A method of testing thermal radiation of a heat conducting sample

Abstract

The application discloses a heat radiation test method of a heat-conducting sample, multi-dimensional temperature detection points and environmental auxiliary detection points are arranged in the inside of an isolation cover, temperature data of a heating part, a metal part, a heat-conducting part and the heat-conducting sample in a test process are collected in real time, and temperature, humidity and air pressure data of a test environment are collected in real time; an AI large model heat radiation analysis system suitable for the heat-conducting sample is constructed, a heat radiation core physical law is embedded as a prior constraint; the collected original data are input into the AI large model heat radiation analysis system for data preprocessing and noise reduction analysis, and environmental interference and temperature measurement errors are eliminated; intelligent calculation, data verification and performance trend research and judgment of heat radiation performance indexes are completed through the AI large model heat radiation analysis system, and a heat radiation performance test report of the heat-conducting sample is output, so that the application not only improves the heat radiation performance test precision of the heat-conducting sample, but also provides strong support for performance evaluation, application selection and technical optimization of the heat-conducting sample.

Description

Technical Field

[0001] This invention relates to the field of electronic component testing technology, and specifically discloses a method for testing the thermal radiation of thermally conductive samples. Background Technology

[0002] Currently, existing technologies for testing the thermal radiation performance of thermally conductive graphite materials mostly focus on static testing of thermal conductivity. This testing method can only detect the thermal conduction performance of the material and cannot accurately reflect its thermal radiation heat dissipation performance under actual working conditions.

[0003] To address the above issues, some dedicated thermal radiation testing structures have been publicly disclosed within the industry. However, these structures still have the following drawbacks:

[0004] First, the existing test structure has shortcomings such as simple design, unreasonable layout of temperature measurement points, and complex heat conduction interference factors. As a result, the heat from the heat source is easily transferred directly to the temperature measurement component through contact conduction, rather than through the sample to complete the heat transfer in the form of thermal radiation. This ultimately leads to a large deviation between the test data and the actual thermal radiation performance of the material.

[0005] Second, the existing testing structure relies heavily on manual calculation for data processing in the detection mode, and lacks functions such as automatic noise reduction, environmental interference removal, and temperature measurement error calibration. The results obtained by simply relying on data fitting without physical constraints have low reliability and cannot complete performance verification, trend analysis and judgment, or automated output of test reports. Overall, the practicality and accuracy of the testing are significantly lacking. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention discloses a method for testing the thermal radiation of thermally conductive samples.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is: a method for testing the thermal radiation of a thermally conductive sample, comprising the following steps:

[0008] The first step is to assemble the heating element, metal parts, heat-conducting parts and heat-conducting sample from bottom to top to form a test component, and then isolate it from the external environment through an isolation cover. The heating element is connected to an external power adjustable power supply to control its heating at different power levels.

[0009] The second step involves arranging multi-dimensional temperature detection points and environmental auxiliary detection points inside the isolation enclosure to collect temperature data of the heating element, metal part, heat-conducting part and heat-conducting sample in real time during the test, as well as temperature, humidity and air pressure data of the test environment in real time.

[0010] The third step is to build an AI large-scale model thermal radiation analysis system adapted to thermally conductive samples, embedding the core physical laws of thermal radiation as prior constraints.

[0011] The fourth step is to input the collected raw data into the AI ​​large model thermal radiation analysis system for data preprocessing and noise reduction analysis to eliminate environmental interference and temperature measurement errors.

[0012] The fifth step involves using an AI-powered large-scale model thermal radiation analysis system to intelligently calculate thermal radiation performance indicators, verify data, and determine performance trends, and then outputting a thermal radiation performance test report for the thermally conductive sample.

[0013] In a preferred embodiment of the present invention, one side of the thermally conductive sample extends outward relative to the thermally conductive component, and a stainless steel component for the partition element is provided at the bottom of the outwardly extending side of the thermally conductive sample. The multi-dimensional temperature detection point also collects the temperature data of the stainless steel component in real time during the test.

[0014] In a preferred embodiment of the present invention, the heating element is either a ceramic heating element or a high-power resistor, the metal element is either a copper plate or an aluminum plate, and the thermally conductive element is either a thermally conductive silicone pad, a graphite film, graphite copper, graphite aluminum, graphene thermally conductive film, or a carbon nanotube film.

[0015] As a preferred embodiment of the present invention, the AI ​​large model thermal radiation analysis system adopts a hybrid architecture of Transformer temporal feature extraction network and CNN spatial feature fusion network. The hybrid architecture includes a data access module, a physical prior constraint module, a preprocessing noise reduction module, an intelligent computing module, a data verification module, a trend judgment module, and a report generation module.

