Composite insulation board heat conductivity coefficient theoretical calculation method and application

Abstract

The invention relates to a theoretical calculation method for the heat conductivity coefficient of a composite insulation board and application. Comprising the following steps: carrying out microstructure characterization on a composite material in the composite insulation board to obtain internal information of the composite material; constructing an interface model of the polymer and the filler to simulate the motion trail of atoms; calculating interface thermal conductivity and interface thermal resistance according to unbalanced molecular dynamics; calculating the heat conductivity coefficient of the composite material through a finite element method; a finite element method or a lattice Boltzmann method is used for simulating the macroscopic heat conduction process of the composite insulation board, microstructure information and interface thermal resistance of a material are considered, and the effective heat conductivity coefficient of the insulation board is calculated; meanwhile, a heat flow path and temperature distribution can be obtained through simulation, and a basis is provided for structural design and optimization of the insulation board.

Description

Technical Field

[0001] This invention relates to the field of thermal conductivity calculation technology for composite insulation boards, and particularly to a theoretical calculation method and application for the thermal conductivity of composite insulation boards. Background Technology

[0002] Composite insulation boards include aluminum foil rubber-plastic insulation boards, self-adhesive rubber-plastic insulation boards, YT inorganic active wall insulation boards, cement fiber boards, Class B composite insulation boards, rock wool composite insulation boards, composite phenolic insulation boards, composite polyurethane insulation boards, and insulating foam glass boards, etc. Composite insulation boards are made with additives, cement, sand, and adhesive as bonding materials; fiberglass mesh and steel bars as reinforcing materials; wood fiber and fly ash as fillers; and polyethylene foam board as insulation material. They are manufactured in factories through a reasonable material ratio and scientific production process. They can be used in new buildings, extensions, renovations of old buildings, flat roof to pitched roof conversions, villas, factory buildings, and rural construction.

[0003] The optimized design of thermal insulation materials involves thermal conductivity calculations. Thermal conductivity is a crucial parameter for quantitatively characterizing the thermal insulation performance of insulation materials, and its accurate and efficient measurement is of great significance for the optimized design of energy-saving materials. The thermal conductivity of building insulation materials is affected by many factors, including material composition, type, combination ratio and method, material density, and molecular structure. Currently, domestic and international scholars mainly use theoretical calculations, indoor experiments, and numerical simulations to study the thermal conductivity of insulation materials. Theoretical calculations can quickly obtain the thermal conductivity of insulation materials and save considerable resources. Many scholars have constructed thermal conductivity models for porous media insulation materials, among which series and parallel models are the most widely used. However, traditional parallel and series models cannot efficiently and accurately predict the thermal conductivity of graphene composite insulation boards. Indoor experiments can accurately predict the thermal conductivity of insulation materials, but experimental testing is time-consuming and labor-intensive, and the test results are affected by experimental methods and errors.

[0004] Existing patent application CN202311255670.3 discloses a parallel-series-parallel calculation method for the equivalent thermal conductivity of a graphene composite insulation board, including the following steps: S1, establishing a parallel-series-parallel calculation model for the graphene composite insulation board; S2, determining the mass and volume proportions of the matrix material and three impurity materials of the graphene composite insulation board; S3, calculating the thermal resistance of the secondary sub-unit, the thermal resistance of the matrix material of the secondary sub-unit, and the thermal resistance of the inclusion material of the secondary sub-unit; S4, calculating the overall unit thermal resistance, the thermal resistance of the primary sub-unit, and the thermal resistance of the matrix material of the primary sub-unit; S5, calculating the equivalent thermal conductivity of the secondary sub-unit; S6, calculating the equivalent thermal conductivity of the primary sub-unit; S7, calculating the equivalent thermal conductivity of the overall thermally conductive unit. However, it also has the following drawbacks:

[0005] The series / parallel model ignores the effects of interfacial thermal resistance and microstructure, leading to calculation errors;

[0006] Insufficient anisotropic treatment: The orientational thermal conductivity of fiber or layered structures is difficult to describe with a single model;

[0007] Interface effect: The contact thermal resistance between different materials in the composite board is not fully taken into account.

[0008] Non-uniformity: Uneven bubble distribution, fiber agglomeration and other actual defects affect the accuracy of the model.

[0009] Therefore, this invention proposes a theoretical calculation method for the thermal conductivity of composite insulation boards and its application. Summary of the Invention

[0010] This invention addresses the technical problems existing in the prior art by providing a theoretical calculation method for the thermal conductivity of composite insulation boards and its application.

