Automatic material heat conductivity coefficient testing method based on heat flow gradient method

Abstract

The invention relates to the technical field of material thermal performance testing, and discloses a material heat conductivity coefficient automatic testing method based on a heat flow gradient method. The method comprises the following steps: firstly, constructing a test environment topology network, collecting three-dimensional structure parameters and environment thermodynamic parameters of a tested material, and inputting the parameters into a thermal field simulation system to generate an internal heat flow distribution model; and analyzing internal cavity distribution characteristics according to the three-dimensional structure parameters, matching the internal cavity distribution characteristics with a cavity characteristic-heat source layout mapping table to determine a steady-state heat source layout scheme, and activating a heat flow gradient analysis device when multiple heat sources are needed. A heat conduction parameter database is called, heat source power parameters and temperature acquisition parameters are extracted based on environmental thermodynamic parameters, the heat source power parameters comprise spacing, axial angles and the like, and the temperature acquisition parameters comprise thermocouple types and acquisition frequencies; and finally, simulating temperature field evolution through a heat flow gradient analysis device, outputting and analyzing related data, and generating a parameter optimization instruction to adjust heat source configuration. According to the method, multiple links are coordinated, material structures and environmental factors are integrated, and automation of the testing process is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of material thermal performance testing, in particular to a material thermal conductivity automatic testing method based on heat flow gradient method. BACKGROUND

[0002] In the field of material science and engineering, the thermal conductivity of materials is an important indicator for evaluating the thermal performance of materials, and its test results have a direct impact on the selection of materials, the design of application scenarios, and the research and development of related products. Currently, the commonly used material thermal conductivity testing methods include two categories: steady-state method and transient method. The steady-state method measures the heat passing through the material per unit time to calculate the thermal conductivity by establishing a stable temperature gradient on both sides of the material. However, when facing materials with complex three-dimensional structures, it is difficult to accurately analyze the internal cavity distribution of the material, leading to unreasonable heat source arrangement and unstable temperature gradient, affecting the reliability of the test results.

[0003] The transient method calculates the thermal conductivity by measuring the temperature response of the material over time under thermal disturbance. However, this method requires high matching of temperature collection frequency and heat source power parameters. When there are uneven heat conduction paths inside the material, it is often difficult to accurately capture the dynamic evolution process of heat flow, resulting in large test errors. In addition, most existing testing methods rely on manual experience to configure heat source parameters and temperature collection parameters, lacking comprehensive consideration of environmental thermodynamic parameters and material structure parameters. When facing a variety of materials to be tested, it is difficult to realize the automation and intelligentization of the testing process, not only increasing the workload of the testers, but also possibly leading to poor repeatability of the test results due to improper parameter settings.

[0004] In traditional testing methods, for scenarios requiring multiple heat source arrangements, there is a lack of effective heat flow gradient analysis means, which cannot simulate the evolution process of the temperature field in real time, nor can it dynamically optimize the heat source configuration according to the actual heat conduction situation, which greatly limits the adaptability of the testing method to complex materials. Therefore, how to construct an automatic testing method that can comprehensively consider material structure, environmental parameters and heat source configuration has become a problem to be solved in the field of material thermal conductivity testing. SUMMARY

[0005] The present application aims to provide a material thermal conductivity automatic testing method based on heat flow gradient method to solve the problems raised in the background.

[0006] To achieve the above-mentioned purpose, the present application provides a material thermal conductivity automatic testing method based on heat flow gradient method, which comprises:

[0007] S1: Construct a test environment topology network, collect the three-dimensional structure parameters and environmental thermodynamic parameters of the measured material in real time, and input them into a thermal field simulation system to generate a heat flow distribution model inside the material;

[0008] S2: Analyze the cavity distribution characteristics inside the material according to the three-dimensional structure parameters, match them with a cavity feature-heat source layout mapping table to determine a steady-state heat source arrangement scheme, and activate a heat flow gradient analysis device when it is determined that multiple heat sources need to be arranged;

