Method for determining the thermal conductivity of a multiscale three-dimensional structure of a resin-based composite material

Abstract

The application discloses a kind of multi-scale three-dimensional structure heat conductivity coefficient determination method of resin-based composite material.The application first obtains the three-dimensional nanometer pore structure of the resin matrix of composite material using high-resolution focused ion beam scanning electron microscope, and constructs the gas phase heat conductivity coefficient and solid phase heat conductivity coefficient model considering the limited effect of gas nanoscale pore and nanoscale particle, the effective heat conductivity coefficient of resin matrix is calculated using lattice Boltzmann method;Then three-dimensional structure of resin-based composite material is obtained using micro-CT, the gas phase heat conductivity coefficient model is constructed, and the effective heat conductivity coefficient of resin matrix is used as solid phase heat conductivity coefficient, and the effective heat conductivity coefficient of resin-based composite material is obtained using lattice Boltzmann method.The application can obtain micrometer pore topography and nanometer pore topography, realize multi-scale pore structure characterization;The effective heat conductivity coefficient of resin-based composite material calculated is closer to actual heat conductivity coefficient, and higher in accuracy.

Description

Technical Field

[0001] This invention relates to the field of thermal protection materials technology, specifically to a method for determining the thermal conductivity of a multi-scale three-dimensional structure of a resin-based composite material. Background Technology

[0002] Resin-based composite materials, due to their excellent high-temperature oxidation resistance and ablation resistance, have significant application value in the aerospace field as ablation materials. With the development of aerospace technology, the flight speed of aircraft is increasing, and the airflow density is also increasing, leading to increasingly complex aerodynamic heating environments. This presents greater challenges to the performance design of resin-based composite materials. Therefore, obtaining accurate thermal conductivity is crucial to ensuring the reliable thermal insulation performance of resin-based composite materials, thereby guaranteeing the stability of the thermal protection system.

[0003] The determination of the thermal conductivity of resin-based composite materials generally includes experimental methods and numerical calculation methods.

[0004] Traditional experimental methods for testing thermal conductivity suffer from drawbacks such as long experimental cycles, large sample sizes, and stringent sample preparation requirements. Common methods for testing the thermal conductivity of thermal protection materials include the planar heat source method and the protective hot plate method. The planar heat source method requires sample sizes of 30×30×3mm or even larger, and necessitates two identical samples with smooth, flat surfaces. The protective hot plate method requires sample sizes of... Even larger samples require two identical pieces with smooth, flat surfaces. Furthermore, preparing thin-film samples of this size for brittle resin-based composite materials is challenging and wastes considerable material during processing. In reality, limitations in material preparation or acquisition methods often result in sample sizes that do not meet testing requirements, making it impossible to determine thermal conductivity and creating a bottleneck in the design of thermal protection materials.

[0005] Two key steps in numerically calculating thermal conductivity are microstructure characterization and modeling, and selecting a reliable model for calculation. The pore and particle structure of resin-based composites are considered direct factors affecting thermal conductivity; therefore, accurate characterization and modeling of the composite material are crucial to the accuracy of numerical thermal conductivity calculations. Currently, commonly used methods for constructing the microstructure of resin-based composites include artificial construction methods and microscopic CT scanning imaging. However, artificial construction methods cannot reflect the true state of the pores and particles within the material, while microscopic CT scanning imaging has low resolution and cannot obtain the morphology of nanoscale pores and particles, resulting in an incomplete understanding of the material's internal structure and an inaccurate model. In reality, the pore diameter distribution of resin-based composites is very wide, ranging from tens of nanometers to several micrometers, and the pore structure is complex, including unconnected pores, semi-connected pores, and connected pores. Therefore, characterizing the microstructure of resin-based composites is difficult, leading to inaccurate information about the internal pore structure and resulting in significant errors in the calculated thermal conductivity. Summary of the Invention

[0006] In view of this, the present invention provides a method for determining the thermal conductivity of resin-based composite materials in a multi-scale three-dimensional structure. This method can meet the needs of determining the thermal conductivity of small-sized samples, and can characterize the internal pore and particle structure information of resin-based composite materials at multiple scales. It can also perform multi-scale modeling and calculation of the overall effective thermal conductivity of the material under the premise of considering nanoscale effects, with high accuracy.