[0011] The technical solution of this invention to solve the above-mentioned technical problems is as follows: a theoretical calculation method for the thermal conductivity of composite insulation boards and its application; including the following steps:

[0012] S1: To characterize the microstructure of the composite material in the composite insulation board and obtain information about the interior of the composite material;

[0013] S2: Construct an interface model of the polymer and filler to simulate the trajectory of atoms;

[0014] S3: Calculate the interfacial thermal conductivity and interfacial thermal resistance based on non-equilibrium molecular dynamics;

[0015] S4: The thermal conductivity of the composite material will be calculated using the finite element method.

[0016] Furthermore, in the theoretical calculation method for the thermal conductivity of the composite insulation board, the microstructure characterization includes:

[0017] X-ray micro-CT uses X-rays to penetrate the sample and reconstructs the three-dimensional structure based on the differences in the degree of X-ray absorption by different substances; scanning electron microscopy uses secondary electrons and backscattered electrons generated by the interaction between the electron beam and the sample surface to image and observe the surface morphology of the sample; transmission electron microscopy allows the electron beam to penetrate the sample, and combined with energy dispersive spectroscopy, it can perform qualitative and quantitative analysis of the elements inside the sample.

[0018] Furthermore, in the theoretical calculation method for the thermal conductivity of the composite insulation board, step S2 includes:

[0019] S21: Heat flow is driven by establishing a temperature gradient in the system. Typically, high-temperature and low-temperature heat baths are placed at opposite ends of the simulation system, allowing heat to flow from the high-temperature end to the low-temperature end, forming heat flow J.

[0020] S22: The heat flux J can be obtained by calculating the transfer of kinetic and potential energy of atoms. At discrete time steps, the heat flux can be expressed as:

[0021]

[0022] Where A is the cross-sectional area perpendicular to the direction of heat flow, Δt is the time interval, and m i and v i These are the mass and velocity of atom i, respectively, φ i It is atomic potential energy, v i,x It is the velocity component of the atom in the direction of heat flow;

[0023] S23: Temperature gradient By re-dividing the system into multiple regions, calculating the average temperature of each region, and then determining the temperature based on the change in temperature with location;

[0024] S24: Calculate the interfacial thermal conductivity and interfacial thermal resistance:

[0025] Interfacial thermal conductivity:

[0026] Interfacial thermal resistance:

[0027] Furthermore, the theoretical calculation method for the thermal conductivity of the composite insulation board includes the following in step S4:

[0028] Variational principle and discretization of heat conduction equation:

[0029] The general form of the heat conduction equation in Cartesian coordinates is:

[0030]

[0031] Where ρ is the material density, c p It is the specific heat capacity at constant pressure, where T is temperature, t is time, k is thermal conductivity, and Q is the internal heat source;

[0032] Based on the variational principle, the solution domain is connected into a finite number of elements e, and a shape function N is defined for each element e. i To approximate the temperature distribution Where n is the number of unit nodes. It is the temperature of cell node i;

[0033] Substituting the approximate temperature distribution into the heat conduction equation, the heat conduction equation for the unit cell is obtained using the weighted residual method:

[0034]

[0035] Among them, [C e [K] is the element heat capacity matrix. e [] is the unit heat conduction matrix. It is the vector of the derivative of the unit node temperature with respect to time, {T e} is the element node temperature vector, {Q e} is the element node thermal load vector;

[0036] Assemble all the equations of the units into a global equation:

[0037]

[0038] By solving this system of equations, the temperature distribution of the entire solution region can be obtained;

[0039] Effective thermal conductivity can be calculated by heat flux q and temperature gradient. According to Fourier's law, this is obtained. In the simulation, the effective thermal conductivity k is determined by calculating the heat flow and temperature gradient in a specific region. eff .

[0040] An application of a theoretical calculation method for the thermal conductivity of a composite insulation board, applicable to any of the aforementioned theoretical calculation methods for the thermal conductivity of a composite insulation board, the application including:

[0041] Microstructure characterization of composite materials in composite insulation boards is performed to obtain information about the interior of the composite materials.

[0042] An interface model of the polymer and filler was constructed to simulate the trajectory of atoms.

[0043] Calculate the interfacial thermal conductivity and interfacial thermal resistance based on nonequilibrium molecular dynamics.