[0016] In a preferred embodiment of the present invention, the physical prior constraint module is based on Stefan-Boltzmann law, Kirchhoff's thermal radiation law, the heat conduction-radiation coupling heat transfer separation criterion, and the law of conservation of energy as prior constraints.

[0017] In a preferred embodiment of the present invention, the preprocessing noise reduction module is used for outlier removal, time-series wavelet noise reduction, adaptive smoothing filtering, environmental temperature interference compensation, sensor temperature measurement error calibration, and separation and deduction of contact heat conduction interference.

[0018] In a preferred embodiment of the present invention, the intelligent computing module is used to calculate thermal radiation power, thermal radiation transfer efficiency, equivalent emissivity, steady-state radiation temperature difference, thermal radiation response time, test repeatability deviation rate, and batch performance consistency index.

[0019] In a preferred embodiment of the present invention, the data verification module is used for energy conservation closure verification, time series stability verification, physical rationality verification, repeatability verification of the same sample, and automatic marking of test anomalies.

[0020] As a preferred embodiment of the present invention, the AI ​​large model performance trend analysis is used for thermal radiation attenuation trend analysis, high temperature stability assessment, batch consistency analysis, root cause localization of abnormal samples, and early warning of usage risks.

[0021] In a preferred embodiment of the present invention, the report generated by the report generation module includes test parameters, preprocessing details, temperature time series curves, core performance indicators, credibility scores, verification conclusions, trend analysis results, qualification determination, and traceability identification.

[0022] The present invention achieves the following beneficial effects:

[0023] 1. This application optimizes the structural design of the testing device and rationally arranges the temperature measurement points, blocking the path of heat from the heat source being directly transferred to the temperature measurement component through contact conduction. This ensures that the heat from the heat source is mainly transferred to the temperature measurement component through the thermal radiation of the sample, significantly reducing the deviation between the test data and the actual thermal radiation effect of the material. This allows the test results to truly reflect the thermal radiation heat dissipation performance of thermally conductive graphite materials under actual working conditions, filling the gap in existing testing methods that cannot accurately characterize the thermal radiation performance of materials.

[0024] 2. This application integrates automatic noise reduction, environmental interference elimination, and temperature measurement error calibration functions to replace the traditional manual data processing mode. This not only significantly reduces the intensity of manual operation and human calculation errors, but also significantly improves data processing efficiency. At the same time, combined with reasonable physical constraints, it can improve the credibility of test results and ensure that the data can be effectively used for the verification and trend analysis of material thermal radiation performance, providing a scientific basis for material performance optimization.

[0025] Other features and advantages of the invention will be set forth in the following description and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the disclosure of this invention and, together with the description, serve to explain the principles of this disclosure.

[0027] Figure 1 This is a schematic diagram of the overall structure disclosed in this invention;

[0028] Figure 2 This is a schematic diagram of the test component structure disclosed in this invention;

[0029] Figure 3 This is a schematic diagram of the structure of the AI ​​large-scale model thermal radiation analysis system disclosed in this invention;

[0030] In the diagram: 10. Test component; 11. Heating element; 12. Metal component; 13. Thermally conductive component; 14. Thermally conductive sample; 15. Stainless steel component; 20. Isolation cover; 30. Adjustable power supply; 40. Multi-dimensional temperature detection point; 41. First temperature probe; 50. Environmental auxiliary detection point; 51. Second temperature probe; 52. Humidity probe; 53. Pressure probe; 60. AI large-scale model thermal radiation analysis system; 61. Data access module; 62. Physical prior constraint module; 63. Preprocessing noise reduction module; 64. Intelligent computing module; 65. Data verification module; 66. Trend analysis module; 67. Report generation module. Detailed Implementation

[0031] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0032] In the description of this invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inner", "around", etc., which indicate orientation or positional relationship, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting this invention.

[0033] Example

[0034] To address the shortcomings of existing technologies that rely on static thermal conductivity testing to assess the thermal radiation performance of thermally conductive materials, which only characterizes a single thermal conduction property and fails to accurately reflect their actual thermal radiation dissipation under real-world conditions; and despite the development of dedicated thermal radiation testing structures in the industry, several technical challenges remain. (Refer to...) Figures 1-2 As shown, this application discloses a method for testing the thermal radiation of a thermally conductive sample, comprising the following steps:

[0035] The first step involves assembling the heating element 11 (used to simulate the heating of electrical components), the metal part 12 (used to simulate the mounting medium of electrical components), the heat-conducting part 13 (used for heat dissipation), and the heat-conducting sample 14 (test sample) from bottom to top to form the test component 10. The test component 10 is then isolated from the external environment by the isolation cover 20, and the heating element 11 is connected to an external power adjustable power supply 30 to control its heating at different power levels.

