layer, and a superalloy substrate, where the ceramic top layer provides the main heat insulation. Currently, the research on the heat insulation characteristics of the microstructure of the coating top layer mainly focuses on heat conduction. However, in a high-temperature environment, radiative heat transfer is also one of the important mechanisms of heat transfer in thermal barrier coatings, and relatively few studies have been conducted on the radiative characteristics of the microstructure of thermal barrier coatings. There is an urgent need to strengthen relevant research to comprehensively understand the heat transfer characteristics of thermal barrier coatings. Due to the high cost of experimental research on radiative characteristics, in recent years, with the rapid development of computer technology, numerical simulation methods have also become one of the important means to study the radiative performance of thermal barrier coatings. Some scholars have used methods such as ray tracing, finite element simulation software simulation, and discrete dipole algorithm to calculate and analyze the radiative characteristics of the pore structure of the coating under specific working conditions. The parameter settings of the pore structure are all based on the assumed conditions of experimental observations, and it is impossible to reversely predict and optimize the design of the pore structure of the coating according to the actual working conditions, so as to provide a certain reference value for the preparation of the coating.

[0003] The existing patent CN114218826A discloses a method for modeling a random multiphase two-dimensional physical model of a coating. The method includes the following steps: constructing a two-dimensional multi-scale random medium of the coating, and on the two-dimensional multi-scale random medium of the coating, using the threshold truncation method to intercept the lubrication phase and pore position coordinates from the total nodes, and using the remaining nodes as the matrix position coordinates to obtain a random multiphase two-dimensional physical model of the coating. In addition, a method for evaluating the coating performance is provided, including: using the above-mentioned method for modeling a random multiphase two-dimensional physical model of the coating to construct a random multiphase two-dimensional physical model of the coating; based on the random multiphase two-dimensional physical model of the coating, using the finite element method for numerical simulation to obtain the coating performance parameters and evaluate the coating performance.

[0004] The existing patent CN118395779A discloses a method for modeling a finite element model of a thermal barrier coating based on a real structure, including the following steps: S1: preparing a sample of the thermal barrier coating system; S2: processing the sample of the thermal barrier coating system to obtain a microscopic structure vector diagram of the thermal barrier coating system sample marked with defects, pores, and impurities; S3: importing and setting the microscopic structure vector diagram of the thermal barrier coating system to obtain a microscopic structure finite element model of the thermal barrier coating system sample marked with defects, pores, and impurities.

[0005] In summary, neither of the above two existing patents has solved the problem that most simulations in the prior art are based on hypothetical conditions of experimental observations, and it is impossible to predict and optimize the pore structure of the coating according to the actual working conditions, making it difficult to meet the precise requirements of coating preparation. Summary of the Invention

[0006] Based on the above technical problems, the present invention proposes an optimization method and device for the pore structure of a thermal barrier coating, which solves the problem that most simulations in the prior art are based on hypothetical conditions of experimental observations, and it is impossible to predict and optimize the pore structure of the coating according to the actual working conditions, making it difficult to meet the precise requirements of coating preparation.

[0007] To achieve the above object, the present invention proposes an optimization method for the pore structure of a thermal barrier coating.

[0008] An optimization method for the pore structure of a thermal barrier coating includes:

[0009] Performing numerical simulation on the pore structure of the thermal barrier coating and generating a corresponding numerical model of the pore structure;

[0010] Performing simulation calculation on the numerical model of the pore structure to determine the initial thermal radiation characteristic parameter value of the pore structure;

[0011] Establishing an optimization model for the thermal radiation characteristic parameters;

[0012] Based on the optimization model, determining the pore structure with the optimal thermal radiation characteristics.

[0013] Further, performing numerical simulation on the pore structure of the thermal barrier coating and generating a corresponding numerical model of the pore structure includes:

[0014] Simulating the pore structure of the thermal barrier coating by using the four-parameter random growth method and generating a corresponding numerical model of the pore structure.

[0015] Further, simulating the pore structure of the thermal barrier coating by using the four-parameter random growth method and generating a corresponding numerical model of the pore structure includes:

[0016] Setting the simulation area of the pore structure;

[0017] Initialize the grid points and pore structure parameters within the simulation region. The pore structure parameters include the growth probability of the nucleation centers of the growth phase, the growth probabilities of the growth nuclei in each direction, and the preset volume fraction of the growth phase.

[0018] Randomly arrange the nucleation centers of the first growth phase according to the growth probability of the nucleation centers of the growth phase.

[0019] The nucleation centers of the first growth phase grow in the i-th direction according to the growth probabilities of the growth nuclei in each direction until the volume fraction of the growth phase reaches the preset volume fraction.

[0020] Furthermore, perform simulation calculations on the pore structure numerical model to determine the thermal radiation characteristic parameters of the pore structure, including:

[0021] Based on the pore structure numerical model, use the simulation method to simulate the thermal radiation process of the pore structure and determine the thermal radiation characteristic parameters of the pore structure. The thermal radiation characteristic parameters include one or more of reflectivity, transmittance, and absorptance.

[0022] Furthermore, based on the pore structure numerical model, use the simulation method to simulate the thermal radiation process of the pore structure and determine the thermal radiation characteristic parameters of the pore structure, including:

[0023] Discretize the Maxwell curl equation using the finite-difference time-domain method to determine the expressions for the time-varying electric field component and magnetic field component.

[0024] Use the expressions for the time-varying electric field component and magnetic field component to determine the electric field intensity vector and magnetic field intensity vector.

[0025] Calculate the Poynting vector according to the electric field intensity vector and magnetic field intensity vector.

[0026] Determine the thermal radiation characteristic parameters of the pore structure based on the Poynting vector.

[0027] Furthermore, calculate the Poynting vector according to the electric field intensity vector and magnetic field intensity vector, including:

[0028] Calculate the Poynting vector according to the electric field intensity vector and magnetic field intensity vector through Formula 1. Formula 1: where S represents the Poynting vector, T = 2π / ω is the period of the electromagnetic wave, E is the electric field intensity vector, and H is the magnetic field intensity vector.

[0029] Furthermore, determine the thermal radiation characteristic parameters of the pore structure based on the Poynting vector, including:

[0030] Determine the radiation intensity of the pore structure through Formula 2 based on the Poynting vector. Formula 2: S = IdΩ, where S represents the Poynting vector and I is the radiation intensity.

[0031] Determine the reflectivity and / or transmittance and / or absorptance of the pore structure according to the radiation intensity.

[0032] Furthermore, an optimization model of the thermal radiation characteristic parameters is established, including:

[0033] Use the LM optimization algorithm to establish an optimization model, and establish an objective function of the optimization model based on the thermal radiation characteristic parameters. The objective function is where m represents the number of sampling points of the thermal radiation characteristic parameters in the translucent band, r i = f(x i , β) - y i , f(x i , β) represents the simulated value of a certain thermal radiation characteristic parameter of the pore structure at a wavelength of x i μm, β is the parameter to be optimized, and y i represents the theoretical value of a certain thermal radiation characteristic parameter of the pore structure at a wavelength of x i μm.

[0034] Furthermore, based on the optimization model, determine the pore structure with the optimal thermal radiation characteristics, including:

[0035] Use the optimization model to call the four-parameter random growth method and the finite-difference time-domain method to iteratively optimize the objective function of the optimization model until the objective function reaches a convergence state.