[0044] The thermal conductivity of the composite material will be calculated using the finite element method.

[0045] Multi-scale simulation is introduced to couple simulation results at different scales; the parameter mapping method uses the calculation results at the microscale as the boundary conditions for the macroscale simulation; the order reduction model generates the temperature-dependent material parameters required for the macroscale simulation by interpolating the microscale results.

[0046] Furthermore, in the application of the theoretical calculation method for the thermal conductivity of the composite insulation board, the parameter mapping method includes: in the heat conduction problem, the interfacial thermal resistance R obtained from molecular dynamics calculations. int The thermal contact resistance R can be used as a boundary condition for macroscopic-scale simulation. intThe relationship between the temperature difference ΔT and the heat flux q follows an extended form of Fourier's law: In macroscopic simulations, this boundary condition can be expressed as the relationship between heat flow and temperature difference at the interface, thereby transmitting microscopic thermal resistance information to the macroscopic scale.

[0047] Furthermore, in the application of the theoretical calculation method for the thermal conductivity of composite insulation boards, the reduced-order model includes Kriging interpolation, used to generate a continuous response surface based on known discrete data points. In multi-scale simulations, Kriging interpolation is used to process the MD results to generate a response surface showing the change in interfacial thermal resistance with temperature.

[0048] Furthermore, the application of the theoretical calculation method for the thermal conductivity of composite insulation boards, based on multi-scale simulation results, yields the thermal conductivity characteristics of different composite material microstructures, thereby improving manufacturing processes or material compositions to produce composite insulation boards with different requirements.

[0049] The beneficial effects of this invention are:

[0050] By characterizing the microstructure of composite insulation boards, we can understand the internal pore structure, fiber distribution, and other information. This information is crucial for accurately calculating the thermal conductivity of the insulation board. For example, porosity affects the heat conduction path and thermal resistance of the material, while the orientation and distribution of fibers affect the anisotropic thermal conductivity of the material.

[0051] The interfacial thermal resistance between different materials in composite insulation boards can be calculated using molecular dynamics simulations. Interfacial thermal resistance is one of the important factors affecting the thermal conductivity of composite insulation boards. Obtaining accurate interfacial thermal resistance through simulation and using it as an input parameter for macroscopic simulations improves the accuracy of thermal conductivity calculations.

[0052] The macroscopic heat conduction process of composite insulation boards is simulated using the finite element method or the lattice Boltzmann method, taking into account the microstructure information of the material and the interfacial thermal resistance, to calculate the effective thermal conductivity of the insulation board. Simultaneously, the heat flow path and temperature distribution can be obtained through simulation, providing a basis for the structural design and optimization of the insulation board.

[0053] By employing multi-scale data transfer technology, microscale simulation results are coupled with macroscale simulations to accurately calculate the thermal conductivity of composite insulation boards. During the calculation process, experimental comparisons verify the model's reliability, and inverse optimization methods are used to adjust the microstructural parameters of the insulation board, thereby improving its insulation performance. For example, adjusting the fiber orientation distribution makes the temperature field of the insulation board more uniform and reduces the temperature field standard deviation, thus improving the insulation effect. Attached Figure Description

[0054] Figure 1This is a flowchart illustrating a theoretical calculation method for the thermal conductivity of a composite insulation board.

[0055] Figure 2 This is a schematic diagram of one embodiment. Detailed Implementation

[0056] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0057] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0058] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0059] A theoretical calculation method for the thermal conductivity of a composite insulation board includes the following steps:

[0060] S1: To characterize the microstructure of the composite material in the composite insulation board and obtain information about the interior of the composite material;

[0061] S2: Construct an interface model of the polymer and filler to simulate the trajectory of atoms;

[0062] S3: Calculate the interfacial thermal conductivity and interfacial thermal resistance based on non-equilibrium molecular dynamics;

[0063] S4: The thermal conductivity of the composite material will be calculated using the finite element method.

[0064] In one embodiment, the microstructure characterization includes:

[0065] X-ray micro-CT uses X-rays to penetrate the sample and reconstructs the three-dimensional structure based on the differences in the degree of X-ray absorption by different substances; scanning electron microscopy uses secondary electrons and backscattered electrons generated by the interaction between the electron beam and the sample surface to image and observe the surface morphology of the sample; transmission electron microscopy allows the electron beam to penetrate the sample, and combined with energy dispersive spectroscopy, it can perform qualitative and quantitative analysis of the elements inside the sample.