[0009] S3: Call a heat conduction parameter database, extract heat source power parameters and temperature collection parameters based on the environmental thermodynamic parameters, the heat source power parameters include heat source spacing, heat source axial angle, heat source strength and action depth, and the temperature collection parameters include thermocouple type and temperature collection frequency;

[0010] S4: Simulate the temperature field evolution process under the action of a steady-state heat source in the heat flow distribution model through the heat flow gradient analysis device, output axial temperature gradient curves, isothermal surface distribution data and heat conduction rate sets, analyze temperature gradient stability, heat source parameter matching degree and heat conduction uniformity, and generate parameter optimization instructions to adjust the heat source configuration.

[0011] Preferably, the specific construction process of the heat flow distribution model includes:

[0012] A1: Perform data format conversion on the three-dimensional structure parameters and environmental thermodynamic parameters of the measured material to adapt them to a thermodynamic calculation engine, and build a continuous material layered heat conduction model through a three-dimensional modeling tool;

[0013] A2: Divide different heat conduction regions of the material layered heat conduction model in the thermal field simulation system, give each region heat conduction attribute values according to the environmental thermodynamic parameters and perform local grid encryption to form a heat flow distribution model containing thermal physical parameters.

[0014] Preferably, the specific determination process of the steady-state heat source arrangement scheme includes:

[0015] Extract the equivalent diameter, distribution density and minimum spacing between cavities of the material from the three-dimensional structure parameters, divide the main cavity group and the secondary cavity group according to the preset cavity classification rule, and calculate the maximum distribution density of the main cavity group and the minimum spacing of the secondary cavity group, respectively;

[0016] Synchronize the maximum distribution density of the main cavity group and the minimum spacing of the secondary cavity group to the cavity feature-heat source layout mapping table to obtain the number of main heat sources and the number of auxiliary heat sources;

[0017] When the sum of the main heat source configuration number and the auxiliary heat source configuration number is zero, outputting a no-heat-source arrangement scheme, otherwise activating the heat flow gradient analysis device and outputting a multi-level heat source configuration scheme.

[0018] Preferably, the specific content of the cavity classification rule includes:

[0019] The cavities in the measured material with an axial angle less than a preset threshold and an equivalent diameter in the same interval are merged into a cavity group, and the average distribution density of the cavity group is taken as the main classification characteristic value.

[0020] Preferably, the heat conduction parameter database includes heat source power configuration logic and temperature acquisition configuration logic, wherein the heat source power configuration logic is:

[0021] (1) matching the distribution density of the main cavity group with the density-power correction coefficient table, multiplying the obtained correction coefficient by the reference heat source intensity value to generate the actual heat source intensity;

[0022] (2) when the heat source is located in the projection area of the main cavity group, the axial angle of the main cavity group is used as the axial angle of the heat source; when the heat source is located in the projection area of the secondary cavity group, the axial angle of the secondary cavity group is used as the axial angle of the heat source, and the direction of the axial angle is offset by a preset angle;

[0023] (3) extracting the specific heat capacity of the material from the environmental thermodynamic parameters, querying the specific heat capacity-acting depth correspondence table to obtain the reference acting depth, and generating the final acting depth after superimposing the material thickness compensation value.

[0024] Preferably, the specific content of the temperature acquisition configuration logic includes:

[0025] Matching the environmental thermodynamic parameters with the thermocouple characteristic database to obtain an adaptive thermocouple model and its reference acquisition frequency;

[0026] Based on the heat source intensity and the acting depth, the heat affected radius is calculated, the temperature acquisition frequency adjustment coefficient is determined according to the product of the heat affected radius and the reference acquisition frequency, and the final acquisition frequency of the thermocouple is corrected accordingly.