[0036] Use the optimization model to call the four-parameter random growth method and the finite-difference time-domain method to iteratively optimize the objective function of the optimization model until the objective function reaches a convergence state, including:

[0037] Use the optimization model to call the four-parameter random growth method and the finite-difference time-domain method to determine the change amount array of the pore structure parameters and the change amount of the objective function value;

[0038] Judge whether the change amount array of the pore structure parameters converges;

[0039] If the change amount array of the pore structure parameters converges, then judge whether the change amount of the objective function value converges;

[0040] If the change amount of the objective function value converges, stop the iteration, and use the pore structure corresponding to the current pore structure parameters as the pore structure with the optimal thermal radiation characteristics;

[0041] If the change amount of the objective function value diverges, update the pore structure parameters according to the change amount array of the pore structure parameters, and iteratively optimize the objective function again.

[0042] Further, before using the optimized model to call the four-parameter stochastic growth method and the finite-difference time-domain method, it further includes:

[0043] Initializing the parameters of the optimized model, where the parameters of the optimized model include the number of convergence times, the convergence threshold, the difference multiple, and the damping coefficient vector.

[0044] Further, determining the change amount of the objective function value includes:

[0045] Differencing the pore structure parameters according to the difference multiple;

[0046] Calling the four-parameter stochastic growth method and the finite-difference time-domain method, and determining the corresponding thermal radiation characteristic parameters based on the differenced pore structure parameters;

[0047] Determining the change amount of the objective function value according to the thermal radiation characteristic parameters before differencing and the values of the thermal radiation characteristic parameters after differencing.

[0048] Further, determining the change amount array of the pore structure parameters includes:

[0049] Determining the Jacobian matrix based on the differenced pore structure parameters;

[0050] According to the Jacobian matrix, solving the change amount array of the pore structure parameters through Equation 4, Equation 4, (J T J + μI)Δβ k = -J T r, where J T is the transpose of the Jacobian matrix of r(β), J is the Jacobian matrix of r(β), μI is the damping matrix, μ represents the damping coefficient vector corresponding to each parameter, I is a positive definite diagonal identity matrix, and Δβ k is the change amount array of the pore structure parameters.

[0051] Further, determining whether the change amount array of the pore structure parameters converges includes:

[0052] Comparing the change amount array of the pore structure parameters Δβ k corresponding to the k-th convergence with the change amount array of the pore structure parameters Δβ k-1 corresponding to the (k - 1)-th convergence;

[0053] If there is an element in the change amount array Δβ k whose absolute value is greater than the absolute value of the corresponding element in Δβ k-1 , or there is an element in the change amount array Δβ k whose sign is different from the sign of the corresponding element in Δβ k-1 , then the change amount array of the pore structure parameters Δβ kArray Δβ of variation amounts with respect to pore structure parameters k-1 Diverge;

[0054] Conversely, if each element of the array of variation amounts Δβ k has an absolute value less than or equal to the absolute value of the corresponding element in Δβ k-1 and the sign of each element in the array of variation amounts Δβ k is the same as the sign of the corresponding element in Δβ k-1 , then the array of variation amounts Δβ of the pore structure parameters k with respect to the array of variation amounts Δβ of the pore structure parameters k-1 Converges.

[0055] Furthermore, if the array of variation amounts of the pore structure parameters shows a divergent trend, then set the damping coefficient vector μ k = 5μ k , and recalculate the array of variation amounts of the pore structure parameters according to Equation 4.

[0056] Furthermore, determining whether the array of variation amounts of the objective function value converges includes:

[0057] Determining whether the absolute value of the difference between the objective function value S k corresponding to the k-th convergence and the objective function value S k-1 corresponding to the (k - 1)-th convergence is less than the convergence threshold;

[0058] If |S k - S k-1 | is less than the convergence threshold, then the array of variation amounts of the objective function value converges.

[0059] Furthermore, it also includes:

[0060] If the array of variation amounts of the pore structure parameters converges, then determine whether the absolute value of each element in the array of variation amounts Δβ k is less than the corresponding convergence threshold;

[0061] If the absolute value of each element in the array of variation amounts Δβ k is less than the corresponding convergence threshold, then stop the iteration and take the pore structure corresponding to the current pore structure parameters as the pore structure with the optimal thermal radiation characteristics.

[0062] To achieve the above object, the present invention proposes an optimization device for the pore structure of a thermal barrier coating.

[0063] An optimization device for the pore structure of a thermal barrier coating, the device includes:

[0064] A numerical simulation module, configured to perform numerical simulation on the pore structure of the thermal barrier coating and generate a corresponding numerical model of the pore structure;

[0065] A calculation module for performing simulation calculations on a numerical model of a pore structure to determine the thermal radiation characteristic parameters of the pore structure;

[0066] A building module for building an optimization model of the thermal radiation characteristic parameters;

[0067] A determination module for determining the pore structure with the optimal thermal radiation characteristics based on the optimization model.

[0068] A computer-readable storage medium, which includes a stored computer program, wherein the computer program can execute the above method when being run by an electronic device.

[0069] A computer program product, including a computer program which implements the steps of the above method when being executed by a processor.

[0070] An electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the above method through the computer program.

[0071] Based on the above technical solutions, the present invention has at least the following beneficial effects:

[0072] 1. The present invention generates a numerical model of a pore structure based on numerical simulation and performs simulation calculations on the numerical model of the pore structure. On the basis of accurately simulating the pore structure of the thermal barrier coating layer with high precision, it accurately evaluates the influence of the pore structure on the thermal radiation performance of the coating, provides a reliable basis for the optimization result of the optimization model, and finally determines the pore structure with the optimal thermal radiation characteristics in combination with the optimization model, realizing the prediction and optimization of the pore structure of the thermal barrier coating.

[0073] 2. The present invention uses the LM optimization algorithm to build an optimization model, repeatedly calls the four-parameter random growth method and the finite-difference time-domain method in the optimization model to determine the change amount array of the pore structure parameters and the change amount of the objective function value, and realizes the iterative optimization of the objective function and the update of the pore structure parameters by successively judging whether the change amount array of the pore structure parameters and the change amount of the objective function value converge, and finally determines the pore structure with the optimal thermal radiation characteristics.

[0074] 3. The present invention proposes to determine the Jacobian matrix based on the pore structure parameters after differentiation, and solve the change amount array of the pore structure parameters according to the Jacobian matrix. This method can efficiently capture the influence of the change of the pore structure parameters on the thermal radiation characteristics, thereby significantly improving the optimization accuracy and efficiency of the pore structure of the thermal barrier coating.