[0066] Specifically: Based on the characteristic that different materials absorb X-rays to varying degrees when X-rays penetrate a sample. When X-rays pass through a sample, their intensity attenuates depending on factors such as the density and atomic number of the material. For composite materials, different components (such as pores, fibers, and matrix) have different linear attenuation coefficients. Therefore, by measuring the intensity change of X-rays after passing through the sample at different angles, the internal structural information of the sample can be obtained. A three-dimensional structural model of the composite material is obtained through X-ray micro-CT scanning and Feldkamp algorithm reconstruction. This model includes information on the distribution of pores and fibers. Specific data includes:

[0067] The size, shape, location, and volume fraction of pores;

[0068] The length, diameter, orientation, and distribution of the fibers.

[0069] SEM (Sequencing Electron Imaging) utilizes secondary electrons and backscattered electrons generated by the interaction of an electron beam with the sample surface for imaging. When a high-energy electron beam bombards the sample surface, it excites the outer electrons of the surface atoms, generating secondary electrons. These secondary electrons have lower energy and primarily originate from depths of a few nanometers to tens of nanometers within the sample surface; their yield is closely related to the surface morphology. Backscattered electrons, on the other hand, are electrons that return after elastic scattering of incident electrons with sample atoms. They have higher energy, and their yield is related to the atomic number of the sample. By detecting the signal intensities of secondary and backscattered electrons and converting them into image grayscale values, the morphological image of the sample surface can be obtained.

[0070] TEM Principle: TEM allows an electron beam to penetrate a sample. Because different regions within the sample scatter electrons differently, the intensity of the transmitted electron beam varies. These electron beams are then focused and magnified by a series of electromagnetic lenses, forming a transmission image of the sample on a fluorescent screen or detector. Factors such as the sample's thickness, density, and crystal structure all affect electron scattering and transmission, resulting in varying contrast in the image. TEM images can provide high-resolution information about the internal microstructure of composite materials, such as the crystal structure of fibers and the matrix, and the micromorphology of interfaces.

[0071] EDS Principle: EDS is based on the characteristic X-ray emission principle of elements. When a high-energy electron beam bombards a sample, the atoms in the sample are excited, inner-shell electrons are ejected to create vacancies, and outer-shell electrons jump to fill these vacancies, releasing characteristic X-rays with specific energies. Different elements have different characteristic X-ray energies. By detecting the energy and intensity of these characteristic X-rays, qualitative and quantitative analysis of the elements in the sample can be performed. EDS analysis obtains information on the types and contents of each element within the sample, expressed as mass fraction or atomic fraction. For example, it can obtain the content of major elements (such as carbon, oxygen, silicon, etc.) in fibers and matrices, as well as the diffusion of elements in interfacial regions.

[0072] In one embodiment, step S2 includes:

[0073] S21: Heat flow is driven by establishing a temperature gradient in the system. Typically, high-temperature and low-temperature heat baths are placed at opposite ends of the simulation system, allowing heat to flow from the high-temperature end to the low-temperature end, forming heat flow J.

[0074] S22: The heat flux J can be obtained by calculating the transfer of kinetic and potential energy of atoms. At discrete time steps, the heat flux can be expressed as:

[0075]

[0076] Where A is the cross-sectional area perpendicular to the direction of heat flow, Δt is the time interval, and m i and v i These are the mass and velocity of atom i, respectively, φ i It is atomic potential energy, v i,x It is the velocity component of the atom in the direction of heat flow;

[0077] S23: Temperature gradient By re-dividing the system into multiple regions, calculating the average temperature of each region, and then determining the temperature based on the change in temperature with location;

[0078] S24: Calculate the interfacial thermal conductivity and interfacial thermal resistance:

[0079] Interfacial thermal conductivity:

[0080] Interfacial thermal resistance:

[0081] Specifically, interfacial thermal conductivity and thermal resistance data can be used to evaluate the heat transfer performance of different polymer-filler combinations, guiding the design of novel composite materials. For example, by comparing the thermal resistance of different filler-polymer interfaces, filler-polymer combinations with lower thermal resistance can be selected to improve the overall thermal conductivity of the composite material.