[0007] The method for determining the multi-scale three-dimensional structural thermal conductivity of resin-based composite materials of the present invention includes:

[0008] S1, The resin matrix of the composite material is scanned by a focused ion beam scanning electron microscope. The spatial resolution of the focused ion beam scanning electron microscope is better than 15nm, and the three-dimensional nanoporous structure of the resin matrix is ​​obtained. The average particle diameter and average pore diameter are calculated.

[0009] S2, based on the three-dimensional nanoporous structure parameters of the resin matrix obtained in S1, construct gas phase thermal conductivity models and solid phase thermal conductivity models that consider the confinement effect of gas nanoscale pores and nanoscale particles.

[0010] S3, based on the gas phase thermal conductivity model and solid phase thermal conductivity model constructed in S2, uses the lattice Boltzmann method to numerically simulate the heat transfer process of the three-dimensional nanoporous structure of the resin matrix obtained in S1, and calculates the effective thermal conductivity of the resin matrix.

[0011] S4. Perform micro-CT scanning on the resin-based composite material to obtain the three-dimensional pore structure of the resin-based composite material, and calculate the average particle diameter, average pore diameter and the spatial angle of each fiber.

[0012] S5: Based on the three-dimensional pore structure parameters of the resin-based composite material obtained in S4, a gas phase thermal conductivity model is constructed; the solid phase thermal conductivity is the effective thermal conductivity of the resin matrix obtained in S3, and based on the fiber space angle, the anisotropic thermal conductivity tensor of the fiber is calculated through coordinate transformation.

[0013] S6, based on the gas phase thermal conductivity model and solid phase thermal conductivity constructed in S5, uses the lattice Boltzmann method to numerically simulate the heat transfer process of the three-dimensional structure of the composite material obtained in S4, and calculates the effective thermal conductivity of the resin-based composite material.

[0014] Preferably, in S1 and S4, the two-dimensional slice grayscale image obtained by scanning is subjected to image processing and three-dimensional reconstruction to obtain a three-dimensional pore structure; the image processing includes: adjusting brightness-contrast, median filtering, threshold segmentation, noise removal and hole filling.

[0015] Ideally, Avizo software is used for image processing; ImageJ is used for pore analysis to calculate particle diameter and pore diameter.

[0016] Preferably, the gas phase thermal conductivity model in S2 and S5 adopts the ZENG gas phase thermal conductivity model or the gas phase thermal conductivity model based on Gauss pore size distribution.

[0017] The solid thermal conductivity model in S2 adopts the Bi & Tang solid thermal conductivity model.

[0018] A better ZENG vapor phase thermal conductivity model is:

[0019]

[0020] η = 0.461N g0 m g (8k B T / πm g ) 1 / 2 l m

[0021]

[0022] N g0 =n g / V∏=p / k B T

[0023] Where, k n,g Where T is the thermal conductivity of the gas phase, and T is the temperature. Let γ be the thermal conductivity of the gas in free space, γ = 1.4 be the adiabatic coefficient of the diatomic gas molecule, Pr be Prandtl's constant, α be the energy regulation coefficient in air, and l be the thermal conductivity of the gas in free space. ch l is the distance between heat exchange surfaces. mη is the mean free path of randomly colliding gas molecules; η is the viscosity, and c is the mean free path of the gas molecules. v For volumetric specific heat, N g0 m is the number density of gas molecules. g k is the mass of the gas. B S is the Boltzmann constant; s ρ is the specific surface area. por Let ρ be the density, π be the porosity, and d be the density. g n is the diameter of a gas molecule. g V is the number of gas molecules, V is the volume, and p is the pressure.

[0024] A better gas-phase thermal conductivity model based on Gaussian pore size distribution is:

[0025]

[0026] Where k(D,σ) is the thermal conductivity of the gas, N is the normalization constant, D' is the pore size, D is the average pore size, and σ is the standard deviation. Let be the thermal conductivity of the free gas. Here are assumed parameter values, C3 is an assumed material property constant, C3 = 1.55, l g It is the mean free path.

