Macroscopic thermal conductivity calculation method, calculation device thereof, storage medium and equipment

Abstract

The application discloses a macroscopic thermal conductivity calculation method and device, a storage medium and a computer device. The macroscopic thermal conductivity calculation method comprises the following steps: dividing a pre-constructed thermal interface material structure model into a predetermined number of unit cells, wherein the predetermined number of unit cells comprise a plurality of unit cells with different thermal conductivities; sequentially obtaining the thermal conductivity and position parameters of each unit cell; calculating the heat flow field and temperature gradient field of each unit cell according to the thermal conductivity and position parameters of each unit cell; and obtaining the macroscopic thermal conductivity of the thermal interface material structure model according to the heat flow field and temperature gradient field of all unit cells. By dividing the thermal interface material structure model into a plurality of unit cells, the properties of each unit cell are identified and the corresponding thermal conductivity is given, so that the heat flow field and temperature gradient field of each unit cell are calculated separately, and the calculation accuracy of the macroscopic thermal conductivity is improved.

Description

Technical Field

[0001] This invention belongs to the field of simulation technology of polymer structural material properties. Specifically, it relates to a method for calculating the macroscopic thermal conductivity of a thermal interface material structural model, a device for calculating macroscopic thermal conductivity, a computer-readable storage medium, and a computer device. Background Technology

[0002] The miniaturization and increasing power of electronic packaging structures have led to a corresponding increase in the heat generated by electronic products, severely impacting device performance and lifespan. An effective method to address device heat dissipation is to fill the space between the chip and the heat sink with a thermal interface material possessing high thermal conductivity and good compressibility to reduce contact thermal resistance. Adding spherical particles with high thermal conductivity to polymers is the most common choice for thermal interface materials. The rational selection of fillers and the adjustment of the filler-to-polymer matrix ratio are of great significance in both academic and industrial applications.

[0003] Given the high cost and long cycle of experimental research, much of the current research on the thermal conductivity of interface materials, both domestically and internationally, is based on computational simulation methods, which mainly fall into two categories. One is analytical and semi-empirical models, such as the Maxwell-Garnett model, Bruggman model, Russell model, and Lewis-Nielsen model. These models provide predicted values ​​of the material's macroscopic thermal conductivity as a function of filler volume fraction through theoretical derivation or empirical formulas. The other is numerical simulation algorithms, primarily the finite element method. This method uses discretization to homogenize and calculate the thermal conductivity of composite materials for specific microstructures.

[0004] However, analytical or semi-empirical models are generally only applicable to thermal interface materials with low filler volume fractions, and their performance predictions for materials with high filler density are inaccurate, exhibiting significant limitations. Furthermore, this method cannot specifically simulate the influence of microstructural parameters (such as particle size distribution, mixing of different particles, and contact thermal resistance between particles) on macroscopic thermal conductivity. Moreover, finite element-based numerical methods suffer from high model complexity and slow computational efficiency. Summary of the Invention

[0005] (I) The technical problem to be solved by the present invention

[0006] The technical problem solved by this invention is: how to identify the thermal conductivity of different regions so as to accurately simulate the macroscopic thermal conductivity of the entire material model.

[0007] (II) Technical Solution Adopted in this Invention

[0008] A method for calculating the macroscopic thermal conductivity of a thermal interface material structure model, the method comprising:

[0009] The pre-constructed thermal interface material structure model is divided into a predetermined number of unit cells, wherein the predetermined number of unit cells includes multiple types of unit cells with different thermal conductivity, and each unit cell has the same size.

[0010] The thermal conductivity and position parameters of each unit cell were obtained sequentially.

[0011] The thermal flow field and temperature gradient field of each unit cell are calculated based on the thermal conductivity and position parameters of each unit cell.

[0012] The macroscopic thermal conductivity of the thermal interface material structure model is obtained based on the thermal flow field and temperature gradient field of all unit cells.

[0013] Preferably, the method for obtaining the thermal conductivity of each unit cell includes:

[0014] Calculate the distance between the determination point of each unit cell and the center of each filling particle;

[0015] The material properties of each unit cell are determined based on the distance between the determination point of each unit cell and the center of each filling particle and the relationship between the radius of each filling particle.

[0016] The thermal conductivity of each unit cell is determined based on the material properties of each unit cell.

