A reliability optimization method for thermal insulation structures based on Bootstrap sampling

Abstract

The present invention discloses a reliability optimization method for a heat insulation structure based on Bootstrap sampling, which relates to the field of heat insulation of high-speed aircraft, and includes: S1, setting model parameters and uncertain distribution parameters for the design of the heat insulation structure based on actual requirements; S2, in the particle swarm optimization (PSO) method, for each particle in the population, generating K 1 set of uncertain variable parameter samples, and then calculating each particle in the population according to the heat transfer equation and boundary conditions to obtain K 1 sets of #imgabs0# and #imgabs1#; S3, based on the Bootstrap sampling method, expanding #imgabs2# and #imgabs3# to K 2 sets of #imgabs4# and #imgabs5#, and obtaining the failure probability #imgabs6# of all particles in the current iteration population through statistics and traversal, so as to be used as the reliability constraint condition of the optimization target; S4, based on the model parameters and uncertain distribution parameters in S1, solving the reliability optimization model to obtain the design variables of the heat insulation structure. The present invention provides a reliability optimization method for a heat insulation structure based on Bootstrap sampling, which makes the consistency between the optimized design result of the heat insulation structure and the actual result higher.

Description

Technical Field

[0001] The present invention relates to the field of heat insulation for high-speed aircraft. More specifically, the present invention relates to a method for optimizing the reliability of a heat insulation structure based on Bootstrap sampling. Background Art

[0002] The heat insulation structure is a key factor for protecting the safe operation of high-speed aircraft and completing various tasks. An effective heat insulation structure can meet the basic requirements of heat protection and insulation for high-speed aircraft, protecting it from the harm of extreme aerodynamic heat environments. A schematic diagram of the multi-layer heat insulation structure of a high-speed aircraft is shown as Figure 2 shown. Its heat insulation structure mainly includes an ablation-resistant layer K, a heat insulation layer G, and a load-bearing layer C from the outside to the inside. h K , h G , h C are the thicknesses of the ablation-resistant layer, the heat insulation layer, and the load-bearing layer respectively. T H is the temperature load outside the ablation-resistant layer. T L is the ambient temperature inside the cabin. T in is the temperature of the inner surface of the load-bearing layer. T out is the temperature of the outer surface of the ablation-resistant layer. In actual applications, the extreme high-temperature heat environment endured by the high-speed aircraft during flight heats the high-temperature resistant layer of the multi-layer heat insulation structure through forced convection heat transfer. At the same time, the ablation-resistant layer radiates heat to the external space, dissipating part of the heat. The main part of the remaining heat is blocked by the heat insulation layer, and a small part of the heat is absorbed, stored, and transferred to the base layer by the material of the ablation-resistant layer itself. The base layer structure conducts natural convection heat transfer with the internal air. The quality of the heat insulation performance of the multi-layer heat insulation structure mainly depends on the material parameters of the heat insulation layer and the thickness dimensions of each layer. The equivalent mechanical performance of the overall structure is mainly determined by the material parameters of the base layer. Therefore, the base layer is sometimes also called the load-bearing layer according to its function. However, currently, when optimizing the design of the multi-layer heat insulation structure of high-speed aircraft, their focuses vary. And the currently relatively mature technologies are mainly divided into the following categories:

[0003] First, when designing the multi-layer heat insulation structure of high-speed aircraft, it mainly focuses on optimizing the multi-layer heat insulation structure by considering the selection of material types under determined material parameters. For example, the patent application titled "A Method for Optimizing the Design of a Multi-Layer Heat Insulation Structure of a High-Speed Aircraft". This case mainly establishes an optimization model by considering the selection of materials for each layer to achieve the purpose of optimizing the structural lightweight.