[0027] A better Bi&Tang solid-state thermal conductivity model is:

[0028]

[0029] Where λ0 is the solid-state thermal conductivity, and c v Let v0 be the volumetric specific heat capacity, v0 be the average sound velocity of a phonon, and Λ0 be the average phonon free path.

[0030] Λ V The mean free path of phonons scattered by the volumetric thermal resistance of phonons. λ bulk v is the thermal conductivity of the bulk material; bulk For solid materials, the velocity of sound;

[0031] Λ S The mean free path of phonons scattered at the boundary contact. a is the contact diameter of the interconnected particles, d p Where A is the particle diameter. inter and A sphere These are the interface area and the coverage area of ​​the spherical particles, respectively. eff =A sphere +2A inter ;

[0032] Λ T Λ is the mean free path of the phonons scattered at the interface.T =3t AB L / 2; t AB Let AB be the energy transfer rate, and under the assumption of a rough interface, t AB =0.5a 2 / dp 2 L is the distance between the centers of the two spherical particles.

[0033] The better ones also include:

[0034] S0: Perform a two-dimensional scanning electron microscope scan on the resin-based composite material to obtain the pore and particle size range. If the pore diameter is greater than 1 μm, the traditional micro-CT scanning imaging method is used to obtain the morphology of the internal voids and particles of the material, and then the thermal conductivity of the material is calculated by modeling. Otherwise, proceed to S1.

[0035] Preferably, in S0, the two-dimensional scanning electron microscope is an S-4800, JSM-7500F, or Sirion 200.

[0036] Preferably, in step S0, the pore and particle diameters are measured using a built-in program of a scanning electron microscope, or the pore and particle diameters are measured using ImageJ software on the scanning electron microscope image to obtain the pore and particle size range.

[0037] Beneficial effects:

[0038] (1) This invention first uses high-resolution focused ion beam scanning electron microscopy to obtain the three-dimensional nanoporous structure of the resin matrix of the composite material, and constructs gas-phase thermal conductivity and solid-phase thermal conductivity models considering the confinement effect of gas nanoscale pores and nanoscale particles. The effective thermal conductivity of the resin matrix is ​​calculated using the lattice Boltzmann method. Then, the three-dimensional structure of the resin-based composite material is obtained using micro-CT, a gas-phase thermal conductivity model is constructed, and the effective thermal conductivity of the resin matrix is ​​used as the solid-phase thermal conductivity. The effective thermal conductivity of the resin-based composite material is then calculated using the lattice Boltzmann method. This invention can obtain both micron-scale and nanoscale pore morphologies, realizing multi-scale pore structure characterization; the calculated effective thermal conductivity of the resin-based composite material is closer to the actual thermal conductivity, with higher accuracy.

[0039] (2) The focused ion beam scanning electron microscope (SEM) and micro-CT characterization methods used in this invention require very small sample sizes. The sample preparation size for SEM can reach 3×3×2 mm, or even smaller, which is only 1 / 150th the size of the sample tested using the planar heat source method. The scanning area size for SEM is approximately 10×10×10 μm, and the sample preparation size for micro-CT can reach... Therefore, it is possible to characterize the structure of small-sized samples and calculate their effective thermal conductivity. Attached Figure Description

[0040] Figure 1 This is a flowchart of the calculation method.

[0041] Figure 2 This is a schematic diagram of multi-scale structural characterization.

[0042] Figure 3 This is a schematic diagram of the sample dimensions.

[0043] Figure 4 Here is the SEM image of the sample.

[0044] Figure 5 This is the particle diameter distribution calculated from SEM images using Image J.

[0045] Figure 6 This is a grayscale image of a two-dimensional section obtained by a focused ion beam scanning electron microscope.

[0046] Figure 7 This is a grayscale image of a two-dimensional slice from a micro-CT scan.

[0047] Figure 8 Two-dimensional slice grayscale images were reconstructed from focused ion beam scanning electron microscopy (a) and micro-CT (b) scans to form three-dimensional images.

