Method and device for calculating thermal conductivity of inorganic non-metallic material, and electronic equipment
By using parallel and Levy models to classify and combine composite inorganic non-metallic materials, the problem of poor adaptability of traditional models is solved, and higher accuracy in predicting thermal conductivity is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INSTITUTE OF PROCESS ENGINEERING CHINESE ACADEMY OF SCIENCES
- Filing Date
- 2023-04-20
- Publication Date
- 2026-07-14
AI Technical Summary
Existing methods for predicting the thermal conductivity of composite inorganic nonmetallic materials lack applicability and accuracy, and traditional models cannot effectively analyze the thermal conductivity of multi-component materials.
The components of the composite inorganic nonmetallic material were classified, combined, and calculated using a parallel model and a Levy model. The volume fraction was determined by combining X-ray diffraction analysis and Archimedes' displacement method. The thermal conductivity was calculated using the parallel model and the Levy model.
It improves the accuracy of thermal conductivity prediction, reducing the error range to below 10%, and is suitable for more complex composition conditions.
Smart Images
Figure CN116469492B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of material performance prediction and calculation, specifically relating to a method for calculating the thermal conductivity of materials, and more particularly to a method, apparatus and electronic equipment for calculating the thermal conductivity of inorganic non-metallic materials. Background Technology
[0002] With the deepening research on inorganic non-metallic materials, single-phase materials are no longer sufficient to meet the needs of practical applications, and material development is moving towards diversification and composite materials. Composite materials have gradually attracted attention due to their rich and unique properties. Unlike single-component materials that have stable thermophysical properties, composite inorganic non-metallic materials have complex compositions and significant instability. Therefore, it is impossible to rely on a single basic model for theoretical calculations and analyses. Thus, it is necessary to study the calculation and prediction of the thermal conductivity of composite inorganic non-metallic materials.
[0003] Currently, the thermal conductivity of composite inorganic nonmetallic materials is mainly obtained through direct laboratory measurements, with common methods including laser scintillation and the plate method. Related theoretical calculations and predictive studies are relatively limited. Commonly used predictive models include series models, parallel models, Maxwell-Okun models, and Krischer models. These models are mostly applied individually to predict and calculate the thermal conductivity of two-phase materials, lacking analytical research specifically for the thermal conductivity of composite inorganic nonmetallic materials.
[0004] CN103115942A discloses a multi-scale prediction method for the thermal conductivity of hardened ordinary cement paste. This method divides the microstructure and composition of hardened ordinary cement paste into multiple phases and scales, then calculates the volume percentage of different phases, and finally calculates the thermal conductivity using a generalized self-consistent method. While this method divides hardened ordinary cement paste at a microscopic level, it is not applicable to general multi-component materials due to the relatively stable composition of ordinary hardened cement.
[0005] CN114935583A discloses a method for evaluating the thermal conductivity of multiphase polyurethane composite aggregates. This method is based on a traditional series-parallel model, which substitutes the apparent density and water absorption rate of rigid polyurethane materials into the model to calculate its thermal conductivity. However, this method requires fitting a function between the apparent density and water absorption rate of the polyurethane material and cannot be directly applied to the direct prediction of the thermal conductivity of other multiphase materials.
[0006] Accurate prediction and analysis of thermal conductivity is of great significance for the composition design and performance optimization of multiphase materials. Therefore, it is particularly important to seek thermal conductivity prediction models and methods with better adaptability and higher accuracy. Summary of the Invention
[0007] The purpose of this invention is to provide a method, apparatus, and electronic device for calculating the thermal conductivity of inorganic non-metallic materials. This method can theoretically predict and analyze the heat transfer performance of composite materials with diverse compositions, improve the accuracy of prediction, and is applicable to the calculation of thermal conductivity under complex conditions.
[0008] To achieve this objective, the present invention adopts the following technical solution:
[0009] In a first aspect, the present invention provides a method for calculating the thermal conductivity of inorganic non-metallic materials, the calculation method comprising the following steps:
[0010] (1) Determine the volume fraction and thermal conductivity of each solid phase component and gas phase component in the material to be tested;
[0011] (2) Using a parallel model and / or Levy model, the components in the material to be tested are classified, combined and calculated to obtain the thermal conductivity of the material to be tested.