[0027] Preferably, the specific analysis method of the temperature gradient stability includes:

[0028] Extracting the slope change characteristics of the axial temperature gradient curve, when the slope fluctuation value continuously falls below a preset threshold and the isothermal surface distribution data presents a concentric diffusion form, it is determined that the temperature gradient stability meets the standard;

[0029] When the slope mutation exceeds a critical value or the isothermal surface distribution data appears non-continuous fracture, the temperature gradient stability is marked as abnormal and the heat source intensity calibration process is triggered.

[0030] Preferably, the specific analysis method of the heat source parameter matching degree comprises:

[0031] The theoretical thermal conductivity coefficient of the material is obtained from the environmental thermodynamic parameters, and the deviation rate of the theoretical isotherm surface spacing and the measured isotherm surface spacing is calculated in combination with the measured heat conduction rate in the heat conduction rate set;

[0032] When the deviation rate is lower than the preset proportion threshold, it is confirmed that the heat source parameter matching degree meets the standard, otherwise a heat source axial angle adjustment instruction is generated.

[0033] Preferably, the specific analysis method of the heat conduction uniformity comprises:

[0034] The range of the heat conduction rate at different axial positions in the heat conduction rate set is counted, and when the range is at the lower limit of the preset interval, the heat conduction uniformity level is marked as unqualified;

[0035] When the range is at the middle of the preset interval, the heat conduction uniformity level is marked as medium;

[0036] When the range is lower than the upper limit of the preset interval, the heat conduction uniformity level is marked as excellent.

[0037] Preferably, when the temperature gradient stability is marked as abnormal or the heat source parameter matching degree does not meet the standard, an automatic calibration cycle is triggered, which specifically comprises:

[0038] The heat source axial angle and the acting depth in the heat source power parameter are called as basic adjustment variables, the heat source axial angle offset is calculated according to the amplitude that the slope fluctuation value exceeds the critical value;

[0039] The acting depth compensation amount is generated according to the deviation rate of the theoretical isotherm surface spacing and the measured isotherm surface spacing, and the heat source configuration parameters in the heat flow gradient analysis device are adjusted synchronously;

[0040] The temperature field evolution process is simulated again under the updated heat source configuration parameters, and the temperature gradient stability and the heat source parameter matching degree verification are iteratively executed until the axial temperature gradient curve and the isotherm distribution data that meet the standard are generated.

[0041] Compared with the prior art, the beneficial effects of the present application are:

[0042] By constructing a test environment topology network, three-dimensional structure parameters and environmental thermodynamic parameters of the measured material can be collected in real time, and input into a heat field simulation system to generate a heat flow distribution model inside the material, which makes the initial conditions of the test more in line with the actual situation by fully considering the structure of the material itself and external environmental factors.

[0043] According to the three-dimensional structure parameter analysis material internal cavity distribution characteristics, and with cavity characteristics-heat source layout matching table matching determines the steady-state heat source layout scheme, when the need for multi-heat source layout activates the heat flow gradient analysis device, this way can be aimed at the specific structure characteristics of the material to plan heat source layout, avoid the test deviation caused by unreasonable heat source layout in the traditional method, make the heat source layout more targeted and scientific.

[0044] Call the heat conduction parameter database, extract the heat source power parameter and temperature collection parameter based on the environmental thermodynamic parameter, cover the heat source spacing, axial angle, strength, action depth and thermocouple type and temperature collection frequency, etc., let the parameter selection has reliable data support, no longer rely on artificial experience, reduce the interference of human factors to the test process, make the parameter configuration more accurate.

[0045] Through the heat flow gradient analysis device in the heat flow distribution model simulates the temperature field evolution process under the action of steady-state heat source, output axial temperature gradient curve, isothermal surface distribution data and heat conduction rate set, and analyzes the temperature gradient stability, heat source parameter matching degree and heat conduction uniformity, generates parameter optimization instruction to adjust heat source configuration, can dynamically respond to various changes in the heat conduction process, so that the heat source configuration is always in a reasonable state, so as to improve the adaptability and flexibility of the test process.

