A method for predicting the thermal conductivity of an anisotropic silicon oxide nanowire aerogel
By constructing a randomly entangled spline curve skeleton through 3D modeling and multiphysics simulation, the distortion problem in predicting the thermal conductivity of silica nanowire aerogels was solved, and efficient and accurate thermal conductivity prediction and material optimization were achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XI AN JIAOTONG UNIV
- Filing Date
- 2026-03-16
- Publication Date
- 2026-06-23
AI Technical Summary
Existing technologies for predicting the thermal conductivity of silica nanowire aerogels use simplified models that lead to distorted results, failing to reflect the anisotropy of the material and the heat transfer path. Furthermore, the experimental methods are time-consuming and costly, and cannot effectively guide material development.
A three-dimensional model based on scanning electron microscope images is used to construct a randomly entangled spline curve skeleton. Combined with solid scanning and multiphysics simulation, the axial and radial thermal conductivity are calculated respectively, and a porous medium heat transfer module is used for accurate prediction.
The thermal conductivity of silica nanowire aerogel was simulated with high fidelity with an error of less than 3%, which significantly shortened the research and development cycle, improved the efficiency of material research and development, and provided a theoretical basis for the design of directional thermal insulation materials.
Smart Images
Figure CN122263406A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of thermal insulation material testing technology, specifically relating to a method for predicting the thermal conductivity of anisotropic silica nanowire aerogel. Background Technology
[0002] Silica nanowire aerogels typically refer to nanoporous materials with a three-dimensional network structure formed by the entanglement and cross-linking of silica nanowires as basic building blocks. Due to their excellent mechanical properties, outstanding thermal insulation performance, and extremely low thermal conductivity, silica nanowire aerogels are considered a new generation of high-efficiency thermal insulation materials. Currently, to obtain the thermal conductivity of silica nanowire aerogels, traditional experimental preparation and measurement methods are commonly used, involving complex chemical processes such as sol-gel, aging and drying, chemical vapor deposition, or electrospinning. This entire process usually takes more than five days to obtain a usable sample. Measurements are then performed using equipment such as laser flare thermal conductivity meters. While this method yields relatively reliable results, it heavily relies on the complete preparation of the sample, and the thermal conductivity meter requires damaging the sample during measurement, causing irreversible loss. To overcome the bottleneck of experimental methods, computational simulation methods have been introduced to predict the thermal conductivity of silica nanowire aerogels.
[0003] However, existing simulation techniques for modeling nanowire aerogels generally employ oversimplified physical models, leading to severely distorted predictions and failing to effectively guide experiments. Existing simplified models include: Homogenization model: This model treats aerogels as a uniform porous continuous medium, describing them only using equivalent thermal conductivity or porosity parameters. This model completely ignores the fact that nanowire aerogels are discrete, intertwined spatial network topologies, and therefore cannot reflect the meandering paths of heat flow in the actual framework.
[0004] Regularized geometric models: These models use periodically arranged regular geometric shapes (such as straight cylinders or arrays of cubes) to simulate the skeleton. While this model introduces a solid structure, it cannot depict the highly curved, randomly oriented, and entangled three-dimensional disordered morphology of real nanowire aerogels as seen under a scanning electron microscope (SEM).
[0005] Therefore, existing methods for measuring the thermal conductivity of silica nanowire aerogels have the following drawbacks: The research and development cycle is long and the cost is high: the actual synthesis process of silica nanowire aerogels is extremely complex and time-consuming, and it takes a long time to obtain samples that can be used for measurement. This means that after each adjustment of the formula or structure, a long wait is required to verify its thermal properties, which seriously restricts the efficiency of material research and development.
[0006] Structural distortion leads to loss of anisotropy: Existing regular or homogeneous models are isotropic and cannot simulate the structural differences of aerogels in the axial and radial directions. In fact, silica nanowire aerogels form specific orientations and entanglements during formation, resulting in drastically different heat transfer paths in different directions. Summary of the Invention
[0007] This invention provides the following technical solution: a method for predicting the thermal conductivity of anisotropic silica nanowire aerogels, comprising the following steps: Step S1: Extraction of feature parameters based on microstructure: The scanning electron microscope image of silica nanowire aerogel is used as the modeling benchmark.