[0012] The method provided by this invention classifies and integrates the components in the material, and then uses the basic structural heat transfer model to predict and calculate the thermal conductivity. This method is more accurate and more in line with the actual situation than the prediction of a single model, which facilitates the selection, design and research of materials in the preparation and application of composite inorganic non-metallic materials.
[0013] Preferably, the method for determining each solid phase component includes X-ray diffraction analysis.
[0014] Preferably, the method for determining the volume fraction of each solid phase component in step (1) includes the following steps: determining the mass fraction of each solid phase component, and calculating the volume fraction of each solid phase component from the obtained mass fraction.
[0015] Preferably, the method for determining the mass fraction includes X-ray fluorescence spectrometry.
[0016] Preferably, the method for calculating the volume fraction from the mass fraction includes a mass-density-volume conversion method.
[0017] The mass-density-volume conversion method described in this invention is to convert the mass fraction of each solid phase component into the mass of each component per unit mass, divide the mass of each component independently by the density of each component to obtain the volume of each component, sum the volumes of each component to obtain the total volume of the material, and divide the volume of each component by the total volume of the material to obtain the volume fraction of each component.
[0018] Preferably, the method for determining the volume fraction of the gas phase component in step (1) includes the Archimedes water displacement method.
[0019] Preferably, the method for classification, combination, and calculation in step (2) includes:
[0020] (a) Take the component with the largest volume fraction in the solid phase as the main phase, and combine the components with the closest thermal conductivity values among the solid phase components other than the main phase in pairs. Calculate the results using a parallel model and denote them as set I. If there are any remaining components with an odd number of components, include the thermal conductivity of that component in set I.
[0021] (b) Combine the closest values in set I in pairs and calculate the result. If there is an odd number remaining in this combination, include its value in the result of this calculation. Repeat the above pairwise combination and calculation until a unique result is obtained, which is denoted as set II. The calculation adopts the Levy model.
[0022] (c) Combine the thermal conductivity of the main phase with the obtained set II, and calculate the result using the Levy model, which is denoted as set III;
[0023] (d) Combine the thermal conductivity of the gas phase components with the obtained set III, and calculate the result using the Levy model, which is denoted as set IV.
[0024] Preferably, the calculation formula for the parallel model in step (2) is:
[0025] k p =k1(1-v2)+k2v2
[0026] In the formula, k p denoted as , where k1 is the thermal conductivity of the first component in the combination; k2 is the thermal conductivity of the second component in the combination; and v2 is the volume fraction of the second component in the combination.
[0027] Preferably, the calculation formula for the Levy model in step (2) is:
[0028]
[0029] In the formula, k L k1 is the thermal conductivity of the combined component; k2 is the thermal conductivity of the first component in the combination; k3 is the thermal conductivity of the second component in the combination.
[0030]
[0031]
[0032] In the formula, v1 is the volume fraction of the first component in the combination, v2 is the volume fraction of the second component in the combination, F is a function of G, and G is a weighted parameter of the thermal conductivity of the components.
[0033] Secondly, the present invention provides a device for calculating the thermal conductivity of inorganic non-metallic materials, the device comprising:
[0034] The input module is used to input the volume fraction and thermal conductivity of each solid and gaseous component in the material to be tested.
[0035] The calculation module is used to classify and combine the components in the material under test and calculate the thermal conductivity of the material under test by using a parallel model and / or a Levy model.
[0036] Thirdly, the present invention provides an electronic device, the electronic device comprising:
[0037] At least one processor; and
[0038] A memory communicatively connected to the at least one processor; wherein,
[0039] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for calculating the thermal conductivity of inorganic non-metallic materials according to any embodiment of the present invention.
[0040] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for calculating the thermal conductivity of inorganic non-metallic materials according to any embodiment of the present invention.