[0046] This automatic test method combines material structure analysis, environmental parameter consideration, heat source configuration optimization and other links organically, forms a closed-loop test system, which can cope with different structure and type of materials, and can play a role in various test scenes, showing strong versatility. At the same time, the whole test process realizes automatic operation, reduces the link of artificial intervention, makes the test process more smooth and efficient. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The working principle diagram of the material thermal conductivity automatic test method based on heat flow gradient method is described.

[0048] Figure 2 The flow chart for constructing the heat flow distribution model is described.

[0049] Figure 3 The flow chart for determining the steady-state heat source layout scheme is described.

[0050] Figure 4 The flow chart for heat source power configuration logic is described.

[0051] Figure 5 The flow chart for heat source parameter matching degree analysis is described. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0053] Please refer to Figure 1 The present application provides a material thermal conductivity automatic testing method based on heat flow gradient method, which comprises the following steps:

[0054] S1: Constructing a test environment topology network, collecting three-dimensional structure parameters and environmental thermodynamic parameters of the measured material in real time, and inputting them into a heat field simulation system to generate a heat flow distribution model inside the material. The three-dimensional structure parameters include spatial feature data such as the geometric size, internal pore distribution, and layered structure of the material; the environmental thermodynamic parameters include environmental temperature, humidity, atmospheric pressure, and initial temperature field distribution of the test system. The heat field simulation system performs multi-physical field coupling calculation based on the input parameters to generate a heat flow distribution model that can reflect the heat flow direction, intensity, and distribution law inside the material.

[0055] S2: Analyzing the internal cavity distribution characteristics of the material according to the three-dimensional structure parameters, matching them with a cavity feature-heat source layout mapping table to determine a steady-state heat source arrangement scheme, and activating a heat flow gradient analysis device when it is determined that multiple heat sources need to be arranged. The cavity distribution characteristics include the size, number, position, and spatial arrangement law of the cavities; the cavity feature-heat source layout mapping table is a pre-constructed database that stores the optimal heat source number, position, and power configuration rules corresponding to different cavity features. Whether multiple heat sources need to be arranged is determined through the matching process, and if so, the heat flow gradient analysis device is started for further analysis.

[0056] S3: Calling a heat conduction parameter database, and extracting heat source power parameters and temperature collection parameters based on environmental thermodynamic parameters. The heat source power parameters include heat source spacing (the spatial distance between adjacent heat sources), heat source axial angle (the included angle between the heat source action direction and the material reference axis), heat source intensity (the heat generated per unit time), and action depth (the internal material depth range affected by the heat source); the temperature collection parameters include thermocouple type (sensor model for measuring temperature) and temperature collection frequency (temperature measurement times per unit time). The heat conduction parameter database stores parameter configuration standards and calculation methods under different environmental conditions.

[0057] S4: Simulate the temperature field evolution process under the action of steady heat source in the heat flow distribution model by the heat flow gradient analysis device, output the axial temperature gradient curve, isothermal surface distribution data and heat conduction rate set, analyze the temperature gradient stability, heat source parameter matching degree and heat conduction uniformity, and generate parameter optimization instructions to adjust the heat source configuration. The axial temperature gradient curve reflects the temperature change rate along a specific axis; the isothermal surface distribution data reflects the distribution of regions with the same temperature inside the material; the heat conduction rate set contains the heat flow transfer speed at different positions. Through the analysis of the above data, it is judged whether the current heat source configuration is reasonable, and if there is deviation, optimization instructions are generated to adjust the power, position and other parameters of the heat source until the test requirements are met.