[0017] Preferably, the method for determining the material properties of each unit cell based on the distance between the determination point of each unit cell and the center of each filling particle and the relationship between the radii of each filling particle includes:

[0018] Determine whether the distance between the determination point of the unit cell and the center of each filling particle is less than or equal to the radius of the filling particle;

[0019] If so, then the material property of the unit cell is determined to be a particle property;

[0020] If not, then determine whether the difference between the distance between the unit cell and the filling particle and the radius of the filling particle is less than or equal to a predetermined value;

[0021] If so, then the unit cell is determined to be located within the interface region of the filling particles, and the material properties of the unit cell are determined to be interface layer properties.

[0022] If not, then the material properties of the unit cell are determined to be matrix properties.

[0023] Preferably, the method for calculating macroscopic thermal conductivity further includes:

[0024] Determine whether the unit cell is simultaneously located within the interface region of two adjacent filling particles;

[0025] If so, then the material properties of the unit cell are determined to be contact layer properties.

[0026] Preferably, the method for calculating the thermal flow field and temperature gradient field of each unit cell based on the thermal conductivity and position parameters of each unit cell includes:

[0027] Step 1: Calculate the temperature gradient Θ of the unit cell according to the following transformation relationship. i (x), where p i (x) represents the polarization field, k(x) represents the thermal conductivity of the unit cell, k0 is a constant, x is the position parameter of the unit cell, and i is the iteration number.

[0028] Θ i (x)=(k(x)-k0) -1 ·p i (x);

[0029] Step 2: Convert the temperature gradient Θ from Step 1 i (x) and polarization field p i (x) is converted to the temperature gradient Θ in Fourier space. i (ξ) and the polarization field p in Fourier space i (ξ);

[0030] Step 3: Calculate the thermal flow field q of the unit cell in Fourier space according to the following transformation relationship. i (ξ),

[0031] q i (ξ)=k0·Θ i (ξ)+p i (ξ);

[0032] Step 4: Obtain the temperature gradient Θ based on Step 2. i (ξ) and the heat flow field q obtained in step three i (ξ) The iteration error is calculated. When the iteration error is less than or equal to the threshold, the iteration calculation is stopped, and the temperature gradient Θ obtained in step two is used. i (ξ) and the heat flow field q obtained in step three i (ξ) represents the final temperature gradient and temperature gradient of the unit cell; if the iteration error is greater than the threshold, proceed to step five;

[0033] Step 5: Polarization field p in the Fourier space from Step 2 i (ξ) is updated, and the updated p is used as the result. i+1 (ξ) is converted into the polarization field p in physical space. i+1 (x), proceed to step one.

[0034] Preferably, the method for obtaining the macroscopic thermal conductivity of the thermal interface material structure model based on the heat flow field and temperature gradient field of all unit cells includes:

[0035] The average heat flow field is obtained by volume averaging the heat flow field of all unit cells, and the average temperature gradient field is obtained by volume averaging the temperature gradient field of all unit cells.

[0036] The ratio of the average heat flow field to the average temperature gradient field is used as the macroscopic thermal conductivity of the thermal interface material structure model.

[0037] Preferably, in step five, the polarization field p in the Fourier space is transformed according to the following relationship. i (ξ) is updated.

[0038] p i+1 (ξ)=p i (ξ)-αk0·Γ 0 (ξ)·q i (ξ)-βΣ 0 (ξ)·Θ i (ξ),

[0039]

[0040] Where α and β are constants.

[0041] This invention also discloses a macroscopic thermal conductivity calculation device for a thermal interface material structure model, the macroscopic thermal conductivity calculation device comprising:

[0042] The model segmentation module is used to divide a pre-built thermal interface material structure model into a predetermined number of unit cells, wherein the predetermined number of unit cells includes multiple types of unit cells with different thermal conductivity.

[0043] The input parameter acquisition module calculates the thermal flow field and temperature gradient field of each unit cell based on the thermal conductivity and position parameters of each unit cell.

[0044] The calculation module is used to calculate the heat flow field and temperature gradient field of each unit cell based on the thermal conductivity and position parameters of each unit cell; and

[0045] The macroscopic thermal conductivity is used to obtain the structural model of the thermal interface material based on the thermal flow field and temperature gradient field of all unit cells.

