Variable-density high-vacuum multi-layer heat insulation structure and optimization method thereof

Abstract

The invention relates to a variable-density high-vacuum multi-layer heat insulation structure and an optimization method thereof, and the optimization method comprises the following steps: determining design parameters and performance indexes of the variable-density high-vacuum multi-layer heat insulation structure, and carrying out the functional expression of each performance index based on the design parameters, so as to obtain a simulation function of each performance index; and based on the simulation function of each performance index, establishing an optimal arrangement problem of the variable-density high-vacuum multi-layer heat insulation structure, and solving the optimal arrangement problem by adopting a genetic algorithm to realize optimization of the variable-density high-vacuum multi-layer heat insulation structure. When the genetic algorithm is adopted to solve the optimal arrangement problem, population individuals are generated based on the number of heat insulation layers among all reflecting screens of the variable-density high-vacuum multi-layer heat insulation structure, and a heat flow density function of the variable-density high-vacuum multi-layer heat insulation structure serves as a fitness function. Compared with the prior art, the method has the advantages of effectively improving the heat insulation performance, being high in efficiency and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of high vacuum and low temperature thermal insulation, and in particular to a variable density high vacuum multi-layer thermal insulation structure and an optimization method thereof. Background Art

[0002] The world is currently facing increasingly severe energy and environmental problems. As a clean and efficient energy source, hydrogen has the advantages of high energy density, high combustion calorific value, and zero carbon emissions. It has broad application prospects in the fields of transportation, energy, and industry in the future, and plays a key role in promoting global energy transformation, responding to climate change, and achieving sustainable development goals. Large-scale storage of hydrogen is one of the key technologies to achieve the widespread application of hydrogen energy, which directly affects the promotion and use efficiency of hydrogen energy in the fields of transportation, energy, and industry. Among the various hydrogen storage technologies that have been developed, liquid hydrogen storage has attracted much attention due to its unique advantages. Liquid hydrogen storage can significantly increase its volume energy density by cooling hydrogen to an extremely low temperature (-253℃) liquid state, that is, storing more hydrogen in the same volume, which makes liquid hydrogen storage have greater advantages under space-constrained conditions. Reducing heat leakage in liquid hydrogen storage tanks is the key to ensuring the safe and efficient storage of liquid hydrogen. Variable density multi-layer insulation technology is widely used due to its efficient thermal insulation effect. Variable density multilayer insulation technology significantly reduces heat transfer and liquid hydrogen evaporation loss by optimizing the density and number of layers of insulation materials. It has excellent durability and stability and is regarded as the most reliable insulation system in current liquid hydrogen storage. It provides important technical support for large-scale storage and transportation of liquid hydrogen. However, existing high vacuum variable density multilayer insulation structures usually adopt high, medium and low three-layer variable density design, which fails to fully optimize insulation performance. Patent CN114792047B also discloses an optimization method for variable density high vacuum multilayer insulation structure, which is based on the exhaustive method to obtain the design parameters of all variable density multilayer insulation material combinations with different thicknesses under the target number of layers, and the search efficiency is low.

[0003] Therefore, there is an urgent need for an efficient algorithm to optimize the optimal variable-density multilayer insulation structure under given boundary conditions. Summary of the invention

[0004] The purpose of the present invention is to provide a variable-density high-vacuum multi-layer thermal insulation structure and an optimization method thereof that can effectively improve thermal insulation performance and have high efficiency.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] An optimization method for a variable-density high-vacuum multi-layer thermal insulation structure comprises the following steps:

[0007] Determine the design parameters and performance indicators of the variable density high vacuum multi-layer insulation structure, and express each of the performance indicators in a functional manner based on the design parameters to obtain a simulation function of each performance indicator;

[0008] Based on the simulation function of each performance index, an optimal arrangement problem of a variable-density high-vacuum multi-layer thermal insulation structure is established, and a genetic algorithm is used to solve the optimal arrangement problem to achieve optimization of the variable-density high-vacuum multi-layer thermal insulation structure;

[0009] When the genetic algorithm is used to solve the optimal arrangement problem, population individuals are generated based on the number of insulation layers between each reflective screen of the variable density high vacuum multi-layer insulation structure, and the heat flux density function of the variable density high vacuum multi-layer insulation structure is used as the fitness function.