[0075] 4. The present invention generates a numerical model of the pore structure of a thermal barrier coating through a four-parameter random growth method, which can flexibly control the shape, size, and distribution of pores, and thus can simulate a pore structure similar to that of an actual thermal barrier coating, providing accurate input for subsequent radiation characteristic analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The accompanying drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0077] Figure 1 is a flowchart of an optimization method for the pore structure of a thermal barrier coating according to an embodiment of the present invention;

[0078] Figure 2 are 26 directions in three-dimensional space when simulating the pore structure by using the four-parameter random growth method according to an embodiment of the present invention;

[0079] Figure 3 is a parameter control interface for generating a coating pore structure model by using the four-parameter random growth method according to an embodiment of the present invention;

[0080] Figure 4 is a schematic cross-sectional view of the x-z axis structure of a lamellar pore structure model of a thermal barrier coating according to an embodiment of the present invention;

[0081] Figure 5 is a schematic diagram of a model and boundary conditions for calculating radiation characteristics by using the FDTD Solutions software according to an embodiment of the present invention;

[0082] Figure 6 is the spectral reflectivity of coatings with a lamellar pore structure and a columnar pore structure calculated by using the FDTD method according to an embodiment of the present invention;

[0083] Figure 7 is the transmittance of coatings with a lamellar pore structure and a columnar pore structure calculated by using the FDTD method according to an embodiment of the present invention;

[0084] Figure 8 is a flowchart for determining the pore structure with the optimal thermal radiation characteristics by using the optimization model according to an embodiment of the present invention;

[0085] Figure 9 is a schematic diagram of an optimization device for the pore structure of a thermal barrier coating according to an embodiment of the present invention;

[0086] Figure 10 is a block diagram of the computer system structure of an electronic device for implementing the embodiments of the present invention;

[0087] Figure 11 Schematic diagram of an electronic device for optimizing the pore structure of a thermal barrier coating according to an embodiment of the present invention. Detailed implementation manners

[0088] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0089] The following further describes the present invention in detail with specific embodiments, and these embodiments should not be construed as limiting the scope claimed by the present invention.

[0090] Embodiment

[0091] To solve the problem that in the prior art, most simulations are based on hypothetical conditions of experimental observations, it is impossible to predict and optimize the coating pore structure according to actual working conditions, and it is difficult to meet the precise requirements of coating preparation, the present invention proposes an optimization method, device and storage medium for the pore structure of a thermal barrier coating.

[0092] To achieve the above object, the present invention proposes an optimization method for the pore structure of a thermal barrier coating.

[0093] As Figure 1 shown in the flowchart of an optimization method for the pore structure of a thermal barrier coating according to an embodiment of the present invention, the method includes the following steps:

[0094] S1, perform numerical simulation on the pore structure of the thermal barrier coating and generate a corresponding numerical model of the pore structure.

[0095] In this embodiment, the four-parameter random growth method is used to simulate the pore structure of the thermal barrier coating and generate a corresponding numerical model of the pore structure. The four-parameter random growth method (QSGS), this algorithm takes a certain phase in the multiphase material system as the growth phase and randomly constructs the nucleation centers of the growth phase, and generates pores with different shapes, sizes and distributions by controlling the growth probability of the nucleation centers and the growth probabilities of the growth nuclei in each direction, so as to simulate the pore microstructure of the real thermal barrier coating. This method has good repeatability and can be used for multiple simulations and analyses, which is beneficial to statistical and comparative studies. For the two-phase (gas, solid) structure of a typical thermal barrier coating, using the four-parameter random growth method to simulate the pore structure of the thermal barrier coating and generate a corresponding numerical model of the pore structure includes the following sub-steps:

[0096] S101, set the simulation area of the pore structure.

[0097] For example, set the simulation area of the pore structure to two-dimensional or three-dimensional.

[0098] S102, initialize the grid points and pore structure parameters in the simulation area.

[0099] In this embodiment, all the grid points in the simulation area are set to 0, and the pore structure parameters include the growth probability of the growth phase nucleation center, the growth probability of the growth nuclei in each direction, and the volume fraction of the growth phase. When initializing the growth probability of the growth phase nucleation center, its value should be less than the preset volume fraction.

[0100] S103, randomly arrange the nucleation centers of the first growth phase according to the growth probability of the growth phase nucleation center.

[0101] After initializing the growth probability of the growth phase nucleation center and the growth probability of the growth nuclei in each direction, randomly arrange the nucleation centers of the first growth phase according to the initialized growth probability of the growth phase nucleation center. After the arrangement, the value of the growth nucleation center is converted from 0 to 1.

[0102] S104, the nucleation centers of the first growth phase grow in the i-th direction according to the growth probability of the growth nuclei in each direction until the volume fraction of the growth phase reaches the preset volume fraction.

[0103] Traverse the randomly arranged nucleation centers of the first growth phase in step S103, and randomly grow each pore node in 26 directions in three-dimensional space according to the growth probability of the growth nuclei in each direction. As Figure 2 shown, the nucleation centers of the growth phase grow in the i-th direction according to the set growth probability and realize binary conversion. The grid points converted to 1 will become new growth nucleation centers and continue to grow with the set growth probability in each direction.

[0104] Stop growing when the volume fraction of the growth phase reaches the preset volume fraction, and generate the corresponding numerical model of the pore structure. Taking the QSGS model parameter setting in Figure 3 as an example, within the calculation area size of 1mm×0.25mm×0.25mm, set the number of grids to 400×100×100, the growth probability of the growth phase nucleation center to 5%, and the growth probability P of the growth nuclei in each direction i(i＝1～6) is P i(i＝1,3) =500, P i(i＝2,4～6) =1, the porosity is 15%, and a numerical model of the lamellar pore structure of the thermal barrier coating is generated. As Figure 4 is the cross-sectional view of the pore structure in the x-z axis.

[0105] S2, perform simulation calculations on the numerical model of the pore structure to determine the thermal radiation characteristic parameters of the pore structure.

[0106] In this embodiment, the numerical model of the pore structure determined in step S1 above is imported into the simulation software, and the simulation software is used to simulate the thermal radiation process of the pore structure to determine the thermal radiation characteristic parameters of the pore structure. The thermal radiation characteristic parameters include one or more of reflectivity, transmittance, and absorptance.

[0107] Specifically, the simulation software used in this embodiment is FDTD Solutions software. After importing the pore structure numerical model into the simulation software, the thermal radiation process of the pore structure is simulated by the FDTD Solutions software. It should be understood that different simulation software can be selected for simulation calculations according to different usage scenarios.

[0108] Furthermore, the FDTD simulation region should be larger than the coating physical model. Its sides are set as periodic boundaries, and the top and bottom have Perfect Matched Layers (PML), as shown in the schematic diagram of the model in Figure 5 The working principle of the FDTD Solutions software is as follows: A plane wave light source is used to simulate the radiation beam on the outer surface side, which is vertically incident from above the region; a reflectivity power monitor is set between the plane wave light source and the upper boundary of the PML to receive all the reflected beams; a transmittance power monitor is set between the lower surface of the TBC coating physical model and the lower boundary of the PML to receive all the radiation beams passing through the coating, so as to simulate and calculate the multi-band radiation characteristics of different thermal barrier coating microstructures, such as reflectivity and transmittance.

[0109] Furthermore, the thermal radiation process of the pore structure is simulated by the FDTD Solutions software to determine the thermal radiation characteristic parameters of the pore structure, specifically including steps S201 to S204.

[0110] S201, Discretize the Maxwell curl equation using the finite-difference time-domain method to determine the expressions of the electric field component and the magnetic field component changing with time.

[0111] The finite-difference time-domain method has become a mature numerical method for solving the Maxwell equation. Its main idea is to discretize the Maxwell curl equation, replace the partial derivatives with central differences, and solve to describe the thermal radiation process of the coating. Among them, the Maxwell curl equation can be expressed in the following form:

[0112]

[0113] After partial differential processing of the Maxwell equation in time and space, the central difference format is used to determine the expressions of the electric field component E and the magnetic field component H changing with time.