[0046] The present invention also discloses a computer-readable storage medium storing a macroscopic thermal conductivity calculation program for a thermal interface material structure model. When the macroscopic thermal conductivity calculation program for the thermal interface material structure model is executed by a processor, the above-mentioned macroscopic thermal conductivity calculation method for the thermal interface material structure model is implemented.

[0047] The present invention also discloses a computer device, the computer device including a computer-readable storage medium, a processor, and a macroscopic thermal conductivity calculation program for a thermal interface material structure model stored in the computer-readable storage medium, wherein the macroscopic thermal conductivity calculation program for the thermal interface material structure model is executed by the processor to implement the above-described method for calculating the macroscopic thermal conductivity of the thermal interface material structure model.

[0048] (III) Beneficial Effects

[0049] This invention discloses a method for calculating the macroscopic thermal conductivity of a thermal interface material structure model, which has the following technical advantages compared to traditional calculation methods:

[0050] (1) By dividing the thermal interface material structure model into multiple unit cells, identifying the properties of each unit cell and assigning corresponding thermal conductivity, the thermal flow field and temperature gradient field of each unit cell can be calculated separately, thereby improving the calculation accuracy of macroscopic thermal conductivity.

[0051] (2) The method of using Fourier transform to homogenize the macroscopic thermal properties of random dispersed structures is faster and requires less memory than general numerical calculation methods. It reduces the calculation cost and improves the calculation efficiency while ensuring accuracy.

[0052] (3) Compared with the analytical model, this application has a wider range of applications and can be used to analyze the influence of various microstructural differences on macroscopic thermal conductivity, such as the influence of dispersion degree, particle size distribution, mixed particles, interfacial thermal resistance and contact thermal resistance. At the same time, the calculation method of this application is applicable to the whole domain calculation from low to high particle filling volume fraction. Attached Figure Description

[0053] Figure 1 This is a flowchart of the macroscopic thermal conductivity calculation method for the thermal interface material structure model of Embodiment 1 of the present invention.

[0054] Figure 2 This is a flowchart illustrating the process of obtaining the thermal conductivity of each unit cell in Embodiment 1 of the present invention.

[0055] Figure 3 This is a schematic diagram showing the distribution of the filler particles in Embodiment 1 of the present invention;

[0056] Figure 4This is a flowchart of the method for calculating the thermal flow field and temperature gradient field of a unit cell in Embodiment 1 of the present invention.

[0057] Figure 5 This is an architectural diagram of the macroscopic thermal conductivity calculation device for the thermal interface material structure model of Embodiment 2 of the present invention;

[0058] Figure 6 This is a schematic diagram of a computer device according to Embodiment 4 of the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0060] Before describing the various embodiments of this application in detail, the inventive concept of this application is first briefly described: In the prior art, when calculating the macroscopic thermal conductivity of thermal interface materials, the thermal conductivity of different regions is not distinguished, resulting in a large error in the calculation results. This application divides the structural model of the thermal interface material into multiple unit cells, identifies the properties of each unit cell and assigns a corresponding thermal conductivity, thereby calculating the heat flow field and temperature gradient field of each unit cell separately, and finally calculates the overall macroscopic thermal conductivity. By considering the thermal conductivity of different regions, the thermal data of each region can be accurately calculated, which can ultimately improve the accuracy of macroscopic thermal conductivity calculation.

[0061] Example 1

[0062] Specifically, such as Figure 1 As shown, the method for calculating the macroscopic thermal conductivity of the thermal interface material structure model in this embodiment includes the following steps:

[0063] Step S10: Divide the pre-built thermal interface material structure model into a predetermined number of unit cells, wherein the predetermined number of unit cells includes multiple types of unit cells with different thermal conductivity, and each unit cell has the same size.

[0064] Step S20: Sequentially obtain the thermal conductivity and position parameters of each unit cell.

[0065] Step S30: Calculate the thermal flow field and temperature gradient field of each unit cell based on the thermal conductivity and position parameters of each unit cell.

[0066] Step S40: Obtain the macroscopic thermal conductivity of the thermal interface material structure model based on the thermal flow field and temperature gradient field of all unit cells.

[0067] Specifically, before performing macroscopic thermal conductivity calculations, it is necessary to construct a thermal interface material structure model, using two different construction methods depending on the different filling densities.