[0048] Figure 9 Boundary conditions for calculating the effective thermal conductivity using the lattice Boltzmann method. Detailed Implementation

[0049] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0050] This invention provides a method for determining the thermal conductivity of a multi-scale three-dimensional structure of a resin-based composite material, such as... Figure 1 As shown, it includes the following steps:

[0051] S1, Focused ion beam scanning electron microscopy is used to scan the resin matrix of the resin-based composite material.

[0052] Focused ion beam scanning electron microscopes (FEMs) can achieve resolutions up to several nanometers, effectively resolving the morphology of nanoscale pores and particles. In this invention, the resolution of the FEM must be better than 15 nm.

[0053] Image processing and 3D reconstruction of two-dimensional slice grayscale images obtained by focused ion beam scanning electron microscopy are performed, including steps such as adjusting brightness-contrast, median filtering, threshold segmentation, noise removal, filling pores, and reconstructing the two-dimensional image into a three-dimensional image, to obtain the three-dimensional nanoporous structure of the matrix.

[0054] Pore ​​structure analysis was performed on the processed focused ion beam scanning electron microscope images to calculate the average particle diameter, average pore diameter, pore diameter distribution, and porosity.

[0055] S2, based on the three-dimensional nanoporous structure parameters of the resin matrix obtained in S1, constructs gas phase thermal conductivity models and solid phase thermal conductivity models that consider the confinement effect of gas nanoscale pores and nanoscale particles.

[0056] The gas-phase thermal conductivity model can be either the ZENG gas-phase thermal conductivity model or a gas-phase thermal conductivity model based on Gaussian pore size distribution. The ZENG gas-phase thermal conductivity model considers the confinement effect of gas nanoscale pores and nanoscale particles during the modeling process; the gas-phase thermal conductivity model based on Gaussian pore size distribution can reflect the influence of the randomness and non-uniformity of pore distribution on gas-phase heat transfer, and can also reflect the confinement effect of gas nanoscale pores and nanoscale particles. The solid-phase thermal conductivity model can be the Bi&Tang solid-phase thermal conductivity model.

[0057] The ZENG gas-phase thermal conductivity model is as follows:

[0058]

[0059] N g0 =n g / VΠ=p / k B T

[0060] η = 0.461N g0 m g (8k B T / πm g ) 1 / 2 l m

[0061]

[0062] Where, k n,g The thermal conductivity is the vapor phase. Let γ be the thermal conductivity of the gas in free space, γ = 1.4 be the adiabatic coefficient of the diatomic gas molecule, Pr be Prandtl's constant, α be the energy regulation coefficient in air, and l be the thermal conductivity of the gas in free space. ch l is the distance between heat exchange surfaces. m S is the mean free path of randomly colliding gas molecules; s ρ is the specific surface area. por Π is density, Π is porosity; N g0 n is the number density of gas molecules. g V is the number of gas molecules, and m is the volume. g For the mass of the gas, c v K is the volumetric specific heat capacity. B Where d is Boltzmann's constant, T is temperature, and d is the constant. g η is the diameter of the gas molecule; p is the pressure; η is the viscosity. g0is the thermal conductivity of gas in free space.

[0063] If the pore size distribution in the resin matrix satisfies the Gaussian distribution, then the gas thermal conductivity can be modeled using the gas phase thermal conductivity model based on the Gaussian pore size distribution.

[0064]

[0065] Where k(D,σ) is the thermal conductivity of the gas, N is the normalization constant, D' is the pore size, D is the average pore size, and σ is the standard deviation. Let be the thermal conductivity of the free gas. Here are assumed parameter values, C3 is an assumed material property constant, C3 = 1.55, l g It is the mean free path.

[0066] The Bi&Tang solid-state thermal conductivity model is as follows:

[0067]

[0068] Where λ0 is the solid-state thermal conductivity, and c v Here, v0 is the volumetric specific heat capacity, v0 is the average sound speed of a phonon, and Λ0 is the average phonon free path.

