Multi-layer heat-conducting wave-absorbing material design method based on genetic algorithm

Abstract

The invention discloses a multilayer heat-conducting wave-absorbing material design method based on a genetic algorithm. The method comprises the following steps: establishing a powder parameter database; the design of the multi-layer wave-absorbing material is completed based on a genetic algorithm, specifically, electromagnetic parameters and heat conductivity coefficients are calculated, the fitness of the material is evaluated, and a better design is generated through iteration by adopting crossover, variation and elite selection strategies; the formula meeting the expected heat-conducting and wave-absorbing performance can be output according to the designed method. According to the method, by introducing calculation of the heat conductivity coefficient and the equivalent medium theory, the sorting of the gaskets on all layers, the selection materials of all layers and the proportion of the filling materials of all layers can be adjusted, and the limitation that the traditional design of the multi-layer wave-absorbing material only depends on thickness adjustment is broken through. Compared with a traditional design method of the multi-layer heat-conducting wave-absorbing material, the design scheme meeting the requirements can be obtained without repeated tests, the consumed time is short, the cost is low, and the accuracy rate is high.

Description

Technical Field

[0001] The invention belongs to the design of heat-conducting and wave-absorbing materials, and in particular relates to a design method of a multilayer heat-conducting and wave-absorbing material. Background Art

[0002] With the widespread application of electronic devices in daily life, the demand for anti-electromagnetic interference and heat dissipation continues to increase. Gaskets prepared by composite thermal conductive materials and absorbing materials can meet the needs of thermal conductivity and wave absorption at the same time, thereby effectively solving the challenges of electronic devices in anti-electromagnetic interference and heat dissipation. Traditional single-layer thermal conductive wave absorbing gaskets have problems such as single absorption peak and narrow wave absorption frequency band, which are difficult to meet the current broadband and thin wave absorption performance requirements. In order to overcome these problems, researchers began to explore the use of multi-layer thermal conductive wave absorbing gaskets. By reasonably designing the structure and materials between each layer, especially achieving impedance matching between layers, the wave absorption frequency band can be effectively expanded, thereby improving the broadband wave absorption performance of the gasket. However, multi-layer gaskets have certain challenges in practical applications. First, there is a thermal resistance interface between different layers of the multi-layer gasket. This thermal resistance will affect the overall thermal conductivity of the gasket, resulting in uneven heat conduction between different layers. Secondly, the thickness, arrangement order and material composition of each layer will have a significant impact on the thermal conductivity and wave absorption characteristics of the gasket. Therefore, adjusting the design of the multi-layer gasket only by experimental methods requires a lot of experimental verification, which is time-consuming and increases the cost of materials.

[0003] In response to this problem, existing studies such as CN111724870A, CN117954014A and CN113688566A have proposed design methods for multi-layer absorbing materials and optimized the absorbing performance by adjusting the thickness of each layer. However, these technologies mainly rely on the filling of single-component absorbing powders in each layer, which has relatively low thermal conductivity and limited controllability. The design of multi-layer absorbing materials focuses on the adjustment of thickness, but there are still major limitations in the coordination and optimization between thermal conductivity and absorbing performance, making it difficult to achieve efficient comprehensive performance.

[0004] Therefore, in order to better meet the needs of modern electronic devices for thermal conductivity and wave absorption performance, it is urgent to develop a design method for multi-layer thermal conductive wave absorbing materials, which can not only improve the comprehensive performance of multi-layer thermal conductive wave absorbing gaskets, but also reduce the high cost and long cycle problems faced in traditional experimental research. Through numerical simulation and computing technology, the optimal material combination and layer structure design can be quickly screened without a large number of experiments, greatly improving R&D efficiency, reducing costs, and promoting technological progress in this field. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for designing a multilayer heat-conducting and wave-absorbing material based on a genetic algorithm.