[0004] Second, when designing the multi-layer thermal insulation structure of a high-speed aircraft, the focus is mainly on the consideration of bionic optimization methods. For example, in the patent application titled "Optimization Method and System for Thermal Protection Scheme of Thermal Insulation and Protection Integrated Structure", this case mainly uses the particle swarm algorithm for structural optimization to obtain the optimized thickness of each layer;

[0005] Third, when dealing with the multi-layer thermal insulation structure of a high-speed aircraft, the focus is mainly on the solution of the unsteady heat transfer equation. For example, in the patent application titled "A Fast Analysis and Design Method for the Thermal Protection System of an Aerospace Aircraft", this case mainly uses the finite difference scheme of one-dimensional heat conduction to solve the one-dimensional unsteady heat transfer equation to achieve the goal of obtaining the structural temperature response, and optimizes the thickness of each layer with the goal of lightweight.

[0006] However, when using the above three methods to design the multi-layer thermal insulation structure of a high-speed aircraft, since the uncertainty of material parameters and temperature loads is not considered, and in actual applications, there is a certain uncertainty in the material properties and loads compared with the theoretical design values, which makes the optimized design results inconsistent with the actual results after the thermal insulation structure is designed. Summary of the Invention

[0007] One object of the present invention is to solve at least the above problems and / or defects and provide at least the advantages described hereinafter.

[0008] To achieve these objects and other advantages of the present invention, a reliability optimization method for a thermal insulation structure based on Bootstrap sampling is provided, including:

[0009] S1. Set the model parameters and uncertain distribution parameters for the design of the thermal insulation structure based on actual requirements;

[0010] S2. In the particle swarm optimization (PSO) method, for each particle in the population, generate K 1 groups of uncertain variable parameter samples, and then calculate each particle in the population according to the heat transfer equation and boundary conditions to obtain K 1 groups of temperatures at the inner surface of the load-bearing layer and equivalent moduli ;

[0011] S3. Based on the Bootstrap sampling method, expand , to K 2 groups of temperatures at the inner surface of the load-bearing layer and equivalent moduli , and obtain the failure probability of all particles in the current iterative population through statistics and traversal, and use it as the reliability constraint condition for the optimization objective;

[0012] S4. Based on the model parameters and uncertainty distribution parameters in S1, solve the reliability optimization model to obtain the design variables of the thermal insulation structure;

[0013] Among them, in S2, the particles refer to the thickness dimensions of the ablation-resistant layer, the bearing layer, and the thermal insulation layer.

[0014] Optionally, in S1, the model parameters include: the allowable minimum equivalent modulus E min , the maximum allowable temperature of the bearing layer , the ambient temperature inside the cabin T L , the temperature load outside the ablation-resistant layer T H , the minimum thickness of the ablation-resistant layer x 1min , the maximum thickness of the ablation-resistant layer x 1max , the minimum thickness of the bearing layer x 2min , the maximum thickness of the bearing layer x 2max , the minimum thickness of the thermal insulation layer x 3min , the maximum thickness of the thermal insulation layer x 3max , the forced convection heat transfer coefficient between the outer surface of the ablation-resistant layer and the temperature load , the natural convection heat transfer coefficient between the inner surface of the bearing layer and the normal temperature static fluid , the stop calculation time t end , the allowable minimum equivalent modulus E min , the maximum allowable failure probability P max , the number of samples of the first group of uncertain variable parameters K 1 , the number of samples of the second group of uncertain variable parameters K 2 ;

[0015] The uncertainty distribution parameters include: the coefficient of variation of the temperature load, E K is the Young's modulus of the ablation-resistant layer K, E G is the Young's modulus of the thermal insulation layer G, E C is the Young's modulus of the bearing layer C.

[0016] Optionally, in S2, the heat transfer equation is characterized by the following formula:

[0017]

[0018] In the above formula, T is the temperature, t is the time, ρ is the density, β is the specific heat capacity, is the thermal conductivity, z is the coordinate in the thickness direction;

[0019] The boundary conditions are characterized by the following formula:

[0020]