[0082] In the actual research process, the model was constructed as a graphene / hexagonal boron nitride (hBN) interface;

[0083] Force field selection: AIREBO-Morse (graphene) + Tersoff (hBN);

[0084] Boundary conditions: periodic in the x / y directions, free surface in the z direction;

[0085] Simulation process:

[0086] Equilibrium phase: 500 ps relaxation under NPT ensemble (pressure coupling limited to x / y directions);

[0087] Non-equilibrium phase: Apply heat bath and run for 2 ns (time step 0.5 fs);

[0088] Results analysis: J = 8.7 GW / m 2 ;

[0089] Indicates that Driven by this, the energy passing through a unit area per unit time. This value is relatively high (experimental values ​​are typically around 5 GW / m²). 2 ).

[0090] Temperature gradient differences:

[0091] Graphene region:

[0092] hBN zone:

[0093] The difference stems from the shorter mean free path of hBN phonons (which results in lower thermal conductivity);

[0094] This case demonstrates that the NEMD method can accurately resolve the atomic-level mechanisms of interfacial thermal resistance (such as phonon mismatch and interfacial chemical bonds), providing a quantitative basis for composite material optimization. In practical applications, experimental characterization (such as time-resolved Raman spectroscopy) is needed to verify the reliability of the simulation parameters.

[0095] In one embodiment, step S4 includes:

[0096] Variational principle and discretization of heat conduction equation:

[0097] The general form of the heat conduction equation in Cartesian coordinates is:

[0098]

[0099] Where ρ is the material density, c p It is the specific heat capacity at constant pressure, where T is temperature, t is time, k is thermal conductivity, and Q is the internal heat source;

[0100] Based on the variational principle, the solution domain is connected into a finite number of elements e, and a shape function N is defined for each element e. i To approximate the temperature distribution Where n is the number of unit nodes. It is the temperature of cell node i;

[0101] Substituting the approximate temperature distribution into the heat conduction equation, the heat conduction equation for the unit cell is obtained using the weighted residual method:

[0102]

[0103] Among them, [C e [K] is the element heat capacity matrix. e [] is the unit heat conduction matrix. It is the vector of the derivative of the unit node temperature with respect to time, {T e} is the element node temperature vector, {Q e} is the element node thermal load vector;

[0104] Assemble all the equations of the units into a global equation:

[0105]

[0106] By solving this system of equations, the temperature distribution of the entire solution region can be obtained;

[0107] Effective thermal conductivity can be calculated by heat flux q and temperature gradient. According to Fourier's law, this is obtained. In the simulation, the effective thermal conductivity k is determined by calculating the heat flow and temperature gradient in a specific region. eff .

[0108] An application of a theoretical calculation method for the thermal conductivity of a composite insulation board, applicable to any of the aforementioned theoretical calculation methods for the thermal conductivity of a composite insulation board, the application including:

[0109] Microstructure characterization of composite materials in composite insulation boards is performed to obtain information about the interior of the composite materials.

[0110] An interface model of the polymer and filler was constructed to simulate the trajectory of atoms.

[0111] Calculate the interfacial thermal conductivity and interfacial thermal resistance based on nonequilibrium molecular dynamics.

[0112] The thermal conductivity of the composite material will be calculated using the finite element method.

[0113] Multi-scale simulation is introduced to couple simulation results at different scales; the parameter mapping method uses the calculation results at the microscale as the boundary conditions for the macroscale simulation; the order reduction model generates the temperature-dependent material parameters required for the macroscale simulation by interpolating the microscale results.

[0114] The parameter mapping method includes: in heat conduction problems, the interfacial thermal resistance R obtained from molecular dynamics calculations. int The thermal contact resistance R can be used as a boundary condition for macroscopic-scale simulation. int The relationship between the temperature difference ΔT and the heat flux q follows an extended form of Fourier's law: In macroscopic simulations, this boundary condition can be expressed as the relationship between heat flow and temperature difference at the interface, thereby transmitting microscopic thermal resistance information to the macroscopic scale.

[0115] The reduced-order model includes Kriging interpolation, used to generate a continuous response surface based on known discrete data points. In the multi-scale simulation, Kriging interpolation is used to process the MD results to generate a response surface showing the interfacial thermal resistance as a function of temperature.

[0116] Based on the results of multi-scale simulations, the thermal conductivity characteristics of different composite material microstructures are obtained, and the manufacturing process or material composition is improved to produce composite insulation boards with different requirements.