[0069] Phonon mean free path Λ due to phonon volume thermal resistance V for

[0070] Mean free path Λ of phonons scattered at boundary contact S for A eff =A sphere +2A inter ;

[0071] Mean free path Λ of phonons scattered at the interface T For Λ T =3t AB L / 2;

[0072] Under the assumption of a rough interface, t AB (AB energy transfer rate) can be expressed as t AB =0.5a 2 / d p 2 ;

[0073] Where, λ bulk v is the thermal conductivity of the bulk material; bulk Let A be the sound velocity in a solid material (taken as 4000 m / s); inter and A sphereThese represent the interface area and the coverage area of ​​the spherical particles, respectively; 'a' is the contact diameter of the interconnecting particles, and 'd' is the contact area of ​​the spherical particles. p Let L be the particle diameter, and L be the distance between the centers of the two spherical particles.

[0074] S3 uses the lattice Boltzmann method to numerically simulate the heat transfer process of the three-dimensional nanoporous structure of the resin matrix obtained in S1, and calculates the effective thermal conductivity of the resin matrix.

[0075] For the three-dimensional nanoporous structure of the resin matrix obtained in S1, and the gas-phase thermal conductivity model and solid-phase thermal conductivity model constructed in S2, based on the three-dimensional steady-state energy conservation equation... Calculate the effective thermal conductivity, where k c The thermal conductivity of a cubic element is... Here, T represents the temperature, and a three-dimensional gradient operator is used. A multi-space discrete-direction multi-relaxation lattice Boltzmann heat conduction model is employed, and MPI parallel programming is carried out. The heat transfer process of the resin matrix is ​​simulated using a multi-relaxation-time D3Q7 model, yielding the effective thermal conductivity k of the resin matrix. eff，1 .

[0076] S4, Micro-CT scan of resin-based composite materials.

[0077] Micro-CT scanning has a maximum resolution of 600nm, which can distinguish pore morphology larger than 1µm.

[0078] Image processing and 3D reconstruction of two-dimensional slice grayscale images obtained by micro-CT scanning are performed, including steps such as adjusting brightness-contrast, median filtering, threshold segmentation, noise removal, filling holes, and reconstructing the two-dimensional image into a three-dimensional image, to obtain the three-dimensional porous structure of resin-based composite materials.

[0079] The pore structure of the processed micro-CT images was analyzed to calculate the average particle diameter, average pore diameter, pore diameter distribution, porosity, and spatial angle of each fiber.

[0080] S5. Based on the three-dimensional pore structure of the resin-based composite material obtained in S4, construct its gas phase thermal conductivity model. The gas phase thermal conductivity model can be constructed in the same way as in S2.

[0081]

[0082] N g0 =n g / VΠ=p / k B T

[0083] η = 0.461N g0 m g (8k B T / πm g )1 / 2 l m

[0084]

[0085] Where, k n,g The thermal conductivity is the vapor phase. Let γ be the thermal conductivity of the gas in free space, γ = 1.4 be the adiabatic coefficient of the diatomic gas molecule, Pr be Prandtl's constant, α be the energy regulation coefficient in air, and l be the thermal conductivity of the gas in free space. ch l is the distance between heat exchange surfaces. m S is the mean free path of randomly colliding gas molecules; s ρ is the specific surface area. por Π is density, Π is porosity; N g0 n is the number density of gas molecules. g V is the number of gas molecules, and m is the volume. g For the mass of the gas, c v For volumetric specific heat capacity, k B Where d is Boltzmann's constant, T is temperature, and d is the constant. g η is the diameter of the gas molecule; p is the pressure; η is the viscosity. g0 The thermal conductivity of gas in free space;

[0086] If the pore size distribution in the composite material is Gaussian, then the gas thermal conductivity is:

[0087]

[0088] Where N is the normalization constant, D' is the pore size, D is the average pore size, and σ is the standard deviation. Let be the thermal conductivity of the free gas. Here are assumed parameter values, C3 is an assumed material property constant, C3 = 1.55, l g It is the mean free path.