[0021] In the above formula, is the thermal conductivity parameter of the anti-ablation layer material, h K-HTF is the forced convection heat transfer coefficient between the outer surface of the anti-ablation layer and the temperature load, ε is the emissivity, σ is the Stefan-Boltzmann constant, T out is the temperature of the outer surface of the anti-ablation layer, is the thermal conductivity parameter of the bearing layer material, h C-NTF is the natural convection heat transfer coefficient between the inner surface of the bearing layer and the normal temperature static fluid, is the gradient of the temperature T with respect to the spatial coordinate z, t is the time, h K is the thickness of the anti-ablation layer, h G is the thickness of the heat insulation layer, h C is the thickness of the bearing layer, is the temperature of the inner surface of the bearing layer at time t, T L is the environmental temperature in the cabin;

[0022] Equivalent modulus is characterized by the following formula:

[0023] In the above formula, E K is the Young's modulus of the anti-ablation layer K, E G is the Young's modulus of the heat insulation layer G, Ec is the Young's modulus of the bearing layer C;

[0024] Among them, the is K 1 calculated under different parameter groups of K 1The in-group surface temperature response is calculated by solving the heat transfer equation and boundary conditions through the pde function in Matlab.

[0025] Preferably, in S3, the failure probability is obtained through the following process:

[0026] S30. For the observed samples , let , i = 1, 2, … n , and n = K 1 , is an unknown distribution function. Then, the empirical distribution function F n constructed from the observed samples is characterized by the following formula:

[0027]

[0028] In the above formula, is the order statistic obtained by sorting x 1 , x 2 , …, x n from smallest to largest, k = 1, 2, …, n - 1;

[0029] S31. Take a random variable with a Dirichlet distribution for sample extraction to obtain the regenerated bootstrap samples , and N = K 2 , ;

[0030] S32. Estimate the error through the following formula:

[0031] In the above formula, is the empirical distribution function of the bootstrap samples, R n is a function of the random variables and F n , is the estimated value of the population parameter , and ;

[0032] S33. Estimate the unknown parameters and repeat S30 - S33 to obtain the stationary values of the parameters to be found;

[0033] S33. Based on and statistically obtain the failure probability of the corresponding particle P f , by traversing all the particles in the population, obtain the failure probabilities of all the particles in the population for this iteration .

[0034] Optionally, in S4, the reliability optimization model is characterized by the following formula:

[0035]

[0036]

[0037] In the above formula, x represents the variable to be optimized in the design, t is the time, ρ obj is the optimization objective function, x min is the lower limit of the design variable, x max is the upper limit of the design variable, , , are respectively the variables in the uncertain distribution parameters, and , , , , , are respectively , , the probability density functions that T H obeys, is T H the probability density function that t end obeys at time t, θ max is the maximum allowable temperature of the bearing layer, E min is the allowable minimum equivalent modulus, P max is the allowable maximum failure probability, E eff is the equivalent modulus, T in is the temperature of the inner surface of the bearing layer, P f is the failure probability of the corresponding particle, , , are the thermal conductivity parameters of the ablative-resistant layer, heat-insulating layer, and load-bearing layer materials respectively, β K , β G , β C are the specific heat capacities of the ablative-resistant layer, heat-insulating layer, and load-bearing layer materials respectively.

[0038] The present invention has at least the following beneficial effects: Compared with the prior art, in the process of optimizing the design of the multi-layer heat-insulating structure of a high-speed aircraft, by introducing the failure probability constraint condition into the optimization design model, the problem of optimizing the design of the heat-insulating structure under reliability constraints is solved, and the obtained solution better meets the requirements of practical applications and effectively reduces the failure risk during use.

[0039] Other advantages, objectives, and features of the present invention will be partially reflected by the following description and partially understood by those skilled in the art through the research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic flow chart of the heat-insulating structure reliability optimization method based on Bootstrap sampling of the present invention;

[0041] Figure 2 is a schematic layout diagram of the heat-insulating structure of the present invention;

[0042] Figure 3 is a schematic diagram of the temperature load in the embodiment of the present invention;

[0043] Figure 4 is a schematic diagram of the optimization result in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following further describes the present invention in detail with reference to the drawings so that those skilled in the art can implement it according to the description in the specification.