[0114] S202, Use the expressions of the electric field component and the magnetic field component changing with time to determine the electric field intensity vector and the magnetic field intensity vector.

[0115] S203, Calculate the Poynting vector according to the electric field intensity vector and the magnetic field intensity vector.

[0116] The transfer of energy in the electromagnetic field is described by the Poynting vector S. Its magnitude represents the energy flux density of the electromagnetic wave, that is, the energy passing through a unit area per unit time, and the unit is W / m 2 . In the process of thermal radiation calculation, the direction of the Poynting vector represents the transmission direction of the electromagnetic wave energy. It is perpendicular to the directions of the electric field and the magnetic field and conforms to the right-hand rule.

[0117] In this embodiment, according to the electric field strength vector and the magnetic field strength vector, the Poynting vector is calculated through Formula 1. Formula 1, where S represents the Poynting vector, T = 2π / ω is the period of the electromagnetic wave, E is the electric field strength vector, and H is the magnetic field strength vector.

[0118] S204. Determine the thermal radiation characteristic parameters of the pore structure based on the Poynting vector.

[0119] Based on the Poynting vector, determine the radiation intensity of the pore structure through Formula 2. Formula 2, S = IdΩ, where S represents the Poynting vector and I is the radiation intensity. Determine the reflectivity and / or transmittance and / or absorptance of the pore structure according to the radiation intensity. Specifically, the radiation intensities obtained by the reflectivity and transmittance power receivers are I reflected 、I transmitted , Since the absorptance α + reflectivity ρ + transmittance τ = 1, the corresponding absorptance can be obtained according to the above parameters and formulas.

[0120] In this embodiment, the reflectivity, transmittance and absorptance of the pore structure are determined by the above method. As Figure 6 shows the spectral reflectivity of the layered pore structure and the columnar pore structure coatings calculated by using the FDTD model; as Figure 7 shows the transmittance of the layered pore structure and the columnar pore structure coatings calculated by using the FDTD model.

[0121] S3. Establish an optimization model for the thermal radiation characteristic parameters.

[0122] In this embodiment, the LM optimization algorithm is used to establish the optimization model, and the objective function of the optimization model is established based on the thermal radiation characteristic parameters. The LM optimization algorithm is an optimization algorithm that combines the Gauss-Newton method and the gradient descent method. Compared with the Gauss-Newton method and the gradient descent method, it is easier to converge. The LM optimization algorithm has certain requirements for the form of the objective function S, which is written in the form of the sum of the squares of the differences between the simulated values and the theoretical values at each point. Combining with the technical problems to be solved by the present invention, the objective function of the optimization model is, where m represents the number of sampling points of the thermal radiation characteristic parameters in the translucent band, ri = f(x i , β) - y i , f(x i , β) represents the simulated value of a certain thermal radiation characteristic parameter of the pore structure at a wavelength of x i μm, β is the parameter to be optimized, and y i represents the theoretical value of a certain thermal radiation characteristic parameter of the pore structure at a wavelength of x i μm. Further, in this embodiment, the theoretical value y i is set to 0 for optimization, so that the reflectivity is maximized or the transmittance is minimized, that is, the pore structure of the coating when the thermal radiation protection performance is optimal. The gradient of the objective function F with respect to the parameter β can be expressed as where r = [r1, r2... r m T , and m represents the number of sampling points of the radiation characteristic parameters (such as reflectivity, transmittance, etc.) in the translucent band. The can be further expressed as where J T is the transpose of the Jacobi matrix of r(β), and the Jacobi matrix of r(β) is

[0123] Further, the second-order partial derivative matrix of the objective function, that is, the Hessian matrix, its matrix elements can be expressed as

[0124]

[0125] Define a new matrix as O, and the elements of matrix O are expressed as The iterative formula of Newton's method is Combining formula five and formula six, the Hessian matrix H can be expressed as: H = 2(J T J + O), the formula five Δβ k = -(J T J + O) -1 J T r, the formula six is, Δβ k = -(J T J) -1 J T r. β k represents the change in the parameter for the next iteration. When the matrix O is ignored, the iterative formula of the Gauss-Newton method is formed, such as formula seven Δβ k = -(J T J) - 1 J T ​r, thus the difficulty of the optimization algorithm is reduced from finding the Hessian matrix of the function to finding the Jacobi matrix of the function. To improve convergence, a damping matrix μI is introduced, where μ represents the damping coefficient vector corresponding to each parameter, and I is a positive definite diagonal identity matrix, forming the iterative formula of the LM optimization algorithm, as shown in Equation (4) (J T J + μI)Δβ k = -J T r.

[0126] S4. Based on the optimization model, determine the pore structure with the optimal thermal radiation characteristics.

[0127] In this embodiment, the four-parameter random growth method and the finite-difference time-domain method are called using the optimization model to iteratively optimize the objective function of the optimization model until the objective function reaches a convergent state.

[0128] As Figure 8 shown in the flowchart of determining the pore structure with the optimal thermal radiation characteristics using the optimization model, this process mainly includes the following sub-steps:

[0129] S401. Using the optimization model, call the four-parameter random growth method and the finite-difference time-domain method to determine the array of changes in pore structure parameters and the change in the objective function value.

[0130] In this embodiment, before using the optimization model to call the four-parameter random growth method and the finite-difference time-domain method, it also includes: S406. Initialize the parameters of the optimization model. The parameters of the optimization model include the number of convergence times, the convergence threshold, the difference multiple, and the damping coefficient vector. In this embodiment, the number of convergence times is defined as k, initialized to 0, the initial pore structure parameter array is The convergence threshold is defined as σ, the difference multiple is defined as ε, and the initial damping coefficient vector is defined as μ0. Based on the initial pore structure parameter array, use the optimization model to call the four-parameter random growth method to generate the corresponding pore structure numerical model, and call the finite-difference time-domain method to determine the initial thermal radiation characteristic parameter values of the coating.

[0131] Among them, determining the change in the objective function value includes the following sub-steps:

[0132] S4011. Differentiate the pore structure parameters according to the difference multiple.

[0133] Taking the first iteration process as an example, the initial pore structure parameters can be differentiated in sequence according to the initialized difference multiple.

[0134] S4012. Call the four-parameter random growth method and the finite-difference time-domain method to determine the corresponding thermal radiation characteristic parameter values based on the differentiated pore structure parameters.

[0135] S4013. Determine the change amount of the objective function value according to the thermal radiation characteristic parameter values before the difference and the thermal radiation characteristic parameter values after the difference.

[0136] Specifically, according to the thermal radiation characteristic parameter values before the difference and the thermal radiation characteristic parameter values after the difference, through the objective function formula respectively determine the corresponding objective function values, and then determine the change amount of the objective function value.

[0137] The process of determining the change amount array of the pore structure parameters is as follows: First, determine the Jacobian matrix based on the pore structure parameters after the difference; then, according to the Jacobian matrix, solve the change amount array of the pore structure parameters through Formula Four. Formula Four: (J T J + μI)Δβ k =-J T r, where J T is the transpose of the Jacobian matrix of r(β), J is the Jacobian matrix of r(β), μI is the damping matrix, μ represents the damping coefficient vector corresponding to each parameter, I is a positive definite diagonal identity matrix, and Δβ k is the change amount array of the pore structure parameters.