[0008] Step S2: Construct a randomly entangled spline curve skeleton: Use 3D modeling software to generate a parametric skeleton.
[0009] Step S3: Solid scanning and anisotropic cell establishment.
[0010] Step S4: Multiphysics simulation based on solid model.
[0011] Preferably, in step S1, the cell diameter of the subsequent model is determined based on the aerogel interlayer spacing, and the volume fraction per unit volume of the nanowires is calculated based on the overall density, the density of the pure nanowire framework, and the air density. The density and diameter of the spline curves are controlled using nanowire aerogel characteristic parameters (such as interlayer spacing and volume fraction), and a randomly entangled nanowire entity network with a true volume fraction is generated by scanning a circular cross-section. This is the foundation for solving model distortion and achieving high-fidelity simulation.
[0012] More preferably, step S2 specifically includes: generating a cylindrical surface with the unit cell spacing determined in step S1 as the diameter of the unit cell; making a 3D sketch on the cylindrical surface and using spline curves as basic units; randomly drawing multiple spline curves with different control points in space to simulate the bending, winding and disordered stacking state of nanowires during the growth process, so that the nanowires have an interwoven but irregularly connected wireframe network.
[0013] Preferably, in step S3, the solid scanning includes: contour scanning, setting the cross-section of the nanowire to be circular, scanning the circular contour along the generated random spline curve to generate a 3D solid nanowire with real volume and diameter.
[0014] In anisotropic unit cells, anisotropy is defined as follows: the direction perpendicular to the upper and lower surfaces of the cylinder is defined as the axial direction of the aerogel, and the direction parallel to the upper and lower surfaces of the cylinder is defined as the radial direction of the aerogel. By combining a microscale model with heat transfer in porous media, directional heat flux boundary conditions are applied in both the axial and radial directions to achieve independent calculation and accurate prediction (error <3%) of the anisotropic thermal conductivity of aerogel materials, filling the gap in existing models that cannot accurately distinguish directional differences.
[0015] Preferably, step S4 specifically includes: importing the solid assembly generated by the 3D modeling software into the simulation software; using the porous medium heat transfer module, applying heat flux boundary conditions consistent with those of the laser heat conduction instrument to different surfaces of the cuboid cell; calculating the axial temperature difference and radial temperature difference respectively; and verifying mesh independence to obtain the temperature difference between the two surfaces. T; The simulated thermal conductivity of the aerogel is:
[0016] in, Simulated thermal conductivity of aerogel; Input heat flux; The thickness of heat transfer. This invention is not only used for prediction but also for reverse optimization of the synthesis process. By adjusting the modeling parameters (nanowire diameter, space ratio, aspect ratio, etc.) and the simulation boundary conditions, the mapping relationship between microstructure parameters and thermal conductivity is obtained. Using this mapping relationship, the optimal microstructure that satisfies the target thermal properties is determined. Based on the optimal microstructure parameters, key process parameters in the actual synthesis (such as nanowire addition amount, precursor concentration, or freezing orientation rate) are derived and adjusted in reverse, thereby locking in the optimal process window before the experiment.
[0017] Preferably, the 3D modeling software includes SolidWorks, Pro / E, UG, and CATIA, and the simulation software includes COMSOL Multiphysics, ANSYS, and Abaqus.
[0018] Preferably, the simulation software has the functions of porous medium heat transfer and microscale correction.
[0019] The beneficial effects of this invention are: 1. High-fidelity microstructure reproduction of this invention: Compared to simplified models, this invention utilizes spline curve solid scanning technology to replicate the highly entangled random characteristics of nanowires within the aerogel. This solid model can realistically reflect the bending and transmission path of heat flow in the nanowire framework and the random contact thermal resistance between fibers, solving the "distortion" problem of traditional models.
[0020] 2. Decoupling and Prediction of Anisotropic Properties in this Invention: The model of this invention constructs anisotropy at the microscopic scale, enabling independent calculation of axial and radial thermal conductivity. Simulation results (axial ~66.02 mW·m) -1 ·K -1 Radial ~49.79 mW·m -1 ·K -1 It accurately quantifies the directional differences of materials, providing a theoretical basis for the design of directional thermal insulation materials.