[0045] For the optimization design of the multi-layer heat-insulating structure of a high-speed aircraft, the present invention proposes a heat-insulating structure reliability optimization method based on Bootstrap sampling, which is used to solve the problem that the existing optimization design method for the multi-layer heat-insulating structure of a high-speed aircraft does not consider the uncertainties of material parameters and loads. By introducing the failure probability constraint condition into the optimization design model, the problem of optimizing the design of the heat-insulating structure under reliability constraints is solved, which better meets the requirements of practical applications.

[0046] A heat-insulating structure reliability optimization method based on Bootstrap sampling includes:

[0047] S10, set the number of iterations, the population size, the material parameters involved in the optimization model parameters, the uncertain distribution parameters related to the load, etc.;

[0048] S20, for each particle in the population, generate K 1 groups of uncertain variable parameter samples. For most cases K 1 it is impossible to be very large;

[0049] S30, for each particle in the population, that is, the thickness dimension of each group, calculate according to the heat transfer equation and boundary conditions to obtain K 1 the temperature of the inner surface of the bearing layer and the equivalent modulus ;

[0050] The heat transfer equation is:

[0051]

[0052] where T is the temperature, t is the time, ρ is the density, β is the specific heat capacity, is the thermal conductivity, z is the coordinate in the thickness direction.

[0053] The boundary conditions are:

[0054]

[0055] where is the thermal conductivity parameter of the anti-ablation layer material, h K-HTF is the forced convection heat transfer coefficient between the outer surface of the anti-ablation layer and the temperature load, ε is the emissivity (between 0 and 1), σ is the Stefan-Boltzmann constant, T out is the temperature of the outer surface of the anti-ablation layer, is the thermal conductivity parameter of the bearing layer material, h C-HT is the natural convection heat transfer coefficient between the inner surface of the bearing layer and the normal temperature static fluid, , is the gradient of the temperature T with respect to the spatial coordinate z, t is the time.

[0056] is K 1 the K 1The in - group surface temperature response is calculated by solving the heat transfer equation and boundary conditions through the pde function in Matlab;

[0057] The equivalent modulus is:

[0058]

[0059] Wherein, , E K is the Young's modulus of the ablation - resistant layer K, E G is the Young's modulus of the heat - insulating layer G, Ec is the Young's modulus of the bearing layer C, one(3)=[1 1 1 ], x= h K h G h C , h K is the thickness of the ablation - resistant layer, h G is the thickness of the heat - insulating layer, h C is the thickness of the bearing layer.

[0060] S40. According to the Bootstrap sampling method, expand the K 1 group of bearing - layer inner - surface temperatures and equivalent moduli to K 2 groups of bearing - layer inner - surface temperatures and equivalent moduli . This step mainly solves the problem that it is difficult to estimate uncertain parameters under small samples. Therefore, the Bootstrap sampling method is used to estimate uncertain parameters during structural optimization. By considering the structural failure probability constraint, a design scheme that conforms to the actual engineering situation is obtained, which specifically includes the following sub - steps:

[0061] S401. Construction of the empirical distribution function

[0062] Since the sample size of the observed sample is limited. Let , i = 1,2, … n , is the unknown distribution function, then the empirical distribution function constructed by the observed sample is:

[0063] In the formula, is the order statistic. Arrange x 1 , x 2,…, x n Sorted in ascending order, where n = K 1 .

[0064] Suppose is a certain parameter of the population, such as the mean and variance, is the estimated value of the population parameter , defined as:

[0065]

[0066] where error n is 's estimation error. It is easy to know that error n is a random variable X and F 's function.

[0067] S402, Sample extraction

[0068] Take a random variable with a Dirichlet distribution, where is a uniformly distributed random number from (0, 1) and is arranged in ascending order. Let , , then there is:

[0069]

[0070] These N groups of resampled samples are defined as: , and this regenerated resampled sample is called a bootstrap sample, and . Each time a computer sample is taken, a group of V i can be obtained, and correspondingly a resampled sample can be obtained. When j changes and samples n times, a set of data can be obtained. A total of N groups are sampled, where N = K 2 .