[0010] Furthermore, the design parameters include the reflectivity of the hot surface of the reflection screen, the reflectivity of the cold surface of the reflection screen, the thickness of each insulation layer and the thermal conductivity of the insulation layer.

[0011] Furthermore, the performance indicators include reflective screen temperature, total heat flux density, thermal radiation heat flux density, gas thermal conduction heat flux density, solid thermal conduction heat flux density and thermal resistance of each layer of the insulation structure.

[0012] Furthermore, in the genetic algorithm, M layers of heat insulation are randomly distributed between N+1 layers of reflection screens, and N is used as the number of genes of the genetic algorithm chromosome to generate an initial population.

[0013] Furthermore, in the genetic algorithm, the fitness function is expressed as:

[0014]

[0015] in, The fitness of the kth individual in the i-th iteration, is the heat flux density of the k-th individual variable-density high-vacuum multilayer insulation structure in the i-th iteration, is the total thermal resistance of the kth individual variable density high vacuum multilayer insulation structure in the i-th iteration, T H is the temperature of the hot end reflective screen of the variable density high vacuum multi-layer insulation structure, T C It is the temperature of the cold end reflection screen of the variable density high vacuum multi-layer insulation structure.

[0016] Furthermore, in the genetic algorithm, the optimization of the variable-density high-vacuum multi-layer insulation structure is achieved through mutation, crossover and elite retention processing.

[0017] Furthermore, during the mutation process, a random number is generated and compared with the set adaptive mutation probability. If the random number is less than the adaptive mutation probability, the current individual is taken as a mutant individual, and multiple gene bits in the chromosome of the mutant individual are flipped or replaced with new gene values. The expression of the adaptive mutation probability is:

[0018]

[0019] Among them, P M is the adaptive mutation probability, f s is the fitness of the mutant individual in the previous iteration, f a is the average fitness of the population in the previous iteration, f min is the minimum fitness of the population in the previous iteration, and P1 is the basic probability of mutation.

[0020] Furthermore, during the crossover process, a random number is generated and compared with the set adaptive crossover probability. If the random number is less than the adaptive crossover probability, a crossover point is randomly selected on the chromosome of the parent individual, and the chromosomes of the two parent individuals are exchanged at these crossover points to form a new offspring individual. The expression of the adaptive crossover probability is:

[0021]

[0022] Among them, P C is the adaptive crossover probability, f s is the fitness of the mutant individual in the previous iteration, f a is the average fitness of the population in the previous iteration, f min is the minimum fitness of the population in the previous iteration, and P2 is the crossover basic probability.

[0023] Furthermore, in the elite retention process, each iteration selects from the population the population with a fitness higher than the preset threshold f th The individual is copied as the optimal individual in this iteration stage and retained as an elite individual. The expression of the preset threshold is:

[0024] f th =(f max -f best )×0.8+f best

[0025] Among them, f max is the maximum fitness in this iteration, f best is the optimal fitness among all iterations.

[0026] The present invention also provides a variable-density high-vacuum multi-layer thermal insulation structure, which is obtained by using the optimization method described above.

[0027] Compared with the prior art, the present invention reduces heat transfer to the greatest extent by accurately calculating the distribution of each layer of insulation material, thereby improving the insulation performance and storage efficiency of the storage tank while ensuring safety, and has the following beneficial effects:

[0028] 1. Compared with the traditional multi-layer insulation structure, the present invention optimizes the distribution of the insulation layer and the reflective layer by optimizing the variable density structure, which can achieve better insulation effect with the same number of reflective screens and insulation layers, has higher thermal resistance, and shows excellent insulation performance improvement potential.

[0029] 2. The present invention uses a genetic algorithm to optimize the variable-density high-vacuum multilayer insulation structure, and the search efficiency is significantly improved compared to the traditional exhaustive method. Compared with other optimization methods, the genetic algorithm has a stronger global search capability and can fully explore all possible solutions in the solution space in a short time, thereby finding the optimal solution more quickly. This optimization method not only improves the design efficiency, but also improves the overall optimization effect of the structural performance.