[0058] Embodiment 1: refer to Figure 2 The construction of the heat flow distribution model begins with the data processing of the three-dimensional structure parameters of the measured material and the environmental thermodynamic parameters. The three-dimensional structure parameters cover the overall geometric size, internal structure details of the material, including the shape, size, relative position relationship of each component, and possible microstructure characteristics such as pores and cracks. These parameters may come from point cloud data generated by three-dimensional scanning equipment, CAD design model or other three-dimensional modeling software output formats. Environmental thermodynamic parameters include temperature, humidity, atmospheric pressure of the test environment, and external factors affecting heat transfer such as thermal radiation intensity around the material and air flow speed. The units and data formats of these parameters may differ depending on the collection equipment, for example, temperature may be in Celsius or Kelvin, and pressure may be in Pascal or standard atmospheric pressure.

[0059] In order to enable these parameters to be effectively recognized and processed by the thermodynamic calculation engine, data format conversion is required. During the conversion process, the three-dimensional structure parameters are unified into a three-dimensional coordinate data format supported by the calculation engine, and the environmental thermodynamic parameters are converted into a unified numerical unit and data structure, such as converting temperature values in different units to Kelvin and converting pressure values to Pascal, and arranging them into a matrix or array form that can be directly read by the calculation engine. After completing the format conversion, with the help of three-dimensional modeling tools, a continuous material layered heat conduction model is constructed according to the actual layering of the material based on the processed three-dimensional structure parameters. During modeling, the thickness, interface characteristics and connection mode between layers of each layer of the material need to be accurately restored to ensure that the model can truly reflect the influence of the physical structure of the material on the heat conduction path, for example, for multi-layer composite materials, the boundary position and interlayer contact state of each layer need to be accurately reflected.

[0060] After the material layered heat conduction model is constructed, it is further processed in the thermal field simulation system. First, according to the physical property differences of the materials, the heat conduction region is divided. The division basis includes the composition, density distribution, crystal structure, etc. of each part of the material, because these factors directly determine the heat conduction performance of the material. For example, the heat conduction performance of the metal component region and the non-metal component region is significantly different, and needs to be divided into different heat conduction regions; the same component but different density parts may also have different heat conduction characteristics due to the difference in internal structure, and also need to be divided into regions.

[0061] After the division is completed, the corresponding heat conduction attribute value is given to each heat conduction region according to the environmental thermodynamic parameters. The heat conduction attribute value includes the heat conduction coefficient, specific heat capacity, thermal diffusivity and other thermal physical parameters. The determination of these parameters needs to be combined with the influence of environmental thermodynamic parameters. For example, the change of environmental temperature will cause the change of the heat conduction coefficient of some materials, at this time the basic heat conduction coefficient needs to be corrected according to the actual environmental temperature. For the region with complex heat flow change, such as the surrounding of the pores in the material and the interface of different materials, because the heat transfer process of these regions is often more complex, the heat flow gradient changes greatly, in order to improve the calculation accuracy of the model in these key regions, local grid refinement processing is needed. Grid refinement reduces the size of the grid unit in this region, increases the number of grids, so that the model can capture the subtle changes of heat flow more carefully.

[0062] After the region division, attribute assignment and local grid refinement, the heat flow distribution model containing the thermal physical parameters is finally formed. The model integrates the structural characteristics and thermal physical parameters of the material, and can accurately simulate the generation, transmission and distribution process of heat flow in the material, providing a reliable basic model for subsequent heat source arrangement, temperature field simulation and calculation of heat conduction coefficient. Each grid unit in the model contains corresponding thermal physical parameters and spatial coordinate information. The thermodynamic calculation engine can solve the heat conduction equation based on this information through numerical calculation method to simulate the heat flow distribution state in the material under different conditions.

[0063] Example 2: see Figure 3 The determination of the steady-state heat source arrangement scheme starts from the extraction and analysis of the cavity features in the material. From the three-dimensional structure parameters of the measured material, the key indicators related to the cavity are extracted, including the equivalent diameter, distribution density and minimum distance between cavities. The equivalent diameter is the diameter obtained by converting the irregularly shaped cavity into a circular shape with the same cross-sectional area. Through this parameter, the size characteristics of different shaped cavities can be uniformly described; the distribution density refers to the number of cavities per unit volume, reflecting the density of the cavities in the material; the minimum distance between cavities is the shortest distance between any two cavities, which is used to evaluate the uniformity of the cavity distribution.