[0071] S403, Error estimation calculation

[0072] The error estimation calculation formula is in the following formula R n :

[0073]

[0074] In the formula, is the empirical distribution function of the bootstrap sample, R n is a random variable and F n function, by means of computer for multiple samplings, the R n probability distribution can be obtained.

[0075] S404, unknown parameter estimation

[0076] error n distribution and R n distribution are combined, and we can get:

[0077]

[0078] Computer simulation sampling K 2 times, K 2 can take 10000 or larger, and then we can get K 2 pieces of , unknown parameter distribution and eigenvalues can be calculated by corresponding statistical methods.

[0079] Repeat S402~S404 to gradually obtain the steady value of the parameter to be found.

[0080] S50, based on K 2 groups of inner surface temperature and equivalent modulus of the bearing layer, the failure probability of the particle is statistically obtained P f ;

[0081] S60, traverse all particles in the population to obtain the failure probability of all particles in the population for this iteration ;

[0082] S70, find all particles that meet the failure probability requirement, and obtain the particle with the optimal objective function as the optimal particle of this generation, and record the characteristic parameters of the particle;

[0083] The optimization model is:

[0084]

[0085]

[0086] Among them, x represents the variable to be optimized in design, specifically the thicknesses of the ablation-resistant layer, heat-insulating layer and bearing layer, t is time, ρ obj is the optimization objective function (i.e., surface mass density), x minis the lower limit of the design variable, x max is the upper limit of the design variable, 、 、 are respectively the variables in the uncertain distribution parameters, and 、 、 , 、 、 are respectively 、 、 the probability density functions they follow, T H is the temperature load outside the ablation-resistant layer, is T H the probability density function that follows at time t, t end is the stop calculation time, θ max is the maximum allowable temperature of the bearing layer, E min is the allowable minimum equivalent modulus, P max is the allowable maximum failure probability, E eff is the equivalent modulus, T in the temperature of the inner surface of the bearing layer, P f is the failure probability of the corresponding particle, 、 、 are respectively the thermal conductivity parameters of the ablation-resistant layer, heat insulation layer, and bearing layer materials, β K 、 β G 、 β C are respectively the specific heat capacities of the ablation-resistant layer, heat insulation layer, and bearing layer materials.

[0087] S80, perform population particle update, update the particle positions and velocities to generate a new population, repeat S20 - S70, if the new optimal particle is better than the previous generation of optimal particles, then perform optimal particle update, otherwise do not update;

[0088] S90, when the iteration update times reach the maximum iteration times, stop the iteration, and output the optimized design variables (i.e., the geometric dimensions of each part of the heat insulation structure).

[0089] Example:

[0090] Such as Figure 1As shown in the figure, the steps in the actual application of the present invention include:

[0091] S1. Set the values of the uncertain distribution parameters in the optimization model as follows: x 1min = 0.5 mm, x 1max = 3 mm, x 2min = 5 mm, x 2max = 50 mm, x 3min = 2 mm, x 3max = 5 mm, E min = 10 GPa, T H = 1200 °C, T L = 25 °C, θ max = 70 °C, h K-HTF = 230 W / (m 2 ·°C), h C-NTF = 10 W / (m 2 ·°C), t end = 600 s, K 1 = 50, K 2 = 10000, P max = 0.01, E min = 140 GPa.

[0092] Set the coefficient of variation of the temperature load in the uncertain distribution parameter to 0.05. For the setting of Young's modulus, thermal conductivity, specific heat capacity, etc., the parameter dataset of the anti-ablative layer material parameter Inconel 718 is shown in Table 1 as follows: T H The parameter dataset of the insulation layer material parameter Saffil is shown in Table 2 as follows:

[0093] Table 1

[0094]

[0095] The parameter dataset of the bearing layer material parameter 2024 aluminum alloy is shown in Table 3 as follows:

[0096] Table 2

[0097]

[0098] The parameter dataset of the bearing layer material parameter 2024 aluminum alloy is shown in Table 3 as follows:

[0099] Table 3

[0100]