[0030] 3. The present invention adopts adaptive crossover and mutation factors to dynamically adjust the parameters of the genetic algorithm, so that the algorithm achieves a good balance between global search and local development, while accelerating the convergence speed and improving the search quality, which helps to improve the overall performance of the genetic algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of variable density high vacuum multi-layer insulation structure in an embodiment of the present invention. In the figure, 1 is a reflective screen, 2 is an insulation layer, 3 is a cold boundary, 4 is a hot boundary, 5 is a liquid hydrogen storage tank, and 6 represents the total heat flux density;

[0032] Figure 2 The figure is a schematic diagram of the process of the optimization method of the present invention. DETAILED DESCRIPTION

[0033] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0034] This embodiment provides an optimization method for a variable density high vacuum multi-layer thermal insulation structure. The variable density high vacuum multi-layer thermal insulation structure used is referenced Figure 1As shown, this variable density high vacuum multi-layer insulation structure is filled with multiple layers of insulation materials under a high vacuum environment, including a reflective screen 1 and an insulation layer (spacer layer) 2 alternately stacked. Specifically, the reflective screen can be made of aluminum foil, and the aluminum foil layer is responsible for reflecting thermal radiation; the insulation layer is made of polyester, and the polyester layer is used as an insulation medium to effectively block heat conduction. The structure uses more polyester layers in the cold end area to reduce the impact of solid heat conduction on the low temperature area; the density of the aluminum foil layer is increased in the hot end area to enhance the resistance to radiation heat transfer. Through the design of this variable density distribution stacking structure combined with high vacuum, the rate of heat transfer from the outside to the inner tank is significantly reduced, and effective insulation protection of the internal cryogenic liquid is achieved.

[0035] like Figure 2 As shown, the optimization method provided in this embodiment includes the following steps:

[0036] Step S1, determining the design parameters of the variable density high vacuum multi-layer insulation structure, the design parameters including the reflectivity of the hot surface of the reflection screen, the reflectivity of the cold surface of the reflection screen, the thickness of each insulation layer, the thermal conductivity of the insulation layer, etc.

[0037] In this embodiment, a double-sided aluminum-plated reflective screen is selected as the reflective layer, and the reflectivity of the hot surface of the reflective screen is ε H =0.04, the reflectivity of the cold surface of the reflective screen is ε C =0.04. The insulation material is polyester, and the thickness of each insulation layer is L spacer =0.26247mm, thermal conductivity of the insulation layer λ spacer =0.15W / (m·K).

[0038] Step S2, determining performance indicators, and expressing each performance indicator in a functional manner based on the design parameters to obtain simulation functions of each performance indicator. Specifically, the performance indicators include reflective screen temperature, total heat flux density, thermal radiation heat flux density, gas heat conduction heat flux density, solid heat conduction heat flux density, and thermal resistance of each layer of the insulation structure.

[0039] (1) The total heat flux density of the variable density high vacuum multi-layer insulation structure is modeled, and the simulation function is as follows:

[0040] q i,total =q i,rad +q i,g_cond +q i,s_cond

[0041] Among them, q i,total is the total heat flux density of the i-th layer of the variable density high vacuum multi-layer insulation structure, q i,rad is the heat flux density of radiation heat transfer in the i-th layer of the variable density high vacuum multi-layer insulation structure, q i,g_cond is the heat flux density of the gas in the i-th layer of the variable density high vacuum multi-layer insulation structure, q i,s_condis the solid thermal conduction heat flux density of the i-th layer of the variable density high vacuum multi-layer insulation structure.

[0042] (2) The thermal radiation heat flux density of the variable density high vacuum multi-layer insulation structure is modeled, and the simulation function is as follows:

[0043] q i,rad =K i,rad (T i,H -T i,C )

[0044] Where K i,rad is the radiation heat transfer coefficient of the i-th layer of the variable density high vacuum multi-layer insulation structure, T i,H is the temperature of the i-th heat reflection screen of the variable density high vacuum multi-layer insulation structure, T i,C is the temperature of the i-th cold reflection screen of the variable density high vacuum multi-layer insulation structure.