[0068] For low fill density: The Markov chain algorithm in the Monte Carlo method is used to randomly distribute the fill particles in representative cubic volume units with periodic boundary conditions. Specifically, all particles are first randomly and non-overlappingly placed into the representative volume units, and then a particle is randomly selected for movement. If the moved particle does not overlap with other particles, the movement is accepted, and the particle's position is updated; otherwise, the movement is rejected, and the particle remains in its original position. This process is repeated, and the position structure is recorded at regular intervals, ultimately obtaining a series of independent representative volume units. These representative volume units are the thermal interface material structure model of this embodiment. Since the number, size, and model volume are determined, models with different structures, i.e., representative volume units, are randomly generated. Accuracy is improved by averaging and calculating the standard deviation of the macroscopic thermal conductivity of multiple representative volume units. It should be noted that the subsequent calculation steps in this embodiment only describe calculations for one representative volume unit; the same processing is applied to other representative volume units.

[0069] For high filling density systems: In the Lammps (Large-scale Atomic / Molecular Massively Parallel Simulator) software, a thermal interface material structure model is established using molecular dynamics. The Lennard-Jones potential energy between particles is set to achieve particle dispersion. When the system energy is at its minimum, the particle positions are stable, and the coordinates of the particle center and the particle radius are output. Specifically, the particles are treated as hard spheres, and the Lennard-Jones potential energy is multiplied by a taper equation term f(r) to describe the interaction between particles.

[0070] Where, when r < r m When r is constant, f(r) = 1.0; when r is constant... m <r<r cut When f(r) = (1-x) 3 *(1+3x+6x 2 When r ≥ r cut When f(r) = 0.0, set a reasonable internal cutoff distance r. m and external cutoff distance r cutThe repulsion range between particles is defined by r, where r represents the distance between two adjacent particles. The particle positions are iteratively adjusted using the conjugate gradient method until each particle is in force equilibrium and the system energy reaches its minimum. As a preferred embodiment, the internal cutoff distance r... m for External cutoff distance r cut Let be the radius of the particle.

[0071] Specifically, in step S10, the constructed thermal interface material structure model is divided into multiple cubic unit cells. The thermal interface material structure model is a cubic model with a volume of L, and the unit cell is a cube with a side length of δx. The size of the unit cell is much smaller than the size of the filling particles. Taking a type of filling particle as an example, a unit cell at a certain location may be inside the particle, outside the particle, or at the boundary between adjacent particles. Therefore, the thermal conductivity of the unit cell can vary. By distinguishing unit cells with different thermal conductivities, the thermal conductivity can be calculated more accurately.

[0072] Furthermore, such as Figure 2 As shown, step S20, the method for obtaining the thermal conductivity of each unit cell includes the following steps:

[0073] Step S21: Calculate the distance between the determination point of each unit cell and the center of each filling particle.

[0074] Step S22: Determine the material properties of each unit cell based on the distance between the determination point of each unit cell and the center of each filling particle and the size relationship of the radius of each filling particle.

[0075] Step S23: Determine the thermal conductivity of each unit cell based on the material properties of each unit cell.

[0076] Specifically, in step S21, one vertex of the unit cell is used as the determination point of the unit cell for subsequent determination of the material properties of the unit cell. The spatial coordinates of the determination point and the center coordinates of each filling particle are obtained, and the distance between the determination point of the unit cell and the center of each filling particle is calculated based on the spatial coordinates of the determination point and the center of each filling particle.

[0077] In step S22, the material properties of each unit cell are determined by the relationship between the distance between the determination point of each unit cell and the center of each filling particle, and the radius of each filling particle. Specifically, as follows... Figure 3As shown, the distance R1 between the decision point P of the unit cell and the center O of the filling particle is calculated. It is then determined whether this distance R1 is less than or equal to the radius R2 of the filling particle. If the distance R1 is less than or equal to the radius R2, the decision point of the unit cell is located within the internal region of the filling particle. In this case, the material property of the unit cell is determined as a particle property, and the thermal conductivity of the filling particle is used as the thermal conductivity of the unit cell. If the distance R1 is greater than the radius R2, it is further determined whether the difference between the distance R1 and the radius R2 is less than or equal to a predetermined value S, i.e., R1 - R2 ≤ S. The predetermined value S is the thickness of the interfacial thermal resistance layer covering the outer surface of the filling particle. If so, the unit cell is located within the interfacial region of the filling particle. Therefore, the material property of the unit cell is determined as an interfacial layer property, and the thermal conductivity of the interfacial thermal resistance layer is used as the thermal conductivity of the unit cell. If the difference between the distance R1 between the unit cell and the filling particles and the radius R2 of the filling particles is greater than a predetermined value S (i.e., R1-R2>S), it indicates that the unit cell is completely outside the filling particles. Therefore, the material property of the unit cell is determined to be a matrix property, and the thermal conductivity of the matrix is ​​used as the thermal conductivity of the unit cell. The predetermined value S is the thickness of the interfacial thermal resistance layer, and preferably, the predetermined value S is the side length of a unit cell.