[0021] 3. This invention replaces long-cycle experiments, improving R&D efficiency several times over: Existing technologies require complex processes such as aging, aging-drying, chemical vapor deposition, or electrospinning to synthesize silica nanowire aerogels, taking more than 5 days. The method of this invention can complete the process from modeling to performance data output in just a few hours, with errors controlled below 3%, greatly accelerating the screening of material formulations and the optimization of structures. Attached Figure Description
[0022] Figure 1 This is a scanning electron microscope (SEM) image of the microstructure of silica nanowire aerogel in an embodiment of the method for predicting the thermal conductivity of anisotropic silica nanowire aerogel according to the present invention. Figure 2 This is a schematic diagram of a randomly entangled nanowire skeleton model constructed based on spline curves in an embodiment of the present invention; Figure 3 This is a schematic diagram of the mesh generation of the solid model in an embodiment of the present invention; Figure 4 This is a temperature field distribution cloud map for axial and radial heat conduction simulation in an embodiment of the present invention; Figure 5 In this embodiment of the invention, after verification of mesh independence, the axial and radial thermal conductivity values are compared with the actual values and the error diagram for different mesh numbers; Figure 6 This is a schematic diagram of the method steps of the present invention. Detailed Implementation
[0023] The relevant technologies of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0024] like Figures 1-6As shown, the method for predicting the thermal conductivity of anisotropic silica nanowire aerogels in this embodiment achieves rapid and non-destructive prediction of the aerogel's thermal properties by constructing anisotropic microscopic topological networks. The specific implementation steps are as follows: Step S1: Extraction of feature parameters based on microstructure: Instead of using idealized parameters, scanning electron microscopy (SEM) images of silica nanowire aerogels are used (see attached image). Figure 1 The aerogel unit cell spacing is used as the modeling benchmark. The cell diameter of the subsequent model is determined based on the aerogel unit cell spacing. Based on the overall density, the density of the pure nanowire framework, and the air density, the volume fraction of the nanowires per unit volume is accurately calculated. This volume fraction will serve as the core constraint parameter when generating the subsequent solid model, ensuring that the porosity of the model is consistent with the real material.
[0025] Step S2: Construct a randomly entangled spline curve skeleton: Parametric skeleton generation was performed using 3D modeling software (SolidWorks). A cylindrical surface was generated with the interlayer spacing determined in step S1 as the diameter of the unit cell. Abandoning the traditional method of generating two-dimensional surfaces, a 3D sketch was created on the cylindrical surface, using splines as the basic unit. Multiple splines with different control points were randomly drawn in space to simulate the bending, entanglement, and disordered stacking of nanowires during growth, resulting in an interwoven but irregularly connected wireframe network. (See appendix) Figure 2 ) Step S3: Solid scanning and anisotropic unit cell establishment: Contour scanning: Set the cross-section of the nanowire to a standard circle, and scan the circular contour along the random spline curve generated above to generate a 3D solid nanowire with real volume and diameter.
[0026] Anisotropy is defined as follows: the direction perpendicular to the upper and lower surfaces of the cylinder is defined as the axial direction of the aerogel, and the direction parallel to the upper and lower surfaces of the cylinder is defined as the radial direction. This design allows for the clear capture of the differences in heat transfer along the long path (axial) and short path (radial) due to the different fiber orientations in subsequent simulations.
[0027] Step S4: Multiphysics simulation based on solid model: Import the solid assembly generated by SolidWorks directly into COMSOL Multiphysics.
[0028] Using a porous medium heat transfer module, heat flux boundary conditions (2.12 × 10⁻⁶) consistent with those of a laser heat conduction instrument were applied to different surfaces of a cuboid cell. 8 W / m 2The axial and radial temperature differences were calculated separately, and mesh independence was verified (see Appendix). Figure 3 ), to obtain the temperature difference between the two sides T. Attached Figure 4 The COMSOL calculation results for the radial and axial directions of the aerogel are presented.
[0029]
[0030] in, Simulated thermal conductivity of aerogel; Input heat flux; The thickness for heat transfer.
[0031] To verify mesh independence and isolate the influence of mesh density on the results, this invention constructed five different mesh numbers to simulate four different models. The axial and radial thermal conductivity values and error data for different mesh numbers are attached. Figure 5 As shown in Appendix 1.