[0045]

[0046] Where σ is the Stefan-Boltzmann constant, σ = 5.67 × 10 -8 W / (σ 2 ·K 4 ).

[0047] (3) The gas heat conduction heat flux density of the variable density high vacuum multi-layer insulation structure is modeled, and the simulation function is as follows:

[0048] q i,g_cond =K i,g_cond (T i.H -T i.C )

[0049] Where K i,g_cond is the thermal conductivity coefficient of the i-th layer of gas in the variable density high vacuum multi-layer insulation structure.

[0050] K i,rad =C1Pα

[0051] Wherein P is the residual gas pressure in the heat insulation layer, and in this embodiment, P is set to 0.001 Pa, and a is the accommodation coefficient, and for air, α=0.9.

[0052]

[0053] Where R is the ideal gas constant, R = 8.314 J / (mol·K), M is the molecular mass of the residual gas in the insulation layer (g / mol), and T is the room temperature of the vacuum interlayer where the residual gas in the insulation layer is located.

[0054] (4) The solid thermal conductivity heat flux density of the variable density high vacuum multi-layer insulation structure is modeled, and the simulation function is as follows:

[0055] q i,s_cond =K i,s_cond (T i,H -T i,C )

[0056] Where K i,s_cond is the thermal conductivity coefficient of the i-th layer of gas in the variable density high vacuum multi-layer insulation structure.

[0057] K i,s_cond =C2fλ spacer / D i

[0058] Where C2 is an empirical constant, which is 0.008 for polyester material, and f is the relative density of the insulation layer material, which is 0.02 for polyester material.

[0059] D i =N i,spacer ×L spacer

[0060] Where D i is the total insulation thickness of the i-th layer of the variable density high vacuum multi-layer insulation structure, N i,spacer is the number of the i-th insulation layer of the variable density high vacuum multi-layer insulation structure.

[0061] (5) The total thermal resistance and temperature of each layer of the variable density high vacuum multi-layer insulation structure are modeled, and the simulation function is as follows:

[0062] K i.total =K i.rad +K i,g_cobd +K i,s_cond

[0063] Where K i.total is the total heat transfer coefficient of the i-th layer of the variable density high vacuum multi-layer insulation structure.

[0064]

[0065] Where R i.total is the total thermal resistance of the i-th layer of the variable density high vacuum multi-layer insulation structure.

[0066]

[0067] Where T n is the temperature of the nth layer reflection screen of the variable density high vacuum multi-layer insulation structure, T C is the temperature of the cold end reflective screen of the variable density high vacuum multi-layer insulation structure, T H The temperature of the hot end reflective screen of the variable density high vacuum multi-layer insulation structure is is the sum of the thermal resistances from the first layer to the nth layer of the variable density high vacuum multilayer insulation structure, is the total thermal resistance of the variable density high vacuum multi-layer insulation structure.

[0068] Step S3, based on the simulation function of each performance index, establish the optimal arrangement problem of the variable density high vacuum multi-layer thermal insulation structure, use the genetic algorithm to solve the optimal arrangement problem, and realize the optimization of the variable density high vacuum multi-layer thermal insulation structure. When using the genetic algorithm to solve the optimal arrangement problem, generate population individuals based on the number of insulation layers between each reflection screen of the variable density high vacuum multi-layer thermal insulation structure, and use the heat flux density function of the variable density high vacuum multi-layer thermal insulation structure as the fitness function.

[0069] The step S3 specifically includes:

[0070] Step S301, initializing the real number coding of the number of thermal insulation layers of each layer of the variable-density high-vacuum multi-layer thermal insulation structure, digitizing it, and establishing a digital population.

[0071] The number of reflective screens is selected as N+1, and the total number of heat insulation layers is M. M layers of heat insulation layers are randomly distributed between N+1 layers of reflective screens, and N is used as the number of genes on the chromosome of the genetic algorithm to generate a population. In this embodiment, in order to ensure high vacuum properties of the heat insulation layer, the number of heat insulation layers between the reflective screens is ensured to be 6 at most and 1 at least.