[0117] The following section uses the thermal conductivity simulation of carbon fiber reinforced resin matrix composites as an example to explain in detail the overall process of parameter mapping methods and reduced-order models (ROMs) in multi-scale simulations:

[0118] A molecular dynamics model of the carbon fiber-resin matrix interface was constructed. For example, an all-atomic force field was used to describe the interaction between carbon fiber and resin molecules; appropriate regions and periodic boundary conditions were set (to define the size of the composite material). Here, a boundary condition with side lengths of Lx = Ly = Lz = 5 nm was selected, containing Ncarbon = 1000 carbon atoms and Nresin = 2000 resin molecules.

[0119] CT scans were performed on carbon fiber reinforced resin matrix composite samples to obtain information on the internal pore structure of the material. Image processing algorithms were used to identify the coordinates of the pores {(xp1,yp1,zp1),(xp2,yp2,zp2),...,(xpm,ypm,zpm)}. It was assumed that m = 50 pores were identified in the sample. A non-equilibrium heat flux was applied. Different heat bath temperatures were set, such as Thigh = 400K at the high-temperature end and Tlow = 300K at the low-temperature end, to allow the system to reach a steady-state heat conduction state. The simulation time step was set to Δt = 1fs, and the total simulation time was t = 10ns. After the simulation reached steady state, the interfacial thermal resistance was calculated. Through multiple simulations, interfacial thermal resistance data points at different temperatures were obtained.

[0120] The interfacial thermal resistance obtained from MD simulation and Kriging interpolation is used as the boundary condition for macroscopic-scale simulation. In FEM simulation, a thermal contact resistance boundary condition is set at the interface between the carbon fiber and the resin matrix, i.e., the corresponding thermal contact resistance is obtained from the response surface based on the current simulation temperature. And according to the calculated heat flow.

[0121] In FEM simulations, appropriate boundary and initial conditions are set, such as the sample's boundary temperature and heat flux. It is assumed that one side of the sample has a constant temperature while the other side has an adiabatic boundary condition. COMSOL software is used to simulate and solve the heat conduction equation, obtaining the macroscopic heat conduction distribution of the composite material. Similarly, in LBM simulations, appropriate boundary conditions are set to simulate fluid flow and heat transfer processes within the porous structure.

[0122] The accuracy of the simulation is verified by comparing the macroscopic simulation results (such as temperature distribution and heat flow distribution) with experimental measurements. For example, the temperature distribution on the surface of a composite material sample is measured using an infrared thermal imager and compared with the temperature distribution obtained from FEM simulation to evaluate the error of the simulation results.

[0123] Based on accurate multi-scale simulation results, the influence of microstructural parameters (such as porosity and interfacial thermal resistance) on macroscopic thermal conductivity is analyzed. For example, by changing the porosity and interfacial thermal resistance, multi-scale simulations are repeated to find the optimal combination of microstructural parameters that can improve the thermal conductivity of composite materials.

[0124] Data obtained from multi-scale simulations (such as interfacial thermal resistance response surfaces at different temperatures, pore-mesh matching information, and macroscopic heat conduction distribution) are organized and stored to establish a multi-scale database. This provides a reference for subsequent research and simulations, reducing the workload of repetitive calculations. For example, when it is necessary to study the thermal conductivity of composite materials with different carbon fiber contents, relevant basic data can be obtained from the database for rapid simulation and analysis.

[0125] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0126] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A theoretical calculation method for the thermal conductivity of a composite insulation board, characterized in that, Includes the following steps: S1: To characterize the microstructure of the composite material in the composite insulation board and obtain information about the interior of the composite material; S2: Construct an interface model of the polymer and filler to simulate the trajectory of atoms; S3: Calculate the interfacial thermal conductivity and interfacial thermal resistance based on non-equilibrium molecular dynamics; S4: The thermal conductivity of the composite material will be calculated using the finite element method.

2. The theoretical calculation method for the thermal conductivity of a composite insulation board according to claim 1, characterized in that, The microstructure characterization includes: X-ray micro-CT uses X-rays to penetrate the sample and reconstructs the three-dimensional structure based on the differences in the degree of X-ray absorption by different substances; scanning electron microscopy uses secondary electrons and backscattered electrons generated by the interaction between the electron beam and the sample surface to image and observe the surface morphology of the sample; transmission electron microscopy allows the electron beam to penetrate the sample, and combined with energy dispersive spectroscopy, it can perform qualitative and quantitative analysis of the elements inside the sample.