[0041] Compared with the prior art, the present invention has the following beneficial effects:
[0042] The calculation method provided by this invention avoids the step of first measuring actual values to deduce the model weighting parameters, thus optimizing the calculation method; it solves the problem of poor adaptability of traditional single prediction models due to their simple configuration, and is more accurate than single model prediction, reducing the error range to below 10%, making it suitable for more complex component situations. Attached Figure Description
[0043] Figure 1 This is a flowchart illustrating a method for calculating the thermal conductivity of inorganic non-metallic materials according to an embodiment of the present invention.
[0044] Figure 2 This is a schematic diagram of a device for calculating the thermal conductivity of inorganic non-metallic materials according to an embodiment of the present invention.
[0045] Figure 3 This is a schematic diagram of the structure of an electronic device that implements the method for calculating the thermal conductivity of inorganic non-metallic materials according to embodiments of the present invention. Detailed Implementation
[0046] The technical solution of the present invention will be further illustrated below through specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the present invention and should not be construed as limiting the invention in any way.
[0047] Figure 1 This invention provides a flowchart of a method for calculating the thermal conductivity of inorganic non-metallic materials, applicable to situations involving the calculation of the thermal conductivity of inorganic non-metallic materials. This method can be executed by a device for calculating the thermal conductivity of inorganic non-metallic materials, which can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method for calculating the thermal conductivity of inorganic non-metallic materials provided by the present invention may include the following steps:
[0048] S110, determine the volume fraction and thermal conductivity of each solid and gaseous component in the material to be tested;
[0049] S120 uses a parallel model and / or Levy model to classify, combine, and calculate the components in the material under test to obtain the thermal conductivity of the material under test.
[0050] Example 1
[0051] This embodiment provides a method for calculating the thermal conductivity of inorganic non-metallic materials.
[0052] In this embodiment, M1 type Al-Mg-Si-O solid thermal storage material is used as the material to be tested, and the calculation method includes the following steps:
[0053] (1) The solid components in the test material were determined by X-ray diffraction analysis; the mass fraction of each solid component in the test material was determined by X-ray fluorescence spectroscopy analysis; the volume fraction of each solid component was calculated from the obtained mass fraction using the mass-density-volume conversion method; the volume fraction of the gaseous component was determined by Archimedes' displacement method; the standard thermal conductivity values of each solid component and gaseous component were consulted; the results are shown in Table 1.
[0054] (2) SiO2 was determined to be the main phase. Based on the similarity of their thermal conductivity values, Al2O3 and TiO2 were combined, denoted as combination 1. Fe2O3 and MgAl2O4 were combined, denoted as combination 2. CaAl4O7 was the odd remaining component. The component with the smaller volume fraction in each combination was designated as the second component. The volume fraction v2 of TiO2 in combination 1 was calculated to be 15.38 vol%, and the volume fraction v2 of Fe2O3 in combination 2 was calculated to be 7.26 vol%. The combined thermal conductivity k was calculated using a parallel model. p1 It is 16.62 W·m -1 ·K-1 k p2 4.54 W·m -1 ·K -1 k p1 k p2 The thermal conductivity of CaAl4O7 is denoted as set I.
[0055] (3) Based on the values in set I, combine combination 2 with CaAl4O7, denoted as combination 3. The volume fraction v2 of CaAl4O7 in combination 3 is calculated to be 18.68 vol%. The thermal conductivity k of combination 3 is calculated using the Levy model. L3 It is 4.33 W·m -1 ·K -1 Then, combine combination 1 and combination 3 to form combination 4. The volume fraction v2 of combination 1 in combination 4 is calculated to be 41.34 vol%. The thermal conductivity k of combination 4 is calculated using the Levy model. L4 It is 8.07 W·m -1 ·K -1 Let be set II.
[0056] (4) Combine group II with the main phase SiO2, denoted as group 5. The volume fraction v2 of SiO2 in group 5 is calculated to be 46.83 vol%. The thermal conductivity k of group 5 is calculated using the Levy model. L5 3.91 W·m -1 ·K -1 Let be set III.
[0057] (5) Combine group III with the gaseous component air, denoted as group 6. The volume fraction v2 of air in group 6 is calculated to be 16.31 vol%. The thermal conductivity k of group 6 is calculated using the Levy model. L6 2.34 W·m -1 ·K -1 Let be set Ⅳ, which is the thermal conductivity of the material to be tested.