[0089] The solid thermal conductivity of the composite material is S3, and the effective thermal conductivity k is calculated based on the three-dimensional image modeling of focused ion beam scanning electron microscopy. eff，1 Based on the fiber space angle, the anisotropic thermal conductivity tensor of the fiber is calculated through coordinate transformation.

[0090] S6. The heat transfer process of the three-dimensional composite material structure obtained in S4 was numerically simulated using the lattice Boltzmann method, and the effective thermal conductivity of the resin-based composite material was calculated.

[0091] For the three-dimensional composite material structure obtained in S4, the model was first divided into a 10×10×10μm mesh. Then, based on the gas phase thermal conductivity model and solid phase thermal conductivity constructed in S5, the effective thermal conductivity was calculated using the three-dimensional steady-state energy conservation equation. MPI parallel programming was performed using a multi-space discrete direction multi-relaxation lattice Boltzmann heat conduction model, and the heat transfer process of the composite material was simulated using a multi-relaxation time D3Q7 model to obtain the effective thermal conductivity k. eff .

[0092] k eff This is the effective thermal conductivity of the final output resin-based composite material.

[0093] To improve experimental efficiency and simplify the experimental procedure, before performing S1, the present invention can first use a two-dimensional scanning electron microscope to perform a preliminary scan on the resin-based composite material to determine the approximate range of pore diameters. If the pore diameters are all greater than 1 μm, the morphology of the internal voids and particles of the material can be obtained by using the traditional micro-CT scanning imaging method, and then the effective thermal conductivity of the material can be modeled and calculated. If there are many pores with diameters less than 1 μm, or even less than 50 nm, then S1 is performed, and a high-resolution focused ion beam scanning electron microscope is used to obtain the nanoscale pore morphology.

[0094] The resolution requirement for the 2D scanning electron microscope (SEM) is not high here; a general SEM such as the S-4800, JSM-7500F, or Sirion200 can be used to obtain a preliminary range of pore and particle sizes. The pore and particle diameters (more than 20 sets) can be measured using the built-in program of the 2D SEM to roughly determine the pore diameter range and calculate the average particle diameter. Alternatively, ImageJ software can be used to measure the pore and particle diameters (more than 20 sets) from the SEM images and calculate the average particle diameter.

[0095] Example 1

[0096] (1) Observe the resin-based composite material with a scanning electron microscope (S-4800) to roughly determine the pore and particle size range. If the pore diameter is greater than 1 μm, the traditional micro-CT scanning imaging method can be used to obtain the pore structure and model and calculate the thermal conductivity; if there are many pores with a diameter less than 1 μm, or even less than 50 nm, then proceed to step (2).

[0097] (2) The particle diameter was calculated using ImageJ on the scanning electron microscope image from step (1). The calculated average particle diameter d n It is 112nm.

[0098] (3) The resin matrix of the resin-based composite material was scanned by focused ion beam scanning electron microscopy (Crossbeam550).

[0099] Among them, the sample preparation size of the resin matrix of the resin-based composite material is 3×3×2mm (including but not limited to this size), the scanning size is about 10×10×10um (including but not limited to this size), and the resolution can reach several nanometers to distinguish the morphology of nanoscale pores and obtain two-dimensional slice grayscale images.

[0100] (4) The Avizo software was used to process the grayscale image of the two-dimensional slice of resin matrix and perform three-dimensional reconstruction, including adjusting brightness-contrast, median filtering, threshold segmentation, noise removal, hole filling and reconstructing the two-dimensional image into a three-dimensional image.

[0101] (5) ImageJ was used to perform porosity analysis on the focused ion beam scanning electron microscope image processed in step (4), and the pore diameter distribution and porosity were calculated. The calculated porosity was 50.62%, and the average pore diameter d was... n,p It is 90.86nm.

[0102] (6) Construct the ZENG gas phase thermal conductivity model and the Bi&Tang solid phase thermal conductivity model of the resin matrix; use the lattice Boltzmann method to numerically simulate the heat transfer process of the three-dimensional nanoporous structure of the resin matrix reconstructed in (4) and calculate the effective thermal conductivity of the resin matrix.