[0006] The technical solution of the present invention is: a design method of multilayer heat-conducting and wave-absorbing materials based on genetic algorithm, the calculation method comprises the following steps: Step 1: Establish a database, which contains electromagnetic parameters, thermal conductivity and specific gravity of powders, wherein the powders are one or more of carbonyl iron powder, sendustine powder, ferrite, iron powder, aluminum oxide, zinc oxide, aluminum nitride, boron nitride, aluminum hydroxide and magnesium hydroxide; the powders are spherical in shape; and the powder particle size D50 is 1-150 μm; Step 2: Complete the design of multi-layer absorbing materials based on genetic algorithm. The specific process includes: ① Setting target parameters of the multilayer absorbing material, including the number of powders filled, the filling ratio of powders, the target frequency band, the minimum reflection loss, the number of layers, the thermal conductivity, the single layer thickness accuracy and the genetic parameters, wherein the genetic parameters include the evolution termination generation, the crossover probability and the mutation probability value; ② Calculate the total length of the binary code of each individual according to the target parameters to generate an initial population; ③Decode each individual of the initial population to obtain the powder type, powder filling ratio and thickness; ④ Calculate the electromagnetic parameters of each layer based on the equivalent medium theory and electromagnetic parameters, and calculate the thermal conductivity of each layer according to the following formula; ⑤ Calculate the input impedance and reflectivity of each layer based on the multilayer transmission line theory, and calculate the thermal conductivity of the multilayer material in combination with the thermal conductivity; ⑥ Calculate the fitness value of each individual based on the target frequency band, minimum reflection loss and thermal conductivity; ⑦ If there are individuals whose fitness meets the set target, the formula is output. Otherwise, the elite selection strategy is adopted, that is, the individual with the largest fitness value in the previous generation is used to replace the individual with the smallest fitness value in the current population, and then a random selection without replacement is performed based on the fitness value of each individual to form a new population 1; ⑧ Based on the set crossover probability, a crossover operation is performed on the new population 1, and the individuals of the new population 1 and the new individuals obtained by the crossover operation form a population 2; ⑨Based on the set mutation probability value, the new population 2 is mutated, and the individuals of the new population 2 and the new individuals obtained by the mutation operation form population 3; ⑩ Use the elite selection strategy to update the population of new population 3, and select individuals with larger fitness values ​​to form the initial population of the next generation; Repeat steps 2 to 11 until the set target value is met or the evolution terminates, and output the final design result.

[0007] The calculation process of the total length of the binary code of each individual is as follows: the filling ratio, thickness and code length of each layer of material of the powder are calculated to obtain the number of binary bits required for the code; wherein the number of code bits of the filling ratio and thickness of the powder is calculated according to the preset number of decimal places and upper and lower limits; the total length of the binary code is calculated as follows: a=[(n1*a1)+a2]*n2 Among them, a1 represents the number of binary digits required for the filling ratio of the coded material in the current number of layers, a2 represents the number of binary digits required for the thickness, n1 represents the type of database material, n2 represents the number of layers of the multi-layer gasket, n3 represents the number of decimal places reserved for the filling ratio, and d max With d min They represent the upper and lower limits of the thickness of each layer of material, n4 represents the number of decimal places retained for the thickness of a single layer, and a represents the total length of each individual binary code.

[0008] The fitness value is calculated by: according to the reflectivity and thermal conductivity of the multi-layer material, the reflectivity and thermal conductivity of each layer of material are calculated by the infinitesimal summation method, and the fitness value is calculated based on this, and the fitness value is optimized according to the target thermal conductivity and the reflection loss coverage area; the fitness value calculation formula is: f = a k *S1 / S2 Among them, k c represents the thermal conductivity of the multilayer gasket, k represents the target thermal conductivity, and a k is the transfer coefficient, S2 represents the target parameter minimum loss coverage area, and S1 represents the occupied range in the target parameter minimum loss coverage area.

[0009] The elite selection strategy includes: selecting the population according to the fitness value of each individual, selecting the individual with the largest fitness value to replace the individual with the smallest fitness value, and forming a new population by random selection without replacement.

[0010] The crossover operation adopts a probability-based uniform crossover method, and the mutation operation adopts a probability-based basic bit mutation method.

[0011] The calculation method and formula of the electromagnetic parameters are as follows: V j =m j / (m1 / ρ1+m2 / ρ2+...+m a / ρ a +m p / ρ p ) Among them, ε j 、v j represents the dielectric constant and volume fraction of the jth filler particle in the composite system, ε m is the dielectric constant of the matrix resin, ε=μ; m1, m2, ..., m a are the masses of the filling particles, ρ1, ρ2, ..., ρ a are the specific gravity of the filling particles, m p is the mass of the resin matrix, ρ p is the specific gravity of the resin matrix.