[0064] After obtaining these parameters, the cavities are grouped according to the preset cavity classification rule to divide the main cavity group and the secondary cavity group. The specific content of the cavity classification rule is: the cavities in the measured material with an axial angle less than a preset threshold and an equivalent diameter in the same interval are merged into the same cavity group, and the average distribution density of the cavity group is taken as the main classification characteristic value. The preset threshold is set according to the material type and test requirements, for example, it can be set to 30 degrees, when the axial angles of two cavities are less than the value, it is considered that they have consistency in the spatial direction; the equivalent diameter interval can be divided into multiple levels, such as 0-2mm, 2-5mm, 5mm and above, and the cavities in the same interval are considered to have similar size characteristics. Through this rule, the cavities are divided into multiple cavity groups, and according to the average distribution density of each group, the group with higher distribution density and greater influence on heat conduction is selected as the main cavity group, and the rest is taken as the secondary cavity group.

[0065] After grouping, the maximum distribution density of the main cavity group and the minimum spacing of the secondary cavity group are calculated. The maximum distribution density of the main cavity group is the maximum value in the distribution density of all cavity groups in the group, which reflects the distribution of the main cavity in the most dense area inside the material; the minimum spacing of the secondary cavity group is the minimum value of the minimum distance between all cavities in the group, which reflects the compactness between the secondary cavities.

[0066] The calculated maximum distribution density of the main cavity group and the minimum spacing of the secondary cavity group are simultaneously input into the cavity feature-heat source layout mapping table to obtain the main heat source configuration number and the auxiliary heat source configuration number. The cavity feature-heat source layout mapping table is a pre-established database, which stores the correlation between different cavity feature parameters and the corresponding heat source configuration number. For example, when the maximum distribution density of the main cavity group exceeds a certain value, the mapping table will give the corresponding main heat source configuration number to ensure that the heat generated by the heat source can effectively cover the dense area of the main cavity; when the minimum spacing of the secondary cavity group is less than a certain threshold, the mapping table will determine the number of auxiliary heat sources that need to be configured to make up for the influence of the secondary cavity on the heat flow distribution.

[0067] According to the sum of the main heat source configuration number and the auxiliary heat source configuration number, the final steady-state heat source layout scheme is determined. When the sum is zero, it means that the influence of the cavity characteristics inside the material on heat conduction is very small, and no heat source needs to be arranged, at this time the no heat source layout scheme is output. If the sum is greater than zero, it means that heat sources need to be arranged, at this time the heat flow gradient analysis device is activated, and the multi-level heat source configuration scheme is output. The multi-level heat source configuration scheme clearly defines the number of main heat sources and auxiliary heat sources, their respective position distribution principles and the coordination relationship between them, for example, the main heat sources are mainly arranged in the projection area of the main cavity group, and the auxiliary heat sources are distributed around the secondary cavity group, so as to realize the precise regulation and control of the thermal field inside the material.

[0068] Example 3: see Figure 4The heat conduction parameter database comprises heat source power configuration logic and temperature acquisition configuration logic, which jointly act on the parameter setting of the heat source and the temperature acquisition device.

[0069] The heat source power configuration logic comprises three key links. The first link is to determine the actual heat source intensity by matching the distribution density of the main cavity group with the density-power correction coefficient table. In the density-power correction coefficient table, different distribution densities of the main cavity group correspond to different correction coefficients. The greater the distribution density, the higher the correction coefficient, because dense cavities will hinder the heat flow more strongly, and higher heat source intensity is needed to offset this effect. After matching the correction coefficient, it is multiplied by the reference heat source intensity value to generate the actual heat source intensity. The reference heat source intensity value is the basic heat source intensity set for materials without cavities or with negligible cavity effects under standard conditions, and its value is determined in advance according to the thermal properties of common materials.