[0078] Furthermore, since the spacing between some filler particles in the thermal interface material structure model is small, there is an overlapping region in the interface thermal resistance layer of two adjacent filler particles. This overlapping region is defined as the contact layer, and the thermal conductivity of the contact layer is different from that of the interface thermal resistance layer. Therefore, it is necessary to further determine whether the unit cell is simultaneously located within the interface region of two adjacent filler particles. If so, it indicates that the unit cell is located within the contact layer region, and the material property of the unit cell is determined to be the contact layer property, thus using the thermal conductivity of the contact layer as the thermal conductivity of the unit cell; otherwise, it indicates that the property of the unit cell is a particle property. This embodiment discusses the case where only one type of filler particle exists. In this case, the thermal conductivity of the unit cell has four different values. When multiple different types of filler particles exist, the thermal conductivity of the unit cell has even more different values, and the determination method is the same as the method described above.

[0079] It should be further noted that the coordinates of the determination point of the unit cell obtained in step S21 are used as the position parameters of the unit cell for subsequent calculations.

[0080] In step S30, an iterative calculation method is used to calculate the heat flow field and temperature gradient field of each unit cell, such as... Figure 4 As shown, the specific method includes the following steps:

[0081] Step 1: Calculate the temperature gradient Θ of the unit cell according to the following transformation relationship.i (x), where p i (x) represents the polarization field, k(x) represents the thermal conductivity of the unit cell, k0 is a constant, x is the position parameter of the unit cell, and i is the iteration number.

[0082] Θ i (x)=(k(x)-k0) -1 ·p i (x);

[0083] Step 2: Convert the temperature gradient Θ from Step 1 i (x) and polarization field p i (x) is converted to the temperature gradient Θ in Fourier space. i (ξ) and the polarization field q in Fourier space i (ξ), that is Here, ξ is the discrete wave vector. It should be noted that the temperature gradient Θ in Fourier space is used during the Fourier transform. i (ξ) and polarization field q i (ξ) is related to the position parameter x of the unit cell in the solid space.

[0084] Step 3: Calculate the thermal flow field q of the unit cell in Fourier space according to the following transformation relationship. i (ξ),

[0085] q i (ξ)=k0·Θ i (ξ)+p i (ξ);

[0086] Step 4: Obtain the temperature gradient Θ based on Step 2. i (ξ) and the heat flow field q obtained in step three i (ξ) The iteration error is calculated. When the iteration error is less than or equal to the threshold, the iteration calculation is stopped, and the temperature gradient Θ obtained in step two is used. i (ξ) and the heat flow field q obtained in step three i (ξ) represents the final temperature gradient and heat flow field of the unit cell; if the iteration error exceeds the threshold, proceed to step five.

[0087] Step 5: Polarization field p in the Fourier space from Step 2 i (ξ) is updated, and the updated p is used as the result. i+1 (ξ) is converted into the polarization field p in physical space. i+1 (x), proceed to step one.

[0088] In step four, the iteration error e i The definition is as follows:

[0089]

[0090] in, The projection matrix representing the discrete wave vector ξ, when the iteration error e i When the value is less than or equal to the threshold, it indicates that the calculation process has converged, and the iterative calculation stops at this point. As a preferred embodiment, the threshold is selected as 10. -3 .

[0091] In step five, the polarization field p in Fourier space is transformed according to the following relationship. i (ξ) is updated.