[0103] The boundary conditions are as follows:

[0104] T(x,y,0)=T h

[0105] T(x,y,L)=T c

[0106]

[0107] Where T h The temperature on the high-temperature side, T c This refers to the temperature on the low-temperature side.

[0108] According to the three-dimensional steady-state energy conservation equation Calculate the effective thermal conductivity, where k c The thermal conductivity of a cubic element is... Here, T is a three-dimensional gradient operator, and T is the temperature.

[0109] Using a multi-spatial discrete-direction multi-relaxation lattice Boltzmann heat conduction model, MPI parallel programming was performed, and a multi-relaxation-time D3Q7 model was used to simulate the heat transfer process of the resin matrix, obtaining the effective thermal conductivity k. eff，1 .

[0110] (7) Perform micro-CT scans on resin-based composite materials.

[0111] The sample preparation size of the resin-based composite material is (Including but not limited to this size), the resolution of micro-CT scanning is up to 600nm to resolve pore morphology larger than 1μm and obtain two-dimensional slice grayscale images.

[0112] (8) Avizo software was used to process and reconstruct the two-dimensional grayscale image of resin-based composite material slices in three dimensions, including adjusting brightness and contrast, median filtering, threshold segmentation, noise removal, hole filling, and reconstructing the two-dimensional image into a three-dimensional image.

[0113] (9) Image J was used to analyze the pore structure of the processed micro-CT images, and the average pore diameter, pore diameter distribution, and porosity were calculated. The calculated porosity was 22.52%, and the average pore diameter was 2.68 μm.

[0114] (10) Construct the ZENG gas phase thermal conductivity model of the resin-based composite material; the solid phase thermal conductivity is taken as the effective thermal conductivity k calculated in (6). eff，1 Based on the fiber space angle, the anisotropic thermal conductivity tensor of the fiber is calculated through coordinate transformation.

[0115] The model was divided into a grid of size 10×10×10μm. The heat transfer process of the reconstructed three-dimensional structure of the resin-based composite material was numerically simulated using the lattice Boltzmann method (8) to calculate the overall effective thermal conductivity of the resin-based composite material.

[0116] The boundary conditions are as follows:

[0117] T(x,y,0)=T h

[0118] T(x,y,L)=T c

[0119]

[0120] Where T h The temperature on the high-temperature side, T c This refers to the temperature on the low-temperature side.

[0121] The effective thermal conductivity is also calculated using the three-dimensional steady-state energy conservation equation.

[0122] MPI parallel programming was performed using a multi-spatial discrete-direction multi-relaxation lattice Boltzmann heat conduction model. The heat transfer process of the composite material was simulated using a multi-relaxation-time D3Q7 model, yielding the final effective thermal conductivity k. eff .

[0123] The effective thermal conductivity k of the resin-based composite material calculated in step (10) effThe thermal conductivity is 0.612 W / m·K, which is only 5.9% different from the thermal conductivity (0.578 W / m·K) measured by the planar heat source method in the experiment.

[0124] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining the thermal conductivity of a multiscale three-dimensional structure of a resin-based composite material, characterized in that, Comprise: S1, scanning the resin matrix of the composite material by focused ion beam scanning electron microscope, the spatial resolution of the focused ion beam scanning electron microscope is better than 15 nm, obtaining the three-dimensional nanopore structure of the resin matrix, calculating the average particle diameter, the average pore diameter; S2, based on the three-dimensional nanopore structure parameters of the resin matrix obtained in S1, constructing the gas phase thermal conductivity coefficient model and the solid phase thermal conductivity coefficient model considering the confinement effect of gas nanoscale pores and nanoscale particles; S3, based on the gas phase thermal conductivity coefficient model and the solid phase thermal conductivity coefficient model constructed in S2, using lattice Boltzmann method to numerically simulate the heat transfer process of the three-dimensional nanopore structure of the resin matrix obtained in S1, and calculating the effective thermal conductivity coefficient of the resin matrix; S4, scanning the resin-based composite material by micro-CT, obtaining the three-dimensional pore structure of the resin-based composite material, calculating the average particle diameter, the average pore diameter and the spatial angle of each fiber; S5, based on the three-dimensional pore structure parameters of the resin-based composite material obtained in S4, constructing the gas phase thermal conductivity coefficient model; the solid phase thermal conductivity coefficient is the effective thermal conductivity coefficient of the resin matrix obtained in S3, and based on the spatial angle of the fiber, the anisotropic thermal conductivity coefficient tensor of the fiber is calculated through coordinate transformation; S6, based on the gas phase thermal conductivity coefficient model and the solid phase thermal conductivity coefficient constructed in S5, using lattice Boltzmann method to numerically simulate the heat transfer process of the three-dimensional structure of the composite material obtained in S4, and calculating the effective thermal conductivity coefficient of the resin-based composite material.