[0032]
[0033] Alternatives to the present invention include: Modeling software alternatives: Geometric modeling can use other CAD software such as Pro / E, UG, and CATIA, as long as it can perform random entity scanning based on spline curves; simulation software can use ANSYS, Abaqus, etc., as long as it has the function of porous medium heat transfer and microscale correction.
[0034] Cell shape substitution: The shape of aerogel unit cell can be adjusted to a cylinder or other irregular prism, as long as it can reflect anisotropy.
[0035] Material substitution: This method is also applicable to the modeling and simulation of other types of nanowire aerogels (such as carbon nanotube aerogels, titanium dioxide aerogels, etc.).
[0036] In summary, this invention significantly improves prediction accuracy and applicability by introducing porous media heat transfer and microscale correction functions. This method not only accurately reflects the complex heat transfer mechanism within nanowire aerogels but also provides a reliable basis for optimizing material design. Furthermore, by flexibly adjusting different boundary conditions, the nonlinear relationship between thermal properties and structural parameters can be further explored, thereby enabling the development of more efficient thermal insulation materials. This technical approach has strong versatility and can be widely applied to the research of other porous material systems, promoting the transformation of related fields from traditional experiments to digital simulation.
[0037] It should be emphasized that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.
Claims
1. A method for predicting the thermal conductivity of anisotropic silica nanowire aerogels, characterized in that, Includes the following steps: Step S1: Extraction of feature parameters based on microstructure: The scanning electron microscope image of silica nanowire aerogel is used as the modeling benchmark; Step S2: Construct a randomly entangled spline curve skeleton: Generate a parametric skeleton using 3D modeling software; Step S3: Solid scanning and anisotropic unit cell establishment; Step S4: Multiphysics simulation based on solid model.
2. The method for predicting the thermal conductivity of anisotropic silica nanowire aerogel according to claim 1, characterized in that, In step S1, the cell diameter of the subsequent model is determined based on the aerogel unit cell spacing, and the volume ratio per unit volume of the nanowire is calculated based on the overall density, the density of the pure nanowire framework, and the air density.
3. The method for predicting the thermal conductivity of anisotropic silica nanowire aerogel according to claim 2, characterized in that, Step S2 specifically includes: generating a cylindrical surface with the unit cell spacing determined in step S1 as the diameter of the unit cell; making a 3D sketch on the cylindrical surface and using spline curves as the basic unit; randomly drawing multiple spline curves with different control points in space to simulate the bending, winding and disordered stacking state of nanowires during the growth process, so that the nanowires have an interwoven but irregularly connected wireframe network.
4. The method for predicting the thermal conductivity of anisotropic silica nanowire aerogel according to claim 1, characterized in that, In step S3, the solid scanning includes: contour scanning, setting the cross-section of the nanowire to be circular, scanning the circular contour along the generated random spline curve to generate a 3D solid nanowire with real volume and diameter; In the anisotropic unit cell, the definition of anisotropy includes: the direction perpendicular to the upper and lower surfaces of the cylinder is defined as the axial direction of the aerogel, and the direction parallel to the upper and lower surfaces of the cylinder is defined as the radial direction of the aerogel.
5. The method for predicting the thermal conductivity of anisotropic silica nanowire aerogel according to claim 1, characterized in that, Step S4 specifically includes: importing the solid assembly generated by the 3D modeling software into the simulation software; applying heat flux boundary conditions consistent with those of the laser heat conduction instrument to different surfaces of the cuboid cell using the porous medium heat transfer module; calculating the axial temperature difference and radial temperature difference respectively; and verifying the mesh independence to obtain the temperature difference between the two surfaces. T; The simulated thermal conductivity of the aerogel is: in, Simulated thermal conductivity of aerogel; Input heat flux; The thickness for heat transfer.
6. The method for predicting the thermal conductivity of anisotropic silica nanowire aerogel according to claim 5, characterized in that, The 3D modeling software includes SolidWorks, Pro / E, UG, and CATIA, while the simulation software includes COMSOL Multiphysics, ANSYS, and Abaqus.
7. The method for predicting the thermal conductivity of anisotropic silica nanowire aerogel according to claim 5, characterized in that, The simulation software has the functions of porous medium heat transfer and microscale correction.