[0092] p i+1 (ξ)=p i (ξ)-αk0·Γ 0 (ξ)·q i (ξ)-βΣ 0 (ξ)·Θ i (ξ),

[0093] in, α and β are constants. In a preferred embodiment, α = 1.5, β = 1.5, and k0 = -k m -k m This indicates the thermal conductivity of the matrix.

[0094] Repeat the iterative process from step one to step five above to finally obtain the temperature gradient and heat flow field of the unit cell.

[0095] In step S40, the average heat flow field is obtained by performing a volume average of the heat flow field of all unit cells. The average temperature gradient field is obtained by volume averaging the temperature gradient fields of all unit cells. Finally, the average heat flow field With mean temperature gradient field The ratio of is used as the macroscopic thermal conductivity of the thermal interface material structure model.

[0096] The macroscopic thermal conductivity calculation method for the thermal interface material structure model disclosed in this embodiment divides the thermal interface material structure model into multiple unit cells, identifies the properties of each unit cell and assigns a corresponding thermal conductivity, thereby calculating the heat flow field and temperature gradient field of each unit cell separately, improving the calculation accuracy of macroscopic thermal conductivity; and adopts the Fourier transform method, which can calculate quickly and reduce the calculation cost.

[0097] Example 2

[0098] like Figure 5As shown, the macroscopic thermal conductivity calculation device for the thermal interface material structure model disclosed in Embodiment 2 includes a model segmentation module 100, an input parameter acquisition module 200, and a calculation module 300. The model segmentation module 100 is used to divide the pre-constructed thermal interface material structure model into a predetermined number of unit cells, which include multiple types of unit cells with different thermal conductivities. The input parameter acquisition module 200 is used to calculate the heat flow field and temperature gradient field of each unit cell based on its thermal conductivity and position parameters. The calculation module 300 is used to calculate the heat flow field and temperature gradient field of each unit cell based on its thermal conductivity and position parameters; and to obtain the macroscopic thermal conductivity of the thermal interface material structure model based on the heat flow field and temperature gradient field of all unit cells.

[0099] Specifically, the input parameter acquisition module 200 is also used to calculate the distance between the determination point of each unit cell and the center of each filling particle; and to determine the material properties of each unit cell based on the relationship between the distance between the determination point of each unit cell and the center of each filling particle and the radius of each filling particle; and finally to determine the thermal conductivity of each unit cell based on the material properties of each unit cell.

[0100] Furthermore, the input parameter acquisition module 200 is also used to determine whether the distance between the determination point of the unit cell and the center of each filling particle is less than or equal to the radius of the filling particle; if yes, then the material property of the unit cell is determined to be a particle property; if no, then the difference between the distance between the unit cell and the filling particle and the radius of the filling particle is determined to be less than or equal to a predetermined value; if yes, then the unit cell is determined to be within the interface region of the filling particle, and the material property of the unit cell is determined to be an interface layer property; if no, then the material property of the unit cell is determined to be a matrix property.

[0101] In addition, the input parameter acquisition module 200 is also used to determine whether the unit cell is simultaneously located in the interface region of two adjacent filling particles; if so, the material property of the unit cell is determined to be the contact layer property, otherwise the material property of the unit cell is determined to be the matrix property.

[0102] Specifically, the calculation module 300 uses the following methods to calculate the thermal flow field and temperature gradient field of each unit cell based on the thermal conductivity and position parameters of each unit cell:

[0103] Step 1: Calculate the temperature gradient Θ of the unit cell according to the following transformation relationship. i (x), where p i (x) represents the polarization field, k(x) represents the thermal conductivity of the unit cell, k0 is a constant, x is the position parameter of the unit cell, and i is the iteration number.

[0104] Θ i (x)=(k(x)-k0) -1 ·p i (x);

[0105] Step 2: Convert the temperature gradient Θ from Step 1 i (x) and polarization field p i (x) is converted to the temperature gradient Θ in Fourier space. i (ξ) and the polarization field p in Fourier space i (ξ), that is Where ξ is the discrete wave vector.