[0101] S2. In the particle swarm optimization (PSO) method, for each particle in the population, generate K 1 a set of uncertain variable parameter samples, and then calculate each particle in the population according to the heat transfer equation and boundary conditions to obtain K 1 a set of temperatures on the inner surface of the bearing layer and equivalent modulus ;

[0102] S3. Based on the Bootstrap sampling method, expand 、 to K 2 a set of temperatures on the inner surface of the bearing layer and equivalent modulus , and obtain the failure probability of all particles in the current iterative population through statistics and traversal , to be used as the reliability constraint condition of the optimization objective;

[0103] S4. Based on the model parameters and uncertain distribution parameters in S1, solve the reliability optimization model to obtain the design variables of the thermal insulation structure;

[0104] Among them, the change of the temperature load in the above process is as Figure 3 shown (in Figure 3 , the solid line is the temperature curve, the dashed line is the 3σ upper bound, and the dash-dotted line is the 3σ lower bound);

[0105] In the above process, when PSO optimization is adopted, with a population size of 50 and 200 iterations, its optimization result is as Figure 4 shown (in Figure 4 , the solid line is the objective function curve, the dashed line is the failure probability curve, and the design variables at point A are: [1.0183, 47.7569, 2.2089] mm, 16.8211 kg / m 2 ; the design variables at point B are: [0.6491, 46.6154, 2.0856] mm, 13.4285 kg / m 2 ; point D is the maximum allowable failure probability), according to the optimization result, the optimal value of the objective function is 13.4285 kg / m 2 , and the corresponding design variables are: x = (0.6491, 46.6154, 2.0856) mm. During the entire optimization process, the particle failure probability is less than 0.01, meeting the reliability constraint conditions.

[0106] In practical engineering applications, a 100% reliable situation does not exist. In most cases, it is only necessary to meet the requirements of the failure probability. This method introduces the failure probability into the structural optimization design, making the optimization design results more in line with the actual situation.

[0107] The above solution is only an illustration of a preferred example, but is not limited thereto. When implementing the present invention, appropriate substitutions and / or modifications can be made according to the needs of users.

[0108] Although the embodiments of the present invention have been disclosed as above, it is not limited to the applications listed in the specification and the embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the field, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the equivalent scope, the present invention is not limited to the specific details and the illustrated and described examples here.

Claims

1. A reliability optimization method for thermal insulation structure based on Bootstrap sampling, characterized in that: include: S1. Set the model parameters and uncertain distribution parameters of the thermal insulation structure design based on actual needs; S2. In the particle swarm optimization PSO method, for each particle in the population, a K 1 set of uncertain variable parameter samples, and then calculate each particle in the population according to the heat transfer equation and boundary conditions to obtain K Temperature of the inner surface of the bearing layer 1 and equivalent modulus ; S3, based on the Bootstrap sampling method, , Expand to K Temperature of the inner surface of the 2 groups of bearing layers and equivalent modulus , the failure probability of all particles in the current iteration population is obtained by statistics and traversal , as the reliability constraint of the optimization objective; S4, based on the model parameters and uncertain distribution parameters in S1, the reliability optimization model is solved to obtain the design variables of the thermal insulation structure; Among them, in S2, the particles refer to the thickness of the anti-ablation layer, the bearing layer, and the thermal insulation layer.

2. The reliability optimization method of thermal insulation structure based on Bootstrap sampling according to claim 1, characterized in that: In S1, the model parameters include: the minimum equivalent modulus allowed E min , Maximum allowable temperature of the bearing layer , Ambient temperature in the cabin T L , Temperature load outside the anti-ablation layer T H , minimum thickness of the anti-ablation layer x 1min , the maximum thickness of the anti-ablation layer x 1max , minimum thickness of the bearing layer x 2min , Maximum thickness of the bearing layer x 2max , minimum thickness of insulation layer x 3min , Maximum thickness of thermal insulation layer x 3max , forced convection heat transfer coefficient between the outer surface of the anti-ablation layer and the temperature load , the natural convection heat transfer coefficient between the inner surface of the bearing layer and the static fluid at room temperature , stop calculating time t end , the minimum equivalent modulus allowed E min , maximum allowable failure probability P max , the number of samples of the first group of uncertain variable parameters K 1. The number of samples of the second group of uncertain variable parameters K 2; The uncertain distribution parameters include: temperature load variation coefficient, E K is the Young's modulus of the anti-ablation layer K, E G is the Young's modulus of the thermal insulation layer G, E C is the Young’s modulus of the load-bearing layer C.