[0072] In this embodiment, in order to increase the genetic diversity of the population, 100 population individuals are generated according to the above rules, and the initialization matrix is ​​obtained:

[0073]

[0074] where n 1,1 It indicates the number of insulation layers between the first and second reflective screens counted from the cold end by the first individual in the population, n 1,2 Indicates the number of insulation layers between the second and third reflective screens of the first individual in the population, n 1,N Indicates the number of insulation layers between the Nth reflective screen and the N+1th reflective screen of the first individual in the population, n 2,1 Indicates the number of insulation layers between the first and second reflective screens of the second individual in the population, n 2,2 Indicates the number of insulation layers between the second and third reflective screens of the second individual in the population, n 2,N Indicates the number of insulation layers between the Nth reflective screen and the N+1th reflective screen of the second individual in the population, n 100,1 Indicates the number of insulation layers between the first and second reflective screens of the 100th individual in the population, n 100,2 Indicates the number of insulation layers between the second and third reflective screens of the 100th individual in the population, n 100,NIt indicates the number of insulation layers between the Nth reflective screen and the N+1th reflective screen of the 100th individual in the population.

[0075] Step S302: using the heat flux density function of the variable-density high-vacuum multi-layer insulation structure as a fitness function to judge the fitness of individuals in the population.

[0076] First, the temperature of each layer is iteratively calculated through a mathematical model to ensure that the temperature difference before and after the iteration of each reflective screen is less than 1×10 -6 .

[0077] The expression of fitness is:

[0078]

[0079] in The fitness of the kth individual in the i-th iteration, is the heat flux density of the k-th individual variable-density high-vacuum multilayer insulation structure in the i-th iteration, is the total thermal resistance of the kth individual variable-density high-vacuum multilayer insulation structure in the i-th iteration.

[0080] Step S303: Perform genetic operations through mutation, crossover, elite preservation, etc.

[0081] During the mutation process, a random number is generated and compared with the set adaptive mutation probability. If the random number is less than the adaptive mutation probability, the current individual is taken as a mutant individual, and multiple gene bits in the chromosome of the mutant individual are flipped or replaced with new gene values. The expression of the adaptive mutation probability is:

[0082]

[0083] Among them, P M is the adaptive mutation probability, f s is the fitness of the mutant individual in the previous iteration, f a is the average fitness of the population in the previous iteration, f min is the minimum fitness of the population in the previous iteration, and P1 is the basic probability of mutation.

[0084] During the crossover process, a random number is generated and compared with the set adaptive crossover probability. If the random number is less than the adaptive crossover probability, a crossover point is randomly selected on the chromosome of the parent individual, and the chromosomes of the two parent individuals are exchanged at these crossover points to form new offspring individuals. The expression of the adaptive crossover probability is:

[0085]

[0086] Among them, PC is the adaptive crossover probability, and P2 is the basic crossover probability.

[0087] In the elite retention process, each iteration selects from the population the fitness of the population that is higher than the preset threshold f. th The individual is copied as the optimal individual in this iteration stage and retained as an elite individual. The expression of the preset threshold is:

[0088] f th =(f max -f best )×0.8+f best

[0089] Among them, f max is the maximum fitness in this iteration, f best is the optimal fitness among all iterations.

[0090] Step S304: terminate the genetic operation and integrate the genetic operation results to obtain the optimal structure.

[0091] When the iterative generation of genetic operation reaches or exceeds the preset threshold, the algorithm will automatically terminate to avoid unnecessary waste of computing resources. In the last generation of offspring at this time, the fitness function of each individual structure is strictly evaluated to select the best performing individual as the final optimization result generated by the genetic operation. This optimal offspring reflects the best combination configuration of the variable density high vacuum multilayer insulation structure under the design constraints and performance objectives.