[0070] The second link is to set the heat source axial angle. When the heat source is located in the projection area of the main cavity group, the axial angle of the main cavity group is directly used as the heat source axial angle. The axial angle of the main cavity group is the main distribution direction of the cavities in the group, and the heat source acts in this direction to more efficiently transfer heat to the surrounding area of the main cavity and reduce heat loss. When the heat source is located in the projection area of the secondary cavity group, the heat source axial angle needs to be offset by a preset angle in the direction of the secondary cavity group axial angle. The preset angle can be set according to the distribution characteristics of the secondary cavities, such as 10 degrees or 15 degrees, and this offset can avoid the mutual interference of heat flow between secondary cavities and make the heat field distribution more uniform.

[0071] The third link is to calculate the final action depth. First, the specific heat capacity of the material is extracted from the environmental thermodynamic parameters. The specific heat capacity is the heat required to raise the temperature of unit mass of a substance by 1 degree Celsius, and the specific heat capacity of different materials under different environmental conditions will differ. According to the extracted specific heat capacity, the reference action depth is obtained by querying the specific heat capacity-action depth correspondence table. In the specific heat capacity-action depth correspondence table, the greater the specific heat capacity, the greater the reference action depth, because a material with high specific heat capacity requires more heat to form a significant temperature gradient. Then, the reference action depth is superimposed with the material thickness compensation value to obtain the final action depth. The material thickness compensation value is calculated according to the difference between the actual thickness of the measured material and the standard test thickness. If the actual thickness is greater than the standard thickness, the compensation value is positive, and vice versa. Through this compensation, the action range of the heat source can cover the effective heat conduction area of the material.

[0072] The temperature collection configuration logic mainly involves thermocouple model selection and collection frequency adjustment. In terms of thermocouple model selection, the environmental thermodynamic parameters need to be matched with the thermocouple characteristic database. The environmental temperature range, humidity, and corrosive gas content in the environmental thermodynamic parameters all affect the measurement accuracy and service life of the thermocouple. The thermocouple characteristic database stores information such as the applicable environmental conditions, measurement range, and accuracy level of different types of thermocouples. By matching, the adaptive thermocouple model that can work stably under the current environment can be selected, and the reference collection frequency of the model can be obtained. The reference collection frequency is the default data collection rate of the thermocouple under standard working conditions, which can meet the temperature change recording requirements under general conditions.

[0073] In terms of collection frequency adjustment, the heat-affected radius is first calculated based on the heat source intensity and the action depth. The heat-affected radius refers to the radius of the area where the heat generated by the heat source can significantly affect the temperature distribution. The greater the heat source intensity and the deeper the action depth, the larger the heat-affected radius. Then, the temperature collection frequency adjustment coefficient is determined according to the product of the heat-affected radius and the reference collection frequency, as follows:

[0074]

[0075] where k is the temperature collection frequency adjustment coefficient, r is the calculated heat-affected radius, f b is the reference collection frequency, r b is the standard heat-affected radius, and f s is the standard collection frequency. Finally, the reference collection frequency is corrected with the adjustment coefficient to obtain the final collection frequency of the thermocouple. When the heat-affected radius is large, it indicates that the temperature field changes widely, and a higher collection frequency may be needed to capture subtle temperature changes. Conversely, if the heat-affected radius is small, the collection frequency can be appropriately reduced to reduce the amount of data. Through this adjustment, the temperature collection can meet the test accuracy requirements and avoid unnecessary resource waste.