[0058] The theoretical predictions, measured values, and errors obtained in this embodiment are shown in Table 5.
[0059] Table 1
[0060]
[0061] Example 2
[0062] This embodiment provides a method for calculating the thermal conductivity of inorganic non-metallic materials.
[0063] In this embodiment, M2 type Al-Mg-Si-O solid thermal storage material is used as the material to be tested, and the calculation method includes the following steps:
[0064] (1) The solid components in the test material were determined by X-ray diffraction analysis; the mass fraction of each solid component in the test material was determined by X-ray fluorescence spectroscopy analysis; the volume fraction of each solid component was calculated from the obtained mass fraction using the mass-density-volume conversion method; the volume fraction of the gaseous component was determined by Archimedes' displacement method; the standard thermal conductivity values of each solid component and gaseous component were consulted; the obtained values are shown in Table 2.
[0065] (2) SiO2 was determined to be the main phase. Based on the similarity of their thermal conductivity values, Al2O3 and TiO2 were combined, denoted as combination 1. Fe2O3 and MgAl2O4 were combined, denoted as combination 2. CaAl4O7 was the odd remaining component. The component with the smaller volume fraction in each combination was designated as the second component. The volume fraction v2 of TiO2 in combination 1 was calculated to be 16.87 vol%, and the volume fraction v2 of Fe2O3 in combination 2 was calculated to be 7.76 vol%. The combined thermal conductivity k was calculated using a parallel model. p1 16.45 W·m -1 ·K -1 k p2 4.54 W·m -1 ·K -1 k p1 k p2 The thermal conductivity of CaAl4O7 is denoted as set I.
[0066] (3) Based on the values in set I, combine combination 2 with CaAl4O7, denoted as combination 3. The volume fraction v2 of CaAl4O7 in combination 3 is calculated to be 18.67 vol%. The thermal conductivity k of combination 3 is calculated using the Levy model. L3 It is 4.33 W·m -1 ·K -1 Combination 1 and Combination 3 are then combined, denoted as Combination 4. The volume fraction v2 of Combination 1 in Combination 4 is calculated to be 42.78 vol%. The thermal conductivity k of Combination 4 is calculated using the Levy model. L4 It is 8.18 W·m -1 ·K -1 Let be set II.
[0067] (4) Combine group II with the main phase SiO2, denoted as group 5. The volume fraction v2 of SiO2 in group 5 is calculated to be 47.91 vol%. The thermal conductivity k of group 5 is calculated using the Levy model. L5 3.87 W·m -1 ·K -1 Let be set III.
[0068] (5) Combine group III with the gaseous component air, denoted as group 6. The volume fraction v2 of air in group 6 is calculated to be 16.47 vol%. The thermal conductivity k of group 6 is calculated using the Levy model. L6 2.30 W·m -1 ·K -1 Let be set Ⅳ, which is the thermal conductivity of the material to be tested.
[0069] The theoretical predictions, measured values, and errors obtained in this embodiment are shown in Table 5.
[0070] Table 2
[0071]
[0072]
[0073] Example 3
[0074] This embodiment provides a method for calculating the thermal conductivity of inorganic non-metallic materials.
[0075] In this embodiment, M3 type Al-Mg-Si-O solid thermal storage material is used as the material to be tested, and the calculation method includes the following steps:
[0076] (1) The solid components in the test material were determined by X-ray diffraction analysis; the mass fraction of each solid component in the test material was determined by X-ray fluorescence spectroscopy analysis; the volume fraction of each solid component was calculated from the obtained mass fraction using the mass-density-volume conversion method; the volume fraction of the gaseous component was determined by Archimedes' displacement method; the standard thermal conductivity values of each solid component and gaseous component were consulted; the obtained values are shown in Table 3.