[0012] The calculation method and formula of the thermal conductivity are as follows: X j =m j / ρ j (D j ) 3 / (m1 / ρ1(D1) 3 +m2 / ρ2(D2) 3 +...m j / ρ j (D j ) 3 ...+m a / ρ a (D a ) 3 ) log k c =V*(X1C1logk1+X2C2logk2+...+X a C a logk a )+(1-V)log(Ck p ) Among them, k c is the thermal conductivity of the gasket, k1, k2, ..., k a are the thermal conductivity of the filling particles, k p is the thermal conductivity of the matrix, V is the volume fraction of the filling particles, C1, C2, ..., C a is the coefficient of the difficulty of the filler conductive chain formation, C is the coefficient that affects the polymer crystallinity and crystal size, X1, X2, ..., X a is the percentage of the number of particles (powder) in the filling particles; m1, m2, ..., m a are the masses of the filling particles, ρ1, ρ2, ..., ρ a are the specific gravity of the filling particles, D1, D2, ..., D a are the particle sizes of the filling particles; The calculation formula and method for the coefficient of the difficulty of forming the conductive chain of the filler and the coefficient of the crystallinity and crystal size of the polymer are as follows: limit the filling particles to one kind, make two gaskets with different filling fractions of the same matrix resin, test their thermal conductivity, and substitute the measured thermal conductivity and the calculated volume fraction into logk c =V*(C1*logk1)+(1-V)*log(C*k p ) formula, we get two linear equations of two variables. Solving the equations can give the difficulty coefficient of the formation of the filler conductive chain and the coefficient that affects the crystallinity and crystal size of the polymer.

[0013] The calculation formula of the reflectivity is: Among them, z in (k) represents the input impedance of the k-th layer pad, z c (k) represents the intrinsic impedance of the kth layer of gasket, r(k) represents the transmission constant of each layer, ε k , μ k represents the dielectric constant and magnetic permeability of the k-th layer of gasket, d(k) represents the thickness of the k-th layer of gasket; R represents the reflectivity of the multi-layer gasket.

[0014] The calculation formula of the thermal conductivity is: where k c Denotes the thermal conductivity of the multilayer gasket, h c Indicates the thickness of the multilayer gasket, k j 、h j Represents the thermal conductivity and thickness of the j-th layer of gasket.

[0015] In step 2⑦, random selection without replacement is performed according to the fitness value of each individual to form a new population. The process is as follows: after normalizing the fitness value of each individual, the expected value corresponding to each individual is calculated; the new population individuals are selected according to the integer part of the expected value, and the remaining individuals of the population are determined by probability-based selection according to the roulette selection strategy.

[0016] In step 2⑧, a uniform crossover method based on probability and random pairing is used to cross the individuals in the new population; and a basic position mutation based on probability is used to mutate the individuals in the new population.

[0017] The technical advantages of the present invention are as follows: (1) The present invention uses computer simulation calculations, equivalent medium theory and thermal conductivity calculation formulas to theoretically accurately design a multilayer gasket that meets the requirements. The thermal conductivity and reflectivity obtained through simulation calculations avoid the tedious process of repeated experiments, greatly improve research and development efficiency, shorten the product development cycle, and reduce experimental costs. (2) The present invention adopts a design method that combines computer simulation with theoretical analysis. Researchers can flexibly adjust the structure, material composition and other design parameters of each layer according to the needs of different application scenarios to achieve precise control of the performance of the multilayer gasket. Compared with the traditional method of material optimization that relies on experimental means, the present invention can more flexibly respond to the complex requirements of different applications for thermal conductivity and wave absorption performance. (3) The present invention breaks the limitation of traditional design that relies only on thickness adjustment by expanding the adjustable variables. In addition to adjusting the reflectivity and thermal conductivity by the thickness of the gasket, other variables (such as material selection, arrangement order of each layer of material, etc.) can also be adjusted to ensure thermal conductivity while optimizing the absorbing performance. This design method greatly improves the comprehensive performance of the material and is conducive to meeting the needs of modern electronic devices for broadband thin absorbing gaskets. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of the method for designing the present invention.