3. The theoretical calculation method for the thermal conductivity of a composite insulation board according to claim 1, characterized in that, Step S2 includes: S21: Heat flow is driven by establishing a temperature gradient in the system. Typically, high-temperature and low-temperature heat baths are placed at opposite ends of the simulation system, allowing heat to flow from the high-temperature end to the low-temperature end, forming heat flow J. S22: The heat flux J can be obtained by calculating the transfer of kinetic and potential energy of atoms. At discrete time steps, the heat flux can be expressed as: Where A is the cross-sectional area perpendicular to the direction of heat flow, Δt is the time interval, and m i and v i These are the mass and velocity of atom i, respectively, φ i It is atomic potential energy, v i,x It is the velocity component of the atom in the direction of heat flow; S23: Temperature gradient By re-dividing the system into multiple regions, calculating the average temperature of each region, and then determining the temperature based on the change in temperature with location; S24: Calculate the interfacial thermal conductivity and interfacial thermal resistance: Interfacial thermal conductivity: Interfacial thermal resistance:

4. The theoretical calculation method for the thermal conductivity of a composite insulation board according to claim 3, characterized in that, The S4 step includes: Variational principle and discretization of heat conduction equation: The general form of the heat conduction equation in Cartesian coordinates is: Where ρ is the material density, c p It is the specific heat capacity at constant pressure, where T is temperature, t is time, k is thermal conductivity, and Q is the internal heat source; Based on the variational principle, the solution domain is connected into a finite number of elements e, and a shape function N is defined for each element e. i To approximate the temperature distribution Where n is the number of unit nodes, T i e It is the temperature of cell node i; Substituting the approximate temperature distribution into the heat conduction equation, the heat conduction equation for the unit cell is obtained using the weighted residual method: Among them, [C e [K] is the element heat capacity matrix. e [] is the unit heat conduction matrix. It is the vector of the derivative of the unit node temperature with respect to time, {T e } is the element node temperature vector, {Q e } is the element node thermal load vector; Assemble all the equations of the units into a global equation: By solving this system of equations, the temperature distribution of the entire solution region can be obtained; Effective thermal conductivity can be calculated by heat flux q and temperature gradient. According to Fourier's law, this is obtained. In the simulation, the effective thermal conductivity k is determined by calculating the heat flow and temperature gradient in a specific region. eff .

5. An application of a theoretical calculation method for the thermal conductivity of a composite insulation board, characterized in that, The application of the theoretical calculation method for the thermal conductivity of a composite insulation board according to any one of claims 1-4, wherein the application includes: Microstructure characterization of composite materials in composite insulation boards is performed to obtain information about the interior of the composite materials. An interface model of the polymer and filler was constructed to simulate the trajectory of atoms. Calculate the interfacial thermal conductivity and interfacial thermal resistance based on nonequilibrium molecular dynamics. The thermal conductivity of the composite material will be calculated using the finite element method. Multi-scale simulation is introduced to couple simulation results at different scales; the parameter mapping method uses the calculation results at the microscale as the boundary conditions for the macroscale simulation; the order reduction model generates the temperature-dependent material parameters required for the macroscale simulation by interpolating the microscale results.

6. The application of the theoretical calculation method for the thermal conductivity of a composite insulation board according to claim 5, characterized in that, The parameter mapping method includes: in heat conduction problems, the interfacial thermal resistance R obtained from molecular dynamics calculations. int The thermal contact resistance R can be used as a boundary condition for macroscopic-scale simulation. int The relationship between the temperature difference ΔT and the heat flux q follows an extended form of Fourier's law: In macroscopic simulations, this boundary condition can be expressed as the relationship between heat flow and temperature difference at the interface, thereby transmitting microscopic thermal resistance information to the macroscopic scale.

7. The application of the theoretical calculation method for the thermal conductivity of a composite insulation board according to claim 5, characterized in that, The reduced-order model includes Kriging interpolation, used to generate a continuous response surface based on known discrete data points. In the multi-scale simulation, Kriging interpolation is used to process the MD results to generate a response surface showing the interfacial thermal resistance as a function of temperature.

8. The application of the theoretical calculation method for the thermal conductivity of a composite insulation board according to claim 5, characterized in that, Based on the results of multi-scale simulations, the thermal conductivity characteristics of different composite material microstructures are obtained, and the manufacturing process or material composition is improved to produce composite insulation boards with different requirements.

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