2. The method of claim 1, wherein, In S1 and S4, the two-dimensional slice gray scale obtained by scanning is processed by image processing and three-dimensional reconstruction to obtain the three-dimensional pore structure; the image processing includes adjusting brightness-contrast, median filtering, threshold segmentation, removing noise points and filling holes.

3. The method of claim 2, wherein, Avizo software is used for image processing; image J is used for pore analysis to calculate the particle diameter and pore diameter.

4. The method of claim 1, wherein, The gas phase thermal conductivity coefficient model in S2 and S5 adopts ZENG gas phase thermal conductivity coefficient model or gas phase thermal conductivity coefficient model based on Gauss pore size distribution; The solid phase thermal conductivity coefficient model in S2 adopts Bi&Tang solid phase thermal conductivity coefficient model.

5. The method of claim 4, wherein, ZENG gas phase thermal conductivity coefficient model is: wherein, is the thermal conductivity of the gas phase, is the temperature, is the thermal conductivity of the free gas, is the Prandtl number, is the energy accommodation coefficient for air, is the distance between the heat exchange surfaces, is the mean free path of randomly colliding gas molecules; is the viscosity, is the volumetric specific heat capacity, is the number density of gas molecules, is the gas mass, is the Boltzmann constant; is the specific surface area, is the density, is the porosity, is the diameter of the gas molecules; is the number of gas molecules, is the volume, is the pressure.

6. The method of claim 4, wherein, The gas phase thermal conductivity coefficient model based on Gauss pore size distribution is: wherein, is the thermal conductivity of the gas, is a normalization constant, D ` is the pore diameter, is the average pore diameter, is the standard deviation, is the thermal conductivity of the free gas, is the assumed parameter value, is the assumed material property constant, , is the mean free path.

7. The method of claim 4, wherein, Bi&Tang solid phase thermal conductivity coefficient model is: wherein, is the solid phase thermal conductivity, cv is the volumetric specific heat capacity, is the average phonon speed, is the average phonon free path, ; Λ V the mean free path of the phonons for scattering caused by the phonon bulk thermal resistance, ; the thermal conductivity of the bulk material; the solid sound velocity of the material; Λ S is the average free path of a boundary contact scattered phonon, ; a is the contact diameter of an interlinked particle, is the particle diameter, and are the interfacial area and the surface area of a spherical particle, respectively, ; Λ T the mean free path of the interface contact scattering phonons, ; the A-B energy transfer rate, under the assumption of a rough interface, ; L is the distance between the centers of the two spherical particles.

8. The method of any one of claims 1 to 7, wherein, Also include: S0, scanning the resin-based composite material by two-dimensional scanning electron microscope to obtain the size range of pores and particles, if the pore diameter is greater than 1 μm, the traditional micro-CT scanning imaging method is used to obtain the morphology of the internal voids and particles of the material, and then the material thermal conductivity is calculated; otherwise, S1 is executed.

9. The method of claim 8, wherein, In S0, the two-dimensional scanning electron microscope adopts S-4800, JSM-7500F or Sirion 200.

10. The method of claim 8, wherein, In S0, the pore and particle diameters are measured by the built-in program of the scanning electron microscope, or the pore and particle diameters are measured by Image J software on the scanning electron microscope image to obtain the size range of pores and particles.

Images