[0019] Figure 2 This is a curve diagram of wave absorption characteristics of specific embodiment 1. DETAILED DESCRIPTION

[0020] In order to better understand the invention, the content of the patent of the present invention is further explained below in conjunction with the implementation examples. However, the content of the patent of the present invention is not limited to the following implementation examples. Based on the embodiments of the present invention, other embodiments obtained by technicians in this field without creative work all fall within the scope of protection of the present invention.

[0021] The operating method of the present invention for obtaining the formula of the multi-layer heat-conducting and wave-absorbing gasket is as follows: (1) Input the target thermal conductivity and absorption data of the gasket, and the number of gasket layers; (2) Set the thickness of each layer of gasket, the thickness range is 0-20mm; (3) Call database data to set the type and particle size of filler; (4) Input the thermal conductivity and specific gravity of the resin matrix; (5) Generate an initial population (an array consisting of 0s and 1s); (6) Decoding the initial population to obtain the thickness and formula of each layer of gasket; (7) According to step 2 of the invention, steps ④, ⑤, and ⑥ automatically calculate the fitness of each individual; (8) If there are individuals whose fitness meets the set target, the formula is output, otherwise the individuals with larger fitness are selected based on probability according to the roulette wheel selection strategy to form population 1; (9) Based on the set crossover probability, a crossover operation is performed on the new population 1, and the individuals of the new population 1 and the new individuals obtained by the crossover operation form a population 2; (10) Based on the set mutation probability value, a mutation operation is performed on the new population 2, and the individuals of the new population 2 and the new individuals obtained by the mutation operation form a population 3; (11) Use the elite selection strategy to update the population of new population 3, and select individuals with larger fitness values ​​to form the initial population of the next generation; repeat steps (6)-(11) (12) Output the powder formula that is closest to the set target.

[0022] The thermal conductive and wave absorbing materials are artificially prepared with the formula that meets the set target and tested to verify whether it achieves the expected set target performance.

[0023] The method for artificially preparing thermal conductive and wave absorbing materials is as follows: The output heat-conducting and wave-absorbing formula (except vinyl silicone oil) that meets the set target is preliminarily dispersed with 100g of mixed silicone oil, and then dispersed evenly using a vacuum mixer for 1.0h; the resulting paste is placed on a release film, calendered according to the set thickness, and vulcanized at 150°C for 15min to obtain each layer of heat-conducting and wave-absorbing gaskets. The mixed silicone oil system is 100 parts of 500cP vinyl silicone oil + 4.0 parts of side hydrogenated silicone oil + 0.33 parts of acetylene cyclohexanol inhibitor + 0.33 parts of platinum catalyst.

[0024] The performance test method of artificially prepared thermal conductive and wave absorbing materials is as follows: Wave absorption performance test: The coaxial method of the vector network analyzer is used to test its wave absorption performance, and its dielectric constant and magnetic permeability are tested to calculate its reflection loss (RL). The formula is as follows. The equipment model used is N5222B. Thermal conductivity test: stack the gaskets in order and test the thermal conductivity of the samples according to the ASTM D5470 method.

[0025] Example 1 The method for obtaining the formula of the multi-layer heat-conducting and wave-absorbing organic silicon gasket by using the present invention is as follows: (1) Input gasket target thermal conductivity 1.9-2.1W / (m*k), reflectivity <-10dB in 6-10GHz; (2) Set the thickness of each gasket layer to 0-1 mm and the number of gasket layers to 3; (3) Calling the database data, selecting fillers, 1 μm spherical zinc oxide, 5 μm spherical carbonyl iron powder, and 14 μm spherical iron silicon aluminum powder; (4) Input resin matrix vinyl silicone oil with thermal conductivity of 0.15-0.20 W / (m*k) and specific gravity of 1.005; (5) Generate an initial population (an array consisting of 0s and 1s); (6) Decoding the initial population to obtain the thickness and formula of each layer of gasket; (7) According to step 2 of the invention, steps ④, ⑤, and ⑥ automatically calculate the fitness of each individual; (8) If there are individuals whose fitness meets the set target, the formula is output, otherwise the individuals with larger fitness are selected based on probability according to the roulette wheel selection strategy to form population 1; (9) Based on the set crossover probability, a crossover operation is performed on the new population 1, and the individuals of the new population 1 and the new individuals obtained by the crossover operation form a population 2; (10) Based on the set mutation probability value, a mutation operation is performed on the new population 2, and the individuals of the new population 2 and the new individuals obtained by the mutation operation form a population 3; (11) Use the elite selection strategy to update the population of the new population 3, and select individuals with larger fitness values ​​to form the initial population of the next generation; repeat steps (6)-(11); (12) The output results of Example 1 are as follows: The output results are shown in Table 1 (mass ratio): Table 1:

[0026] According to Table 1, the test results of preparing the organic silicone thermal conductive wave absorbing gasket with the formula that meets the set target output by running the program of Example 1 are compared with the expected set target performance as shown in Table 2. Table 2:

Claims

1. A method for designing a multilayer thermal conductive and wave absorbing material based on a genetic algorithm, characterized in that: The method comprises the following steps: Step 1: Establish a database, which contains electromagnetic parameters, thermal conductivity and specific gravity of powders, wherein the powders are one or more of carbonyl iron powder, sendustine powder, ferrite, iron powder, aluminum oxide, zinc oxide, aluminum nitride, boron nitride, aluminum hydroxide and magnesium hydroxide; the powders are spherical in shape; and the powder particle size D50 is 1-150 μm; Step 2: Complete the design of multi-layer absorbing materials based on genetic algorithm. The specific process includes: ① Setting target parameters of the multilayer absorbing material, including the number of powders filled, the filling ratio of powders, the target frequency band, the minimum reflection loss, the number of layers, the thermal conductivity, the single layer thickness accuracy and the genetic parameters, wherein the genetic parameters include the evolution termination generation, the crossover probability and the mutation probability value; ② Calculate the total length of the binary code of each individual according to the target parameters to generate an initial population; ③Decode each individual of the initial population to obtain the powder type, powder filling ratio and thickness; ④ Calculate the electromagnetic parameters of each layer based on the equivalent medium theory and electromagnetic parameters, and calculate the thermal conductivity of each layer according to the following formula; ⑤ Calculate the input impedance and reflectivity of each layer based on the multilayer transmission line theory, and calculate the thermal conductivity of the multilayer material in combination with the thermal conductivity; ⑥ Calculate the fitness value of each individual based on the target frequency band, minimum reflection loss and thermal conductivity; ⑦ If there are individuals whose fitness meets the set target, the formula is output. Otherwise, the elite selection strategy is adopted, that is, the individual with the largest fitness value in the previous generation is used to replace the individual with the smallest fitness value in the current population, and then a random selection without replacement is performed based on the fitness value of each individual to form a new population 1; ⑧ Based on the set crossover probability, a crossover operation is performed on the new population 1, and the individuals of the new population 1 and the new individuals obtained by the crossover operation form a population 2; ⑨Based on the set mutation probability value, the new population 2 is mutated, and the individuals of the new population 2 and the new individuals obtained by the mutation operation form population 3; ⑩ Use the elite selection strategy to update the population of the new population 3, and select individuals with larger fitness values ​​to form the initial population of the next generation; Repeat steps 2 to 11 until the set target value is met or the evolution terminates, and output the final design result.

2. The method according to claim 1, characterized in that The calculation process of the total length of the binary code of each individual is as follows: the filling ratio, thickness and code length of each layer of material of the powder are calculated to obtain the number of binary bits required for the code; wherein the number of code bits of the filling ratio and thickness of the powder is calculated according to the preset number of decimal places and upper and lower limits; the total length of the binary code is calculated as follows: a=[(n1*a1)+a2]*n2 Among them, a1 represents the number of binary digits required for the filling ratio of the coded material in the current number of layers, a2 represents the number of binary digits required for the thickness, n1 represents the type of database material, n2 represents the number of layers of the multi-layer gasket, and n3 represents the number of decimal places reserved for the filling ratio. max With d min They represent the upper and lower limits of the thickness of each layer of material, n4 represents the number of decimal places retained for the thickness of a single layer, and a represents the total length of each individual binary code.