[0092] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0093] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0094] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing a variable density high vacuum multi-layer insulation structure, characterized in that: The following steps are involved: Determine the design parameters and performance indicators of the variable density high vacuum multi-layer insulation structure, and express each of the performance indicators in a functional manner based on the design parameters to obtain a simulation function of each performance indicator; Based on the simulation function of each performance index, an optimal arrangement problem of a variable-density high-vacuum multi-layer thermal insulation structure is established, and a genetic algorithm is used to solve the optimal arrangement problem to achieve optimization of the variable-density high-vacuum multi-layer thermal insulation structure; When the genetic algorithm is used to solve the optimal arrangement problem, population individuals are generated based on the number of insulation layers between each reflective screen of the variable density high vacuum multi-layer insulation structure, and the heat flux density function of the variable density high vacuum multi-layer insulation structure is used as the fitness function.

2. The optimization method of the variable density high vacuum multi-layer insulation structure according to claim 1, characterized in that: The design parameters include the reflectivity of the hot surface of the reflection screen, the reflectivity of the cold surface of the reflection screen, the thickness of each insulation layer and the thermal conductivity of the insulation layer.

3. The optimization method of the variable density high vacuum multi-layer insulation structure according to claim 1, characterized in that: The performance indicators include the reflection screen temperature, the total heat flux density, the thermal radiation heat flux density, the gas heat conduction heat flux density, the solid heat conduction heat flux density and the thermal resistance of each layer of the insulation structure.

4. The optimization method of the variable density high vacuum multi-layer insulation structure according to claim 1, characterized in that: In the genetic algorithm, M layers of heat insulation are randomly distributed between N+1 layers of reflection screens, and N is used as the number of genes of the genetic algorithm chromosome to generate an initial population.

5. The optimization method of the variable density high vacuum multi-layer insulation structure according to claim 1, characterized in that: In the genetic algorithm, the fitness function is expressed as: in, The fitness of the kth individual in the i-th iteration, is the heat flux density of the k-th individual variable-density high-vacuum multilayer insulation structure in the i-th iteration, is the total thermal resistance of the kth individual variable density high vacuum multilayer insulation structure in the i-th iteration, T H is the temperature of the hot end reflective screen of the variable density high vacuum multi-layer insulation structure, T C It is the temperature of the cold end reflection screen of the variable density high vacuum multi-layer insulation structure.

6. The optimization method of the variable density high vacuum multi-layer insulation structure according to claim 1, characterized in that: In the genetic algorithm, the optimization of the variable-density high-vacuum multi-layer thermal insulation structure is achieved through mutation, crossover and elite retention processing.

7. The optimization method of the variable density high vacuum multi-layer insulation structure according to claim 6, characterized in that: During the mutation process, a random number is generated and compared with the set adaptive mutation probability. If the random number is less than the adaptive mutation probability, the current individual is taken as a mutant individual, and multiple gene bits in the chromosome of the mutant individual are flipped or replaced with new gene values. The expression of the adaptive mutation probability is: Among them, P M is the adaptive mutation probability, f s is the fitness of the mutant individual in the previous iteration, f a is the average fitness of the population in the previous iteration, f min is the minimum fitness of the population in the previous iteration, and P1 is the basic probability of mutation.

8. The optimization method of the variable density high vacuum multi-layer insulation structure according to claim 6, characterized in that: During the crossover process, a random number is generated and compared with the set adaptive crossover probability. If the random number is less than the adaptive crossover probability, a crossover point is randomly selected on the chromosome of the parent individual, and the chromosomes of the two parent individuals are exchanged at these crossover points to form a new offspring individual. The expression of the adaptive crossover probability is: Among them, P C is the adaptive crossover probability, f s is the fitness of the mutant individual in the previous iteration, f a is the average fitness of the population in the previous iteration, f min is the minimum fitness of the population in the previous iteration, and P2 is the crossover basic probability.

9. The optimization method of the variable density high vacuum multi-layer insulation structure according to claim 6, characterized in that: In the elite retention process, each iteration selects from the population the fitness of the population that is higher than the preset threshold f. th The individual is copied as the optimal individual in this iteration stage and retained as an elite individual. The expression of the preset threshold is: f th =(f max -f best )×0.8+f best Among them, f max is the maximum fitness in this iteration, f best is the optimal fitness among all iterations.

10. A variable density high vacuum multi-layer insulation structure, characterized in that: The method is obtained by using the optimization method described in any one of claims 1 to 9.

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