[0077] (2) SiO2 was determined to be the main phase. Based on the similarity of their thermal conductivity values, Al2O3 and TiO2 were combined, denoted as combination 1. Fe2O3 and MgAl2O4 were combined, denoted as combination 2. CaAl4O7 was the odd remaining component. The component with the smaller volume fraction in each combination was designated as the second component. The volume fraction v2 of TiO2 in combination 1 was calculated to be 24.14 vol%, and the volume fraction v2 of Fe2O3 in combination 2 was calculated to be 7.23 vol%. The combined thermal conductivity k was calculated using a parallel model. p1 It is 15.63 W·m -1 ·K -1 k p2 4.54 W·m -1 ·K -1 k p1 k p2 The thermal conductivity of CaAl4O7 is denoted as set I.
[0078] (3) Based on the values in set I, combine combination 2 with CaAl4O7, denoted as combination 3. The volume fraction v2 of CaAl4O7 in combination 3 is calculated to be 36.47 vol%. The thermal conductivity k of combination 3 is calculated using the Levy model. L3 It is 4.14 W·m -1 ·K -1 Combination 1 and Combination 3 are then combined, denoted as Combination 4. The volume fraction v2 of Combination 1 in Combination 4 is calculated to be 29.03 vol%. The thermal conductivity k of Combination 4 is calculated using the Levy model. L4 It is 6.47 W·m -1 ·K -1 Let be set II.
[0079] (4) Combine group II with the main phase SiO2, denoted as group 5. The volume fraction v2 of SiO2 in group 5 is calculated to be 47.10 vol%. The thermal conductivity k of group 5 is calculated using the Levy model. L5 3.40 W·m -1 ·K -1 Let be set III.
[0080] (5) Combine group III with the gaseous component air, denoted as group 6. The volume fraction v2 of air in group 6 is calculated to be 15.25 vol%. The thermal conductivity k of group 6 is calculated using the Levy model. L6 2.04 W·m -1 ·K -1 Let be set Ⅳ, which is the thermal conductivity of the material to be tested.
[0081] The theoretical predictions, measured values, and errors obtained in this embodiment are shown in Table 5.
[0082] Table 3
[0083]
[0084] Example 4
[0085] This embodiment provides a method for calculating the thermal conductivity of inorganic non-metallic materials.
[0086] In this embodiment, M4 type Al-Mg-Si-O solid thermal storage material is used as the material to be tested, and the calculation method includes the following steps:
[0087] (1) The solid components in the test material were determined by X-ray diffraction analysis; the mass fraction of each solid component in the test material was determined by X-ray fluorescence spectroscopy analysis; the volume fraction of each solid component was calculated from the obtained mass fraction using the mass-density-volume conversion method; the volume fraction of the gaseous component was determined by Archimedes' displacement method; the standard thermal conductivity values of each solid component and gaseous component were consulted; the obtained values are shown in Table 4.
[0088] (2) SiO2 was determined to be the main phase. Based on the similarity of their thermal conductivity values, Al2O3 and TiO2 were combined, denoted as combination 1. Fe2O3 and MgAl2O4 were combined, denoted as combination 2. CaAl4O7 was the odd remaining component. The component with the smaller volume fraction in each combination was designated as the second component. The volume fraction v2 of TiO2 in combination 1 was calculated to be 21.50 vol%, and the volume fraction v2 of Fe2O3 in combination 2 was calculated to be 7.23 vol%. The combined thermal conductivity k was calculated using a parallel model. p1 It is 15.93 W·m -1 ·K -1 k p2 4.54 W·m -1 ·K -1 k p1 k p2 The thermal conductivity of CaAl4O7 is denoted as set I.
[0089] (3) Based on the values in set I, combine combination 2 with CaAl4O7, denoted as combination 3. The volume fraction v2 of CaAl4O7 in combination 3 is calculated to be 36.46 vol%. The thermal conductivity k of combination 3 is calculated using the Levy model. L3 It is 4.14 W·m -1 ·K -1 Then, combine combination 1 and combination 3 to form combination 4. The volume fraction v2 of combination 1 in combination 4 is calculated to be 28.37 vol%. The thermal conductivity k of combination 4 is calculated using the Levy model. L4 It is 6.45 W·m -1 ·K -1 Let be set II.
[0090] (4) Combine group II with the main phase SiO2, denoted as group 5. The volume fraction v2 of SiO2 in group 5 is calculated to be 44.35 vol%. The thermal conductivity k of group 5 is calculated using the Levy model. L5 3.53 W·m -1 ·K -1 Let be set III.