[0106] Step 3: Calculate the thermal flow field q of the unit cell in Fourier space according to the following transformation relationship. i (ξ),

[0107] q i (ξ)=k0·Θ i (ξ)+p i (ξ);

[0108] Step 4: Obtain the temperature gradient Θ based on Step 2. i (ξ) and the heat flow field q obtained in step three i (ξ) The iteration error is calculated. When the iteration error is less than or equal to the threshold, the iteration calculation is stopped, and the temperature gradient Θ obtained in step two is used. i (ξ) and the heat flow field q obtained in step three i (ξ) represents the final temperature gradient and heat flow field of the unit cell; if the iteration error exceeds the threshold, proceed to step five.

[0109] Step 5: Polarization field p in the Fourier space from Step 2 i (ξ) is updated, and the updated p is used as the result. i+1 (ξ) is converted into the polarization field p in physical space. i+1 (x), proceed to step one.

[0110] Example 3

[0111] This embodiment also discloses a computer-readable storage medium that stores a macroscopic thermal conductivity calculation program for a thermal interface material structure model. When the macroscopic thermal conductivity calculation program for the thermal interface material structure model is executed by a processor, it implements the macroscopic thermal conductivity calculation method for the thermal interface material structure model of embodiment one or embodiment two.

[0112] Example 4

[0113] This fourth embodiment also discloses a computer device, at the hardware level, such as... Figure 6As shown, the terminal includes a processor 12, an internal bus 13, a network interface 14, and a computer-readable storage medium 11. The processor 12 reads the corresponding computer program from the computer-readable storage medium and runs it, forming a request processing device at the logical level. Of course, in addition to the software implementation, one or more embodiments of this specification do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices. The computer-readable storage medium 11 stores a macroscopic thermal conductivity calculation program for a thermal interface material structure model. When the processor executes the macroscopic thermal conductivity calculation program for the thermal interface material structure model, it implements the above-described macroscopic thermal conductivity calculation method for the thermal interface material structure model.

[0114] Computer-readable storage media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0115] The specific embodiments of the present invention have been described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that modifications and improvements can be made to these embodiments without departing from the principles and spirit of the present invention as defined by the claims and their equivalents, and such modifications and improvements should also be within the protection scope of the present invention.

Claims

1. A method for calculating the macroscopic thermal conductivity of a thermal interface material structure model, characterized in that, The method for calculating macroscopic thermal conductivity includes: The pre-constructed thermal interface material structure model is divided into a predetermined number of unit cells, wherein the predetermined number of unit cells includes multiple types of unit cells with different thermal conductivity, and each unit cell has the same size. The thermal conductivity and position parameters of each unit cell were obtained sequentially. The thermal flow field and temperature gradient field of each unit cell are calculated based on the thermal conductivity and position parameters of each unit cell. The macroscopic thermal conductivity of the thermal interface material structure model is obtained based on the thermal flow field and temperature gradient field of all unit cells. Methods for calculating the heat flow field and temperature gradient field of each unit cell based on the thermal conductivity and position parameters of each unit cell include: Step 1: Calculate the temperature gradient of the unit cell according to the following transformation relationship. ,in Represents the polarization field. Indicates the thermal conductivity of a unit cell. It is a constant. These are the position parameters of the unit cell. For the number of iterations, ； Step 2: Convert the temperature gradient from Step 1 and polarization field Temperature gradient converted to Fourier space polarization field of Fourier space ; Step 3: Calculate the thermal flow field of the unit cell in Fourier space according to the following transformation relationship. , ； Step 4: Obtain the temperature gradient based on Step 2. and the heat flow field obtained in step three The iteration error is calculated, and the iteration calculation is stopped when the iteration error is less than or equal to a threshold. The temperature gradient obtained in step two is then used. and the heat flow field obtained in step three The final temperature gradient and temperature gradient of the unit cell; if the iteration error is greater than the threshold, proceed to step five; Step 5: Polarization field of the Fourier space from Step 2 Perform the update and obtain the updated data. Polarization field converted into physical space Proceed to step one.

2. The method for calculating the macroscopic thermal conductivity of the thermal interface material structure model according to claim 1, characterized in that, Methods for obtaining the thermal conductivity of each unit cell include: Calculate the distance between the determination point of each unit cell and the center of each filling particle; The material properties of each unit cell are determined based on the distance between the determination point of each unit cell and the center of each filling particle and the relationship between the radius of each filling particle. The thermal conductivity of each unit cell is determined based on the material properties of each unit cell.