[0138] Specifically, the Jacobian matrix is expressed as In this embodiment, each element in the Jacobi matrix is approximately obtained by the forward difference method. For example, the element in the matrix Substitute the obtained Jacobian matrix and other parameters into Formula Four to solve the change amount array Δβ of the pore structure parameters k .

[0139] S402. Determine whether the change amount array of the pore structure parameters converges.

[0140] Furthermore, the process of determining whether the change amount array of the pore structure parameters converges is as follows:

[0141] S4021. Compare the change amount array Δβ k of the pore structure parameters corresponding to the k-th convergence with the change amount array Δβ k-1 of the pore structure parameters corresponding to the (k - 1)-th convergence.

[0142] S4022. If there is an element in the change amount array Δβ k whose absolute value is greater than the absolute value of the corresponding element in Δβ k-1 , or there is an element in the change amount array Δβ k whose sign is different from the sign of the corresponding element in Δβ k-1 , then the change amount array Δβ kArray of change amounts Δβ with respect to pore structure parameters k-1 Diverge.

[0143] S4023, conversely, if the array of change amounts Δβ k the absolute value of each element in is less than or equal to the absolute value of the corresponding element in Δβ k-1 and the sign of each element in the array of change amounts Δβ k is the same as the sign of the corresponding element in Δβ k-1 then the array of change amounts Δβ of the pore structure parameters k Array of change amounts Δβ with respect to pore structure parameters k-1 Converge.

[0144] Further, if the array of change amounts of the pore structure parameters shows a divergent trend, then set the damping coefficient vector μ k = 5μ k , re - determine the Jacobian matrix and recalculate the array of change amounts of the pore structure parameters according to Formula Four.

[0145] S403, if the array of change amounts of the pore structure parameters converges, then determine whether the change amount of the objective function value converges.

[0146] Further, the process of determining whether the array of change amounts of the objective function value converges is as follows:

[0147] S4031, determine whether the absolute value of the difference between the objective function value S k corresponding to the k - th convergence and the objective function value S k-1 corresponding to the (k - 1)-th convergence is less than the convergence threshold.

[0148] S4032, if |S k - S k-1 | is less than the convergence threshold, then the array of change amounts of the objective function value converges.

[0149] S404, if the change amount of the objective function value converges, stop the iteration, and take the pore structure corresponding to the current pore structure parameters as the pore structure with the optimal thermal radiation characteristics.

[0150] S405, if the change amount of the objective function value diverges, update the pore structure parameters according to the array of change amounts of the pore structure parameters, and iterate and optimize the objective function again.

[0151] In another embodiment of the present invention, a corresponding convergence threshold is set for each element in the array of change amounts Δβ k , and it is determined whether the array of change amounts of the objective function value converges through steps S406 and S407. Specifically,

[0152] S406, if the change amount array of the pore structure parameters converges, then determine whether the absolute value of each element in the change amount array Δβ k is less than the corresponding convergence threshold value.

[0153] S407, if the absolute value of each element in the change amount array Δβ k is less than the corresponding convergence threshold value, then stop the iteration, and use the pore structure corresponding to the current pore structure parameters as the pore structure with the optimal thermal radiation characteristics.

[0154] It can be understood that when it is determined that the change amount array of the pore structure parameters converges, it is possible to determine whether further iteration is required by judging the convergence of the objective function or by judging the convergence of each parameter in the change amount array of the pore structure parameters.

[0155] If further iteration is required, then let X k+1 = X k + Δβ k , k = k + 1, update the pore structure parameters and the number of iterations respectively, and perform iteration based on the updated parameters.

[0156] To achieve the above object, the present invention provides an optimization device for the pore structure of a thermal barrier coating.

[0157] As Figure 9 shown in the schematic diagram of an optimization device for the pore structure of a thermal barrier coating according to an embodiment of the present invention, the optimization device implements the above optimization method, and the optimization device includes: a numerical simulation module 101, a calculation module 102, a model establishment module 103, and a determination module 104. The functions of each module will be introduced in detail below.

[0158] The numerical simulation module 101 is used to perform numerical simulation on the pore structure of the thermal barrier coating and generate a corresponding pore structure numerical model.

[0159] The calculation module 102 is used to perform simulation calculations on the pore structure numerical model to determine the thermal radiation characteristic parameters of the pore structure.

[0160] The model establishment module 103 is used to establish an optimization model for the thermal radiation characteristic parameters.

[0161] The determination module 104 is used to determine the pore structure with the optimal thermal radiation characteristics based on the optimization model.

[0162] Furthermore, performing numerical simulation on the pore structure of the thermal barrier coating and generating a corresponding pore structure numerical model includes:

[0163] Simulating the pore structure of the thermal barrier coating by using a four-parameter random growth method and generating a corresponding pore structure numerical model.

[0164] As an alternative, the above device is also used to simulate the pore structure of the thermal barrier coating by using the four-parameter random growth method and generate a corresponding numerical model of the pore structure, including:

[0165] Set the simulation area of the pore structure;

[0166] Initialize the lattice points and pore structure parameters in the simulation area, where the pore structure parameters include the growth probability of the nucleation center of the growth phase, the growth probability of the growth nuclei in each direction, and the preset volume fraction of the growth phase;

[0167] Randomly arrange the nucleation centers of the first growth phase according to the growth probability of the nucleation center of the growth phase;

[0168] The nucleation centers of the first growth phase grow in the i-th direction according to the growth probability of the growth nuclei in each direction until the volume fraction of the growth phase reaches the preset volume fraction.

[0169] As an alternative, the above device is also used to perform simulation calculations on the numerical model of the pore structure to determine the thermal radiation characteristic parameters of the pore structure, including:

[0170] Based on the numerical model of the pore structure, use the simulation method to simulate the thermal radiation process of the pore structure and determine the thermal radiation characteristic parameters of the pore structure. The thermal radiation characteristic parameters include one or more of reflectivity, transmittance, and absorptance.

[0171] As an alternative, the above device is also used to, based on the numerical model of the pore structure, use the simulation method to simulate the thermal radiation process of the pore structure and determine the thermal radiation characteristic parameters of the pore structure, including:

[0172] Discretize the Maxwell curl equation by using the finite-difference time-domain method to determine the expressions of the electric field component and the magnetic field component changing with time;

[0173] Use the expressions of the electric field component and the magnetic field component changing with time to determine the electric field intensity vector and the magnetic field intensity vector;

[0174] Calculate the Poynting vector according to the electric field intensity vector and the magnetic field intensity vector;

[0175] Determine the thermal radiation characteristic parameters of the pore structure based on the Poynting vector.

[0176] As an alternative, the above device is also used to calculate the Poynting vector according to the electric field intensity vector and the magnetic field intensity vector, including:

[0177] Calculate the Poynting vector according to the electric field intensity vector and the magnetic field intensity vector through Formula 1. Formula 1, Among them, S represents the Poynting vector, T = 2π / ω is the period of the electromagnetic wave, E is the electric field intensity vector, and H is the magnetic field intensity vector.

[0178] As an alternative solution, the above device is also used to determine the thermal radiation characteristic parameters of the pore structure according to the Poynting vector, including:

[0179] According to the Poynting vector, the radiation intensity of the pore structure is determined by Equation 2, Equation 2: S = IdΩ, where S represents the Poynting vector and I is the radiation intensity;

[0180] The reflectivity and / or transmittance and / or absorptance of the pore structure are determined according to the radiation intensity.