[0091] (5) Combine group III with the gaseous component air, denoted as group 6. The volume fraction v2 of air in group 6 is calculated to be 17.32 vol%. The thermal conductivity k of group 6 is calculated using the Levy model. L6 2.04 W·m -1 ·K -1 Let be set Ⅳ, which is the thermal conductivity of the material to be tested.
[0092] The theoretical predictions, measured values, and errors obtained in this embodiment are shown in Table 5.
[0093] Table 4
[0094]
[0095] Table 5
[0096] <![CDATA[Measured thermal conductivity (W·m -1 ·K -1 )]]> <![CDATA[Predictive thermal conductivity (W·m -1 ·K -1 )]]> error(%) Example 1 2.27 2.34 3.08 Example 2 2.12 2.30 8.49 Example 3 2.22 2.04 8.11 Example 4 1.95 2.04 4.62
[0097] The method for calculating the thermal conductivity of inorganic non-metallic materials provided by this invention can predict and calculate the thermal conductivity of multi-component inorganic non-metallic materials with accurate prediction and an error range as low as 10%.
[0098] Example 5
[0099] This embodiment provides a method such as Figure 2 The illustrated device for calculating the thermal conductivity of inorganic non-metallic materials is applicable to situations involving the calculation of the thermal conductivity of inorganic non-metallic materials. This device can be implemented in hardware and / or software and can be configured in electronic devices with data processing capabilities. Figure 2 As shown, the device for calculating the thermal conductivity of inorganic non-metallic materials in this embodiment may include: an input module 510 and a calculation module 520. Wherein:
[0100] Input module 510 is used to input the volume fraction and thermal conductivity of each solid phase component and gas phase component in the material to be tested;
[0101] The calculation module 520 is used to classify and combine the components in the material under test and calculate the thermal conductivity of the material under test by using a parallel model and / or a Levy model.
[0102] The device for calculating the thermal conductivity of inorganic non-metallic materials provided in this embodiment can execute the method for calculating the thermal conductivity of inorganic non-metallic materials provided in any embodiment of the present invention, and has the corresponding functions and beneficial effects of executing the method for calculating the thermal conductivity of inorganic non-metallic materials.
[0103] Example 6
[0104] This embodiment provides a method such as Figure 3The schematic diagram of the electronic device shown illustrates a method for calculating the thermal conductivity of inorganic non-metallic materials, which can be used to implement the calculation method of any embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.
[0105] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.
[0106] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0107] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the method for calculating the thermal conductivity of inorganic non-metallic materials.
[0108] In some embodiments, the method for calculating the thermal conductivity of inorganic nonmetallic materials can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for calculating the thermal conductivity of inorganic nonmetallic materials described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for calculating the thermal conductivity of inorganic nonmetallic materials by any other suitable means (e.g., by means of firmware).
[0109] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0110] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0111] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0112] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0113] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.
[0114] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.
[0115] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.
Claims
1. A method for calculating the thermal conductivity of inorganic non-metallic materials, characterized in that, The calculation method includes the following steps: (1) Determine the volume fraction and thermal conductivity of each solid phase component and gas phase component in the material to be tested; (2) Using a parallel model and / or the Levy model, the components in the material to be tested are classified, combined, and calculated to obtain the thermal conductivity of the material to be tested, including: (a) Take the component with the largest volume fraction in the solid phase as the main phase, and combine the components with the closest thermal conductivity values among the solid phase components other than the main phase in pairs. Calculate the results using a parallel model and denote them as set I. If there are any remaining components with an odd number of components, include the thermal conductivity of that component in set I. (b) Combine the closest values in set I in pairs and calculate the result. If there is an odd number remaining in this combination, include its value in the result of this calculation. Repeat the above pairwise combination and calculation until a unique result is obtained, which is denoted as set II. The calculation adopts the Levy model. (c) Combine the thermal conductivity of the main phase with the obtained set II, and calculate the result using the Levy model, which is denoted as set III; (d) Combine the thermal conductivity of the gas phase components with the obtained set III, and calculate the result using the Levy model. This result is denoted as set IV, which is the thermal conductivity of the material to be tested.