3. The reliability optimization method of thermal insulation structure based on Bootstrap sampling according to claim 1, characterized in that: In S2, the heat transfer equation is characterized by the following formula: In the above formula, T is temperature, t is time, ρ is the density, β is the specific heat capacity, is the thermal conductivity, z is the thickness direction coordinate; The boundary condition is characterized by the following formula: In the above formula, is the thermal conductivity parameter of the anti-ablation layer material, h K-HTF is the forced convection heat transfer coefficient between the outer surface of the anti-ablation layer and the temperature load, ε is the emissivity, σ is the Stefan-Boltzmann constant, T out is the temperature of the outer surface of the anti-ablation layer, is the thermal conductivity parameter of the bearing layer material, h C-NTF is the natural convection heat transfer coefficient between the inner surface of the bearing layer and the static fluid at room temperature, is the gradient of temperature T with respect to spatial coordinate z, t is time, h K is the thickness of the anti-ablation layer, h G is the thickness of the insulation layer, h C is the thickness of the bearing layer, is the temperature of the inner surface of the bearing layer at time t, T L is the ambient temperature in the cabin; Equivalent modulus It is characterized by the following formula: In the above formula, E K is the Young's modulus of the anti-ablation layer K, E G is the Young's modulus of the thermal insulation layer G, Ec is the Young's modulus of the load-bearing layer C; Among them, the The temperature response of the inner surface of the K1 group calculated under different parameters of the K1 group is obtained based on the heat transfer equation and boundary conditions and is solved and calculated by the pde function in MATLAB.

4. The reliability optimization method of thermal insulation structure based on Bootstrap sampling as claimed in claim 3, characterized in that: In S3, the failure probability The acquisition process includes: S30, for the observed samples ,make , i =1,2,… n ,and n = K 1 , If the distribution function is unknown, then the empirical distribution function constructed by the observed sample is F n It is characterized by the following formula: In the above formula, Yes x 1, x 2,…, x n The order statistics obtained after sorting from small to large, k =1,2,…,n-1; S31. Take a random variable with a Dirichlet distribution Perform sample extraction to obtain a regenerated self-service sample ,and N = K 2, ; S32. Estimation of error is performed by the following formula: In the above formula, is the empirical distribution function of the bootstrap sample, R n is a random variable and F n The function of is the overall parameter The estimated value of ; S33, estimate the unknown parameters, and repeat S30 to S33 to obtain the stable value of the desired parameter; S33, based on and Statistically obtain the failure probability of the corresponding particles P f , by traversing all particles in the population, the failure probability of all particles in this iteration population is obtained .

5. The reliability optimization method of thermal insulation structure based on Bootstrap sampling according to claim 4 is characterized in that: In S4, the reliability optimization model is characterized by the following formula: In the above formula, x represents the variable to be optimized, t is the time, ρ obj To optimize the objective function, x min is the lower limit of the design variable, x max is the upper limit of the design variable, , , are variables in the uncertain distribution parameters, and , , , , , They are , , The probability density function is obeyed, T H is the temperature load outside the anti-ablation layer, for T H The probability density function obeyed at time t is, t end To stop counting time, θ max is the maximum allowable temperature of the bearing layer, E min To allow for a minimum equivalent modulus, P max is the maximum allowed failure probability, E eff is the equivalent modulus, T in The temperature of the inner surface of the bearing layer, P f is the failure probability of the corresponding particle, , , are the thermal conductivity parameters of the anti-ablation layer, thermal insulation layer, and bearing layer materials, β K , β G , β C They are the specific heat capacities of the anti-ablation layer, thermal insulation layer and bearing layer materials respectively.

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