[0181] As an alternative solution, the above device is also used to establish an optimization model for the thermal radiation characteristic parameters, including:

[0182] An optimization model is established using the LM optimization algorithm, and an objective function of the optimization model is established based on the thermal radiation characteristic parameters. The objective function is where m represents the number of sampling points of the thermal radiation characteristic parameters in the translucent band, r i = f(x i ,β) - y i , f(x i ,β) represents the simulated value of a certain thermal radiation characteristic parameter of the pore structure at a wavelength of x i μm, β is the parameter to be optimized, and y i represents the theoretical value of a certain thermal radiation characteristic parameter of the pore structure at a wavelength of x i μm.

[0183] As an alternative solution, the above device is also used to determine the pore structure with the optimal thermal radiation characteristics based on the optimization model, including:

[0184] The four-parameter random growth method and the finite-difference time-domain method are called using the optimization model to iteratively optimize the objective function of the optimization model until the objective function reaches a convergence state.

[0185] Calling the four-parameter random growth method and the finite-difference time-domain method using the optimization model to iteratively optimize the objective function of the optimization model until the objective function reaches a convergence state includes:

[0186] Calling the four-parameter random growth method and the finite-difference time-domain method using the optimization model to determine the change amount array of the pore structure parameters and the change amount of the objective function value;

[0187] Determine whether the change amount array of the pore structure parameters converges;

[0188] If the array of variation amounts of pore structure parameters converges, determine whether the variation amount of the objective function value converges;

[0189] If the variation amount of the objective function value converges, stop the iteration and take the pore structure corresponding to the current pore structure parameters as the pore structure with the optimal thermal radiation characteristics;

[0190] If the variation amount of the objective function value diverges, update the pore structure parameters according to the array of variation amounts of pore structure parameters, and perform iterative optimization on the objective function again.

[0191] As an alternative solution, the above device is also used to, before performing iterative optimization on the objective function of the optimization model, further include:

[0192] Initialize the parameters of the optimization model, and the parameters of the optimization model include the number of convergence times, convergence threshold, difference multiple, and damping coefficient vector.

[0193] As an alternative solution, the above device is also used to determine the variation amount of the objective function value, including:

[0194] Differentiate the pore structure parameters according to the difference multiple;

[0195] Call the four-parameter random growth method and the finite-difference time-domain method to determine the corresponding thermal radiation characteristic parameters based on the differentiated pore structure parameters;

[0196] Determine the variation amount of the objective function value according to the thermal radiation characteristic parameters before differentiation and the values of the thermal radiation characteristic parameters after differentiation.

[0197] As an alternative solution, the above device is also used to determine the array of variation amounts of pore structure parameters, including:

[0198] Determine the Jacobian matrix based on the differentiated pore structure parameters;

[0199] According to the Jacobian matrix, solve the array of variation amounts of pore structure parameters through Formula 4, Formula 4, (J T J + μI)Δβ k = -J T r, where J T is the transpose of the Jacobian matrix of r(β), J is the Jacobian matrix of r(β), μI is the damping matrix, μ represents the damping coefficient vector corresponding to each parameter, I is a positive definite diagonal identity matrix, and Δβ k is the array of variation amounts of pore structure parameters.

[0200] As an alternative solution, the above device is also used to determine whether the array of variation amounts of pore structure parameters converges, including:

[0201] Compare the change amount array Δβ of the pore structure parameters corresponding to the k-th convergence k with the change amount array Δβ of the pore structure parameters corresponding to the (k - 1)-th convergence k-1 ;

[0202] If there is an element in the change amount array Δβ k whose absolute value is greater than the absolute value of the corresponding element in Δβ k-1 , or if there is an element in the change amount array Δβ k whose sign is different from the sign of the corresponding element in Δβ k-1 , then the change amount array Δβ of the pore structure parameters k diverges with respect to the change amount array Δβ of the pore structure parameters k-1 ;

[0203] Conversely, if the absolute value of each element in the change amount array Δβ k is less than or equal to the absolute value of the corresponding element in Δβ k-1 , and the sign of each element in the change amount array Δβ k is the same as the sign of the corresponding element in Δβ k-1 , then the change amount array Δβ of the pore structure parameters k converges with respect to the change amount array Δβ of the pore structure parameters k-1 .

[0204] As an optional solution, the above device is also used to, if the change amount array of the pore structure parameters shows a divergent trend, make the damping coefficient vector μ k = 5μ k , and recalculate the change amount array of the pore structure parameters according to Formula Four.

[0205] As an optional solution, the above device is also used to determine whether the change amount array of the objective function value converges, including:

[0206] Judge whether the absolute value of the difference between the objective function value S k corresponding to the k-th convergence and the objective function value S k-1 corresponding to the (k - 1)-th convergence is less than the convergence threshold;

[0207] If |S k - S k-1 | is less than the convergence threshold, then the change amount array of the objective function value converges.

[0208] As an optional solution, the above device is also used to, and further includes:

[0209] If the change amount array of the pore structure parameters converges, then judge the change amount array Δβ kwhether the absolute value of each element in is less than the corresponding convergence threshold;

[0210] If the change amount array Δβ k and the absolute value of each element in is less than the corresponding convergence threshold, stop the iteration, and use the pore structure corresponding to the current pore structure parameters as the pore structure with the optimal thermal radiation characteristics.

[0211] In the embodiments of the present application, the term "module" or "unit" refers to a computer program with a predetermined function or a part of a computer program, which works together with other related parts to achieve a predetermined goal, and can be fully or partially implemented by using software, hardware (such as a processing circuit or a memory), or a combination thereof. Similarly, one processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of the overall module or unit that includes the function of the module or unit.

[0212] Regarding the device in the above embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0213] According to one aspect of the present application, there is provided a computer program product, which includes a computer program.

[0214] The serial numbers of the embodiments of the present application above are only for description and do not represent the superiority or inferiority of the embodiments.

[0215] Figure 10 Schematically shows a block diagram of a computer system of an electronic device for implementing the embodiments of the present application.

[0216] It should be noted that Figure 10 the computer system 1100 of the electronic device shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0217] Such as Figure 10As shown, the computer system 1100 includes a central processing unit 1101 (CPU), which can perform various appropriate actions and processes according to the programs stored in the read-only memory 1102 (ROM) or the programs loaded from the storage section 1108 into the random access memory 1103 (RAM). In the random access memory 1103, various programs and data required for system operation are also stored. The central processing unit 1101, the read-only memory 1102, and the random access memory 1103 are connected to each other via a bus 1104. An input / output interface 1105 (Input / Output interface, i.e., I / O interface) is also connected to the bus 1104.

[0218] The following components are connected to the input / output interface 1105: an input section 1106 including a keyboard, a mouse, etc.; an output section 1107 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a local area network card, a modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the input / output interface 1105 as needed. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1110 as needed so that a computer program read from it can be installed into the storage section 1108 as needed.

[0219] In particular, according to an embodiment of the present application, the processes described in each method flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 1109, and / or installed from the removable medium 1111. When the computer program is executed by the central processing unit 1101, various functions defined in the system of the present application are executed.