3. The method according to claim 1, characterized in that The fitness value is calculated by: according to the reflectivity and thermal conductivity of the multi-layer material, the reflectivity and thermal conductivity of each layer of material are calculated by the infinitesimal summation method, and the fitness value is calculated based on this, and the fitness value is optimized according to the target thermal conductivity and the reflection loss coverage area; the fitness value calculation formula is: f=a k *S1 / S2 Among them, k c represents the thermal conductivity of the multilayer gasket, k represents the target thermal conductivity, and a k is the transfer coefficient, S2 represents the target parameter minimum loss coverage area, and S1 represents the occupied range in the target parameter minimum loss coverage area.

4. The method according to claim 1, characterized in that The elite selection strategy includes: selecting the population according to the fitness value of each individual, selecting the individual with the largest fitness value to replace the individual with the smallest fitness value, and forming a new population by random selection without replacement.

5. The method according to claim 1, characterized in that The crossover operation adopts a probability-based uniform crossover method, and the mutation operation adopts a probability-based basic bit mutation method.

6. The method according to claim 1, characterized in that The calculation method and formula of the electromagnetic parameters are as follows: V j =m j / (m1 / ρ1+m2 / ρ2+...+m a / r a +m p / r p ) Among them, ε j 、v j represents the dielectric constant and volume fraction of the jth filler particle in the composite system, ε m is the dielectric constant of the matrix resin, ε=μ; m1, m2, ..., m a are the masses of the filling particles, ρ1, ρ2, ..., ρ a are the specific gravity of the filling particles, m p is the mass of the resin matrix, ρ p is the specific gravity of the resin matrix.

7. The method according to claim 1, characterized in that The calculation method and formula of the thermal conductivity are as follows: X j =m j / ρ j (D j ) 3 / (m1 / ρ1(D1) 3 +m2 / ρ2(D2) 3 +...m j / ρ j (D j ) 3 ...+m a / ρ a (D a ) 3 ) logk c =V*(X1C1logk1+X2C2logk2+...+X a C a logk a )+(1-V)log(Ck p ) Among them, k c is the thermal conductivity of the gasket, k1, k2, ..., k a are the thermal conductivity of the filling particles, k p is the thermal conductivity of the matrix, V is the volume fraction of the filling particles, C1, C2, ..., C a is the coefficient of the difficulty of the filler conductive chain formation, C is the coefficient that affects the polymer crystallinity and crystal size, X1, X2, ..., X a is the percentage of the number of particles (powder) in the filling particles; m1, m2, ..., m a are the masses of the filling particles, ρ1, ρ2, ..., ρ a are the specific gravity of the filling particles, D1, D2, ..., D a are the particle sizes of the filling particles; The calculation formula and method for the coefficient of the difficulty of forming the conductive chain of the filler and the coefficient of the crystallinity and crystal size of the polymer are as follows: limit the filling particles to one kind, make two gaskets with different filling fractions of the same matrix resin, test their thermal conductivity, and substitute the measured thermal conductivity and the calculated volume fraction into logk c =V*(C1*logk1)+(1-V)*log(C*k p ) formula, we get two linear equations of two variables. Solving the equations can give the difficulty coefficient of the formation of the filler conductive chain and the coefficient that affects the crystallinity and crystal size of the polymer.

8. The method according to claim 1, characterized in that The calculation formula of the reflectivity is: Among them, z in (k) represents the input impedance of the k-th layer pad, z c (k) represents the intrinsic impedance of the kth layer of gasket, r(k) represents the transmission constant of each layer, ε k , μ k represents the dielectric constant and magnetic permeability of the k-th layer of gasket, d(k) represents the thickness of the k-th layer of gasket; R represents the reflectivity of the multi-layer gasket.

9. The method according to claim 1, characterized in that: The calculation formula of the thermal conductivity is: Among them, k c Denotes the thermal conductivity of the multilayer gasket, h c Indicates the thickness of the multilayer gasket, k j 、h j Represents the thermal conductivity and thickness of the j-th layer of gasket.

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