3. The method for calculating the macroscopic thermal conductivity of the thermal interface material structure model according to claim 2, characterized in that, The method for determining the material properties of each unit cell based on the distance between the determination point of each unit cell and the center of each filling particle and the relationship between the radii of each filling particle includes: Determine whether the distance between the determination point of the unit cell and the center of each filling particle is less than or equal to the radius of the filling particle; If so, then the material property of the unit cell is determined to be a particle property; If not, then determine whether the difference between the distance between the unit cell and the filling particle and the radius of the filling particle is less than or equal to a predetermined value; If so, then the unit cell is determined to be located within the interface region of the filling particles, and the material properties of the unit cell are determined to be interface layer properties. If not, then the material properties of the unit cell are determined to be matrix properties.

4. The method for calculating the macroscopic thermal conductivity of the thermal interface material structure model according to claim 3, characterized in that, The method for calculating macroscopic thermal conductivity also includes: Determine whether the unit cell is simultaneously located within the interface region of two adjacent filling particles; If so, then the material properties of the unit cell are determined to be contact layer properties.

5. The method for calculating the macroscopic thermal conductivity of the thermal interface material structure model according to claim 1, characterized in that, Methods for obtaining the macroscopic thermal conductivity of the thermal interface material structure model based on the heat flow field and temperature gradient field of all unit cells include: The average heat flow field is obtained by volume averaging the heat flow field of all unit cells, and the average temperature gradient field is obtained by volume averaging the temperature gradient field of all unit cells. The ratio of the average heat flow field to the average temperature gradient field is used as the macroscopic thermal conductivity of the thermal interface material structure model.

6. The method for calculating the macroscopic thermal conductivity of the thermal interface material structure model according to claim 1, characterized in that, In step five, the polarization field in Fourier space is transformed according to the following relationship. Update ， ， ， ； in, and It is a constant.

7. A device for calculating the macroscopic thermal conductivity of a thermal interface material structure model, characterized in that, The macroscopic thermal conductivity calculation device includes: The model segmentation module is used to divide a pre-built thermal interface material structure model into a predetermined number of unit cells, wherein the predetermined number of unit cells includes multiple types of unit cells with different thermal conductivity. The input parameter acquisition module calculates the thermal flow field and temperature gradient field of each unit cell based on the thermal conductivity and position parameters of each unit cell. The calculation module is used to calculate the heat flow field and temperature gradient field of each unit cell based on the thermal conductivity and position parameters of each unit cell; and The macroscopic thermal conductivity of the thermal interface material structure model is obtained based on the thermal flow field and temperature gradient field of all unit cells. Methods for calculating the heat flow field and temperature gradient field of each unit cell based on the thermal conductivity and position parameters of each unit cell include: Step 1: Calculate the temperature gradient of the unit cell according to the following transformation relationship. ,in Represents the polarization field. Indicates the thermal conductivity of a unit cell. It is a constant. These are the position parameters of the unit cell. For the number of iterations, ； Step 2: Convert the temperature gradient from Step 1 and polarization field Temperature gradient converted to Fourier space polarization field of Fourier space ; Step 3: Calculate the thermal flow field of the unit cell in Fourier space according to the following transformation relationship. , ； Step 4: Obtain the temperature gradient based on Step 2. and the heat flow field obtained in step three The iteration error is calculated, and the iteration calculation is stopped when the iteration error is less than or equal to a threshold. The temperature gradient obtained in step two is then used. and the heat flow field obtained in step three The final temperature gradient and temperature gradient of the unit cell; if the iteration error is greater than the threshold, proceed to step five; Step 5: Polarization field of the Fourier space from Step 2 Perform the update and obtain the updated data. Polarization field converted into physical space Proceed to step one.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a macroscopic thermal conductivity calculation program for a thermal interface material structure model. When the macroscopic thermal conductivity calculation program for the thermal interface material structure model is executed by a processor, it implements the macroscopic thermal conductivity calculation method for the thermal interface material structure model according to any one of claims 1 to 6.

9. A computer device, characterized in that, The computer device includes a computer-readable storage medium, a processor, and a macroscopic thermal conductivity calculation program for a thermal interface material structure model stored in the computer-readable storage medium. When the processor executes the macroscopic thermal conductivity calculation program for the thermal interface material structure model, it implements the macroscopic thermal conductivity calculation method for the thermal interface material structure model according to any one of claims 1 to 6.

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