[0036] The second step involves arranging multi-dimensional temperature detection points 40 and environmental auxiliary detection points 50 inside the isolation enclosure 20 to collect temperature data of the heating element 11, metal part 12, heat-conducting part 13, and heat-conducting sample 14 in real time during the test, as well as collecting temperature, humidity, and air pressure data of the test environment in real time. (The multi-dimensional temperature detection point 40 must include at least a first temperature probe 41 that is in direct contact with the heating element 11, metal part 12, heat-conducting part 13, and heat-conducting sample 14, while the environmental auxiliary detection point 50 is suspended in the isolation enclosure 20 and includes at least a second temperature probe 51, a humidity probe 52, and a pressure probe 53.)

[0037] The third step is to construct an AI large-scale model thermal radiation analysis system 60 adapted to the thermally conductive sample 14, and embed the core physical laws of thermal radiation as prior constraints.

[0038] The fourth step is to input the collected raw data into the AI ​​large model thermal radiation analysis system 60 for data preprocessing and noise reduction analysis to eliminate environmental interference and temperature measurement errors.

[0039] The fifth step involves using the AI ​​large-scale model thermal radiation analysis system 60 to intelligently calculate thermal radiation performance indicators, verify data, and determine performance trends, and then outputting a thermal radiation performance test report for the thermally conductive sample 14.

[0040] In the second step, the heating element 11 is preferably a ceramic heating element or a high-power resistor, the metal element 12 is preferably a copper plate or an aluminum plate, and the heat-conducting element 13 is preferably a thermally conductive silicone pad, graphite film, graphite copper, graphite aluminum, graphene thermally conductive film or carbon nanotube film; while the thermally conductive sample 14 can be made of graphite thermally conductive material; in addition, the above-mentioned components are preferably thin and light, so as to achieve seamless bonding between the components and eliminate the thermal conduction deviation caused by gaps.

[0041] Based on the second step, this application also extends outward on one side of the thermally conductive sample 14 relative to the thermally conductive component 13. The bottom of the outwardly extending side of the thermally conductive sample 14 is provided with a stainless steel component 15 for the partition components. The multi-dimensional temperature detection point 40 also collects the temperature data of the stainless steel component 15 in real time during the test (the multi-dimensional temperature detection point 40 must be in direct contact with the stainless steel component 15).

[0042] The above-mentioned test device has a structural design and reasonable layout of temperature measurement points, which blocks the path of heat from the heat source to the temperature measurement component through contact conduction. This ensures that the heat from the heat source is mainly transferred to the temperature measurement component through the thermal radiation of the sample, which greatly reduces the deviation between the test data and the actual thermal radiation effect of the material. This allows the test results to truly reflect the thermal radiation heat dissipation performance of thermally conductive graphite materials under actual working conditions.

[0043] refer to Figure 3As shown, in the third step, the AI ​​large model thermal radiation analysis system 60 of this application adopts a hybrid architecture of Transformer temporal feature extraction network and CNN spatial feature fusion network. The hybrid architecture includes a data access module 61, a physical prior constraint module 62, a preprocessing noise reduction module 63, an intelligent computing module 64, a data verification module 65, a trend judgment module 66, and a report generation module 67.

[0044] The physical prior constraint module 62 is based on the Stefan-Boltzmann law, Kirchhoff's thermal radiation law, the heat transfer separation criterion of heat conduction and heat radiation coupling, and the law of conservation of energy as prior constraints.

[0045] Preprocessing noise reduction module 63 is used for outlier removal, time-series wavelet noise reduction, adaptive smoothing filtering, environmental temperature interference compensation, sensor temperature measurement error calibration, and separation and subtraction of contact heat conduction interference.

[0046] The intelligent calculation module 64 is used to calculate thermal radiation power, thermal radiation transfer efficiency, equivalent emissivity, steady-state radiation temperature difference, thermal radiation response time, test repeatability deviation rate, and batch performance consistency index; the data verification module 65 is used for energy conservation closure verification, temporal stability verification, physical rationality verification, repeatability verification of the same sample, and automatic marking of test anomalies.

[0047] AI large-scale model performance trend analysis is used for thermal radiation decay trend analysis, high-temperature stability assessment, batch consistency analysis, root cause localization of abnormal samples, and early warning of usage risks.

[0048] The report generated by the report generation module 67 includes test parameters, preprocessing details, temperature time series curves, core performance indicators, reliability scores, verification conclusions, trend analysis results, pass / fail determination, and traceability identification.