2. The calculation method according to claim 1, characterized in that, Methods for determining the various solid phase components include X-ray diffraction analysis.
3. The calculation method according to claim 1, characterized in that, The method for determining the volume fraction of each solid component in step (1) includes the following steps: Determine the mass fraction of each solid component, and calculate the volume fraction of each solid component from the obtained mass fraction.
4. The calculation method according to claim 3, characterized in that, Methods for determining the mass fraction include X-ray fluorescence spectroscopy.
5. The calculation method according to claim 3, characterized in that, The method for calculating the volume fraction from the mass fraction includes a mass-density-volume conversion method.
6. The calculation method according to claim 1, characterized in that, The method for determining the volume fraction of the gas phase components in step (1) includes the Archimedes water displacement method.
7. The calculation method according to claim 1, characterized in that, The calculation formula for the parallel model in step (2) is as follows: In the formula, k p denoted as , where k1 is the thermal conductivity of the first component in the combination; k2 is the thermal conductivity of the second component in the combination; and v2 is the volume fraction of the second component in the combination.
8. The calculation method according to claim 1, characterized in that, The calculation formula for the Levy model in step (2) is as follows: In the formula, k L k1 is the thermal conductivity of the combined component; k2 is the thermal conductivity of the first component in the combination; k3 is the thermal conductivity of the second component in the combination. In the formula, v1 is the volume fraction of the first component in the combination, v2 is the volume fraction of the second component in the combination, F is a function of G, and G is a weighted parameter of the thermal conductivity of the components.
9. The calculation method according to claim 1, characterized in that, The calculation method includes the following steps: (i) Determine the solid components in the test material by X-ray diffraction analysis; determine the mass fraction of each solid component in the test material by X-ray fluorescence spectroscopy analysis, and calculate the volume fraction of each solid component; The volume fraction of gaseous components was determined by Archimedes' displacement method. Consult the standard thermal conductivity values for each solid and gaseous component; (ii) Take the component with the largest volume fraction in the solid phase as the main phase, and combine the components with the closest thermal conductivity values among the solid phase components other than the main phase in pairs. Calculate the results using a parallel model and denote them as set I. If there are any remaining components with an odd number of components, include the thermal conductivity of that component in set I. (iii) Combine the closest values in set I in pairs and calculate the result. If there is an odd number remaining in this combination, include its value in the result of this calculation. Repeat the above pairwise combination and calculation until a unique result is obtained, which is denoted as set II. The calculation adopts the Levy model. (iv) Combine the thermal conductivity of the main phase with the obtained set II, and calculate the result using the Levy model, which is denoted as set III; (v) Combine the thermal conductivity of the gas phase components with the obtained set III, and calculate the result using the Levy model. This result is denoted as set IV, which is the thermal conductivity of the material to be tested.
10. A device for calculating the thermal conductivity of inorganic non-metallic materials, characterized in that, The device includes: The input module is used to input the volume fraction and thermal conductivity of each solid and gaseous component in the material to be tested. The calculation module is used to classify and combine the components in the material under test using a parallel model and / or the Levy model, and calculate the thermal conductivity of the material under test. The specific process includes: (a) Take the component with the largest volume fraction in the solid phase as the main phase, and combine the components with the closest thermal conductivity values among the solid phase components other than the main phase in pairs. Calculate the results using a parallel model and denote them as set I. If there are any remaining components with an odd number of components, include the thermal conductivity of that component in set I. (b) Combine the closest values in set I in pairs and calculate the result. If there is an odd number remaining in this combination, include its value in the result of this calculation. Repeat the above pairwise combination and calculation until a unique result is obtained, which is denoted as set II. The calculation adopts the Levy model. (c) Combine the thermal conductivity of the main phase with the obtained set II, and calculate the result using the Levy model, which is denoted as set III; (d) Combine the thermal conductivity of the gas phase components with the obtained set III, and calculate the result using the Levy model. This result is denoted as set IV, which is the thermal conductivity of the material to be tested.
11. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for calculating the thermal conductivity of inorganic non-metallic materials according to any one of claims 1-9.