[0220] In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 1109, and / or installed from the removable medium 1111. When the computer program is executed by the central processing unit 1101, various functions provided by the embodiments of the present application are executed.

[0221] According to another aspect of the embodiments of the present application, an electronic device for optimizing the pore structure of a thermal barrier coating is further provided. In this embodiment, the electronic device is taken as an example of a terminal device for illustration. As Figure 11 shown, the electronic device includes a memory 1202 and a processor 1204. A computer program is stored in the memory 1202, and the processor 1204 is configured to execute the steps in any of the above method embodiments through the computer program.

[0222] Optionally, in this embodiment, the above electronic device may be at least one of multiple network devices in a computer network.

[0223] Optionally, in this embodiment, the above processor may be configured to execute the methods in the embodiments of the present application through a computer program.

[0224] Optionally, those of ordinary skill in the art can understand that Figure 11 the structure shown is only schematic, Figure 11 and it does not limit the structure of the above electronic device. For example, the electronic device may further include more or fewer components (such as a network interface, etc.) than those shown Figure 11 , or have a different configuration from that shown Figure 11 .

[0225] Among them, the memory 1202 can be used to store software programs and modules, such as program instructions / modules corresponding to the method and device for optimizing the pore structure of the thermal barrier coating in the embodiments of the present application. The processor 1204 executes various functional applications and data processing by running the software programs and modules stored in the memory 1202, that is, implements the above method for optimizing the pore structure of the thermal barrier coating. The memory 1202 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 1202 may further include a memory remotely disposed relative to the processor 1204, and these remote memories can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof. Among them, the memory 1202 can specifically but not limitedly be used to store pore structure parameter data information. As an example, as Figure 11 shown, the above memory 1202 may include but not limitedly the numerical simulation module 101, the calculation module 102, the establishment module 103, and the determination module 104 in the above device for optimizing the pore structure of the thermal barrier coating. In addition, it may further include but not limitedly other module units in the above device, which will not be elaborated in this example.

[0226] Optionally, the above-mentioned transmission device 1206 is configured to receive or send data via a network. Specific examples of the above-mentioned network may include a wired network and a wireless network. In one example, the transmission device 1206 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers through a network cable, so as to communicate with the Internet or a local area network. In one example, the transmission device 1206 is a Radio Frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0227] In addition, the above-mentioned electronic device further includes: a display 1208, configured to display the above-mentioned thermal radiation characteristic data; and a connection bus 1210, configured to connect each module component in the above-mentioned electronic device.

[0228] In other embodiments, the above-mentioned terminal device or server may be a node in a distributed system. Among them, the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the multiple nodes in a form of network communication. Among them, the nodes can form a peer-to-peer network, and any form of computing device, such as electronic devices like servers and terminals, can become a node in the blockchain system by joining the peer-to-peer network.

[0229] According to one aspect of the present application, there is provided a computer-readable storage medium. The processor of the electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes the optimization method of the thermal barrier coating pore structure provided in the above-mentioned various optional implementation manners.

[0230] Optionally, in this embodiment, the above-mentioned computer-readable storage medium may be configured to store the methods for executing the embodiments of the present application.

[0231] Optionally, in this embodiment, those of ordinary skill in the art can understand that all or part of the steps in the above-mentioned various methods can be completed by instructing the relevant hardware of the terminal device through a program, and the program can be stored in a computer-readable storage medium. The storage medium may include: a flash drive, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disc, etc.

[0232] The serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0233] If the integrated units in the above embodiments are implemented in the form of software function units and sold or used as independent products, they can be stored in the above computer-readable storage media. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing one or more electronic devices to execute all or part of the steps of the methods described in various embodiments of this application.

[0234] In the above embodiments of this application, the descriptions of the various embodiments each have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0235] In the several embodiments provided by this application, it should be understood that the disclosed application program can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

[0236] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0237] In addition, the various functional units in the various embodiments of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software function units.

[0238] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0239] In summary, from the above description, it can be seen that the above embodiments of the present invention achieve the following technical effects:

[0240] 1. The present invention generates a numerical model of pore structure based on numerical simulation, and performs simulation calculations on the numerical model of pore structure. On the basis of accurately simulating the pore structure of the thermal barrier coating layer with high precision, it accurately evaluates the influence of the pore structure on the thermal radiation performance of the coating, provides a reliable basis for optimizing the optimization result of the model, and finally determines the pore structure with the best thermal radiation characteristics in combination with the optimization model, realizing the prediction and optimization of the pore structure of the thermal barrier coating.

[0241] 2. The present invention uses the LM optimization algorithm to establish an optimization model, repeatedly calls the four-parameter random growth method and the finite-difference time-domain method in the optimization model, determines the change amount array of pore structure parameters and the change amount of the objective function value, and realizes the iterative optimization of the objective function and the update of the pore structure parameters by sequentially judging whether the change amount array of pore structure parameters and the change amount of the objective function value converge, and finally determines the pore structure with the best thermal radiation characteristics.

[0242] 3. The present invention proposes to determine the Jacobian matrix based on the pore structure parameters after differentiation, and solve the change amount array of pore structure parameters according to the Jacobian matrix. This method can efficiently capture the influence of the change of pore structure parameters on the thermal radiation characteristics, thus significantly improving the optimization accuracy and efficiency of the pore structure of the thermal barrier coating.

[0243] 4. The present invention generates a numerical model of the pore structure of the thermal barrier coating by the four-parameter random growth method. This method can flexibly control the shape, size and distribution of pores, and can thus simulate a pore structure similar to the actual thermal barrier coating, providing an accurate input for subsequent radiation characteristic analysis.

[0244] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0245] It should be noted that in the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

Claims

1. An optimization method for the pore structure of a thermal barrier coating, characterized in that, Including: Numerically simulate the pore structure of the thermal barrier coating and generate a corresponding numerical model of the pore structure; Perform simulation calculations on the numerical model of the pore structure to determine the thermal radiation characteristic parameters of the pore structure; Establish an optimization model for the thermal radiation characteristic parameters; Based on the optimization model, determine the pore structure with the optimal thermal radiation characteristics.

2. The method according to claim 1, wherein Numerically simulate the pore structure of the thermal barrier coating and generate a corresponding numerical model of the pore structure, including: Simulate the pore structure of the thermal barrier coating using the four-parameter random growth method and generate a corresponding numerical model of the pore structure.

3. The method according to claim 2, wherein Simulate the pore structure of the thermal barrier coating using the four-parameter random growth method and generate a corresponding numerical model of the pore structure, including: Set the simulation area of the pore structure; Initialize the lattice points and pore structure parameters within the simulation area, where the pore structure parameters include the growth probability of the nucleation center of the growth phase, the growth probability of the growth nuclei in each direction, and the preset volume fraction of the growth phase; Randomly arrange the nucleation centers of the first growth phase according to the growth probability of the nucleation center of the growth phase; The nucleation centers of the first growth phase grow in the i-th direction according to the growth probability of the growth nuclei in each direction until the volume fraction of the growth phase reaches the preset volume fraction.

4. The method according to claim 2, wherein Perform simulation calculations on the numerical model of the pore structure to determine the thermal radiation characteristic parameters of the pore structure, including: Based on the numerical model of the pore structure, use the simulation method to simulate the thermal radiation process of the pore structure and determine the thermal radiation characteristic parameters of the pore structure. The thermal radiation characteristic parameters include one or more of reflectivity, transmittance, and absorptance.