[0049] The AI-based large-scale thermal radiation analysis system 60 described above can replace the traditional manual data processing mode, which not only significantly reduces the intensity of manual operation and human calculation errors, but also significantly improves data processing efficiency. At the same time, combined with reasonable physical constraints, it can improve the credibility of test results, ensure that the data can be effectively used for the verification and trend analysis of material thermal radiation performance, and provide a scientific basis for material performance optimization.

[0050] In the description of this specification, the references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0051] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent transformations or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for testing the thermal radiation of a thermally conductive sample, characterized in that, Includes the following steps: The first step is to assemble the heating element, metal parts, heat-conducting parts and heat-conducting sample from bottom to top to form a test component, and then isolate it from the external environment through an isolation cover. The heating element is connected to an external power adjustable power supply to control its heating at different power levels. The second step involves arranging multi-dimensional temperature detection points and environmental auxiliary detection points inside the isolation enclosure to collect temperature data of the heating element, metal part, heat-conducting part and heat-conducting sample in real time during the test, as well as temperature, humidity and air pressure data of the test environment in real time. The third step is to build an AI large-scale model thermal radiation analysis system adapted to thermally conductive samples, embedding the core physical laws of thermal radiation as prior constraints. The fourth step is to input the collected raw data into the AI ​​large model thermal radiation analysis system for data preprocessing and noise reduction analysis to eliminate environmental interference and temperature measurement errors. The fifth step involves using an AI-powered large-scale model thermal radiation analysis system to intelligently calculate thermal radiation performance indicators, verify data, and determine performance trends, and then outputting a thermal radiation performance test report for the thermally conductive sample.

2. The method for testing the thermal radiation of a thermally conductive sample according to claim 1, characterized in that, One side of the thermally conductive sample extends outward relative to the thermally conductive component. At the bottom of the outwardly extending side of the thermally conductive sample, there is a stainless steel component for the partition element. The multi-dimensional temperature detection point also collects the temperature data of the stainless steel component in real time during the test.

3. The method for testing the thermal radiation of a thermally conductive sample according to claim 1, characterized in that, The heating element is either a ceramic heating element or a high-power resistor; the metal element is either a copper plate or an aluminum plate; and the thermally conductive element is either a thermally conductive silicone pad, a graphite film, graphite copper, graphite aluminum, graphene thermally conductive film, or a carbon nanotube film.

4. The method for testing the thermal radiation of a thermally conductive sample according to claim 1, characterized in that, The AI ​​large-scale model thermal radiation analysis system adopts a hybrid architecture of Transformer temporal feature extraction network and CNN spatial feature fusion network. The hybrid architecture includes a data access module, a physical prior constraint module, a preprocessing and noise reduction module, an intelligent computing module, a data verification module, a trend analysis module, and a report generation module.

5. The method for testing the thermal radiation of a thermally conductive sample according to claim 4, characterized in that, The physical prior constraint module is based on Stefan-Boltzmann law, Kirchhoff's thermal radiation law, the heat conduction and thermal radiation coupling heat transfer separation criterion, and the law of conservation of energy as prior constraints.

6. The method for testing the thermal radiation of a thermally conductive sample according to claim 4, characterized in that, The preprocessing noise reduction module is used for outlier removal, time-series wavelet noise reduction, adaptive smoothing filtering, environmental temperature interference compensation, sensor temperature measurement error calibration, and separation and deduction of contact heat conduction interference.

7. The method for testing the thermal radiation of a thermally conductive sample according to claim 4, characterized in that, The intelligent computing module is used to calculate thermal radiation power, thermal radiation transfer efficiency, equivalent emissivity, steady-state radiation temperature difference, thermal radiation response time, test repeatability deviation rate, and batch performance consistency index.

8. The method for testing the thermal radiation of a thermally conductive sample according to claim 4, characterized in that, The data verification module is used for energy conservation closure verification, temporal stability verification, physical rationality verification, repeatability verification of the same sample, and automatic marking of test anomalies.

9. The method for testing the thermal radiation of a thermally conductive sample according to claim 4, characterized in that, The performance trend analysis of the AI ​​large model is used for thermal radiation attenuation trend analysis, high temperature stability assessment, batch consistency analysis, root cause localization of abnormal samples, and early warning of usage risks.

10. A method for testing the thermal radiation of a thermally conductive sample according to claim 4, characterized in that, The report generated by the report generation module includes test parameters, preprocessing details, temperature time series curves, core performance indicators, credibility score, verification conclusions, trend analysis results, qualification criteria, and traceability identification.

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