5. The method according to claim 4, wherein Based on the numerical model of the pore structure, use the simulation method to simulate the thermal radiation process of the pore structure and determine the thermal radiation characteristic parameters of the pore structure, including: Discretize the Maxwell curl equation using the finite-difference time-domain method to determine the expressions of the electric field component and the magnetic field component varying with time; Use the expressions of the electric field component and the magnetic field component varying with time to determine the electric field intensity vector and the magnetic field intensity vector; Calculate the Poynting vector according to the electric field intensity vector and the magnetic field intensity vector; Determine the thermal radiation characteristic parameters of the pore structure based on the Poynting vector.

6. The method according to claim 5, wherein Calculate the Poynting vector according to the electric field intensity vector and the magnetic field intensity vector, including: According to the electric field intensity vector and the magnetic field intensity vector, the Poynting vector is calculated by Formula 1, and the Formula 1, where S represents the Poynting vector, T = 2π / ω is the period of the electromagnetic wave, E is the electric field intensity vector, and H is the magnetic field intensity vector.

7. The method according to claim 5, wherein Determine the thermal radiation characteristic parameters of the pore structure based on the Poynting vector, including: Based on the Poynting vector, determine the radiation intensity of the pore structure through Formula Two, where Formula Two is S = IdΩ, where S represents the Poynting vector and I is the radiation intensity; Determine the reflectivity and / or transmittance and / or absorptance of the pore structure according to the radiation intensity.

8. The method according to any one of claims 5 to 7, characterized in that Establish an optimization model for the thermal radiation characteristic parameters, including: An optimization model is established using the LM optimization algorithm, and an objective function of the optimization model is established based on the thermal radiation characteristic parameters. The objective function is where m represents the number of sampling points of the thermal radiation characteristic parameters in the translucent band, r i = f(x i , β) - y i , f(x i , β) represents the simulated value of a certain thermal radiation characteristic parameter of the pore structure at a wavelength of x i μm, β is the parameter to be optimized, and y i represents the theoretical value of a certain thermal radiation characteristic parameter of the pore structure at a wavelength of x i μm.

9. The method according to claim 8, wherein Based on the optimization model, determine the pore structure with the optimal thermal radiation characteristics, including: Use the optimization model to call the four-parameter random growth method and the finite-difference time-domain method to iteratively optimize the objective function of the optimization model until the objective function reaches a convergence state.

10. The method according to claim 9, wherein Using the optimization model to call the four-parameter stochastic growth method and the finite-difference time-domain method to iteratively optimize the objective function of the optimization model until the objective function reaches a convergence state, including: Using the optimization model to call the four-parameter stochastic growth method and the finite-difference time-domain method to determine the change amount array of the pore structure parameters and the change amount of the objective function value; Judging whether the change amount array of the pore structure parameters converges; If the change amount array of the pore structure parameters converges, then judging whether the change amount of the objective function value converges; If the change amount of the objective function value converges, stop the iteration and take the pore structure corresponding to the current pore structure parameters as the pore structure with the optimal thermal radiation characteristics; If the change amount of the objective function value diverges, update the pore structure parameters according to the change amount array of the pore structure parameters and iteratively optimize the objective function again.

11. The method according to claim 10, characterized in that, Before using the optimization model to call the four-parameter stochastic growth method and the finite-difference time-domain method, it further includes: Initializing the parameters of the optimization model, and the parameters of the optimization model include the number of convergence times, the convergence threshold, the difference multiple, and the damping coefficient vector.

12. The method according to claim 11, wherein Determining the change amount of the objective function value, including: Differencing the pore structure parameters according to the difference multiple; Calling the four-parameter stochastic growth method and the finite-difference time-domain method to determine the corresponding thermal radiation characteristic parameters based on the differenced pore structure parameters; Determining the change amount of the objective function value according to the thermal radiation characteristic parameters before difference and the thermal radiation characteristic parameters after difference.

13. The method according to claim 12, characterized in that, Determining the change amount array of the pore structure parameters, including: Determining the Jacobian matrix based on the differenced pore structure parameters; According to the Jacobian matrix, the change amount array of the pore structure parameters is solved by Formula Four, and Formula Four is (J T J + μI)Δβ k = -J T r, where J T is the transpose of the Jacobian matrix of r(β), J is the Jacobian matrix of r(β), μI is the damping matrix, μ represents the damping coefficient vector corresponding to each parameter, I is a positive definite diagonal identity matrix, and Δβ k is the change amount array of the pore structure parameters.

14. The method according to claim 13, wherein Judging whether the change amount array of the pore structure parameters converges, including: Compare the change amount array Δβ of the pore structure parameters corresponding to the k-th convergence k with the change amount array Δβ of the pore structure parameters corresponding to the (k - 1)-th convergence k-1 ; If there is an element in the change amount array Δβ k whose absolute value is greater than the absolute value of the corresponding element in Δβ k-1 , or if there is an element in the change amount array Δβ k whose sign is different from the sign of the corresponding element in Δβ k-1 , then the change amount array Δβ of the pore structure parameter k diverges with respect to the change amount array Δβ of the pore structure parameter k-1 ; Conversely, if the absolute value of each element in the change amount array Δβ k is less than or equal to the absolute value of the corresponding element in Δβ k-1 , and the sign of each element in the change amount array Δβ k is the same as the sign of the corresponding element in Δβ k-1 , then the change amount array Δβ k of the pore structure parameter converges relative to the change amount array Δβ k-1 of the pore structure parameter.

15. The method according to claim 14, wherein If the array of variation amounts of the pore structure parameters shows a divergent trend, let the damping coefficient vector μ k = 5μ k , and recalculate the array of variation amounts of the pore structure parameters according to Formula 4.

16. The method according to claim 11, wherein Judging whether the change amount array of the objective function value converges, including: Determine the objective function value S corresponding to the k-th convergence k and the objective function value S corresponding to the (k - 1)-th convergence k-1 whether the absolute value of the difference is less than the convergence threshold; If |S k -S k-1 | is less than the convergence threshold, the array of change amounts of the objective function value converges.

17. The method according to claim 14, characterized in that, It further includes: If the array of variation amounts of the pore structure parameters converges, it is determined whether the absolute value of each element in the array of variation amounts Δβ k is less than the corresponding convergence threshold; If the absolute value of each element in the change amount array Δβ k is less than the corresponding convergence threshold, stop the iteration, and use the pore structure corresponding to the current pore structure parameters as the pore structure with the optimal thermal radiation characteristics.

18. An optimization device for the pore structure of a thermal barrier coating, characterized in that Including: A numerical simulation module for numerically simulating the pore structure of the thermal barrier coating and generating a corresponding pore structure numerical model; A calculation module for performing simulation calculations on the pore structure numerical model to determine the thermal radiation characteristic parameters of the pore structure; A building module for building an optimization model of the thermal radiation characteristic parameters; A determination module for determining the pore structure with the optimal thermal radiation characteristics based on the optimization model.

19. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein the computer program can be executed when run by an electronic device to perform the method described in any one of claims 1 to 17.

20. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1 to 17.

21. An electronic device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is configured to execute the method described in any one of claims 1 to 17 through the computer program.

Images