[0076] Example 4: see Figure 5The analysis of the stability of the temperature gradient starts from the slope variation characteristics of the axial temperature gradient curve. The axial temperature gradient curve is a curve of the temperature change with distance at different positions along the preset axial direction (such as the length direction or the thickness direction of the material) in the heat flow distribution model, and the slope thereof reflects the temperature change rate per unit distance. The slope variation characteristics are obtained by continuously calculating the slope difference between two adjacent points in the curve, and these differences form the basis data of the slope fluctuation value. When the slope fluctuation value continuously falls below a preset threshold value within a period of time, and the isothermal surface distribution data presents a concentric diffusion pattern, it can be determined that the temperature gradient stability meets the standard. The isothermal surface distribution data is a surface formed by the collection of points with the same temperature inside the material, and the concentric diffusion pattern means that these surfaces are regularly circular or elliptical and expand outward with the heat source as the center, without obvious distortion, overlap or rupture. If a slope mutation exceeding the critical value is detected, that is, the slope difference between the current moment and the previous moment suddenly increases and exceeds the critical value, or the isothermal surface distribution data appears discontinuous rupture, that is, the isothermal surface is suddenly interrupted in a certain area and cannot form a complete continuous surface, the temperature gradient stability is marked as abnormal, and the heat source intensity calibration process is triggered. After the heat source intensity calibration process is started, the output power of the heat source will be gradually adjusted, and the slope variation and the isothermal surface distribution will be monitored in real time until the abnormal state is eliminated.

[0077] The analysis of the matching degree of the heat source parameters needs to combine the theoretical thermal conductivity coefficient of the material and the measured heat conduction rate. The theoretical thermal conductivity coefficient of the material is obtained from the environmental thermodynamic parameters, which are calculated based on the chemical composition, density of the material, and temperature, pressure and other conditions of the test environment, through thermodynamic theory. The measured heat conduction rate comes from the heat conduction rate set, which contains the heat conduction rate data of multiple monitoring points in the heat flow distribution model, and these data are obtained by simulating the heat transfer process under the action of a stable heat source. The theoretical isothermal surface spacing is determined by the theoretical thermal conductivity coefficient of the material and the heat source intensity, that is, the distance interval that the heat generated by the heat source per unit time can be transferred under the theoretical thermal conductivity coefficient; the measured isothermal surface spacing is obtained by analyzing the distribution of the same temperature points in the temperature collection data. The deviation rate of the theoretical isothermal surface spacing and the measured isothermal surface spacing is calculated, and when the deviation rate is lower than the preset proportion threshold, it is confirmed that the heat source parameter matching degree meets the standard; if the deviation rate exceeds the preset proportion threshold, a heat source axial angle adjustment instruction is generated, which contains the direction and amplitude suggestion of the angle adjustment.

[0078] When the temperature gradient stability marker is abnormal or the heat source parameter matching degree is substandard, an automatic calibration cycle is triggered. The automatic calibration cycle adjusts the heat source configuration parameters based on the heat source axial angle and the action depth in the heat source power parameters. For the case of abnormal temperature gradient stability, the heat source axial angle offset is calculated according to the amplitude of the slope fluctuation value exceeding the critical value. The greater the amplitude, the greater the offset. For example, when the slope fluctuation value exceeds the critical value by 50%, the axial angle offset may be set to 5 degrees, and when it exceeds 100%, the axial angle offset may be set to 10 degrees. For the case of substandard heat source parameter matching degree, the action depth compensation is generated according to the deviation rate of the theoretical isotherm interval and the measured isotherm interval. When the deviation rate is positive, it means that the measured heat-affected range is smaller than the theoretical value, and the compensation is positive, i.e. the action depth is increased. When the deviation rate is negative, the compensation is negative, i.e. the action depth is reduced.

[0079] After the axial angle offset and the action depth compensation are calculated, the heat source configuration parameters in the heat flow gradient analysis device are adjusted synchronously, including updating the axial angle, the action depth of the heat source, and the corresponding heat source intensity. After the adjustment is completed, the temperature field evolution process is simulated again under the updated heat source configuration parameters, and the axial temperature gradient curve, the isotherm distribution data and the heat conduction rate set are output again, and the temperature gradient stability and the heat source parameter matching degree are analyzed again. This process is repeated until the temperature gradient stability meets the standard and the heat source parameter matching degree meets the standard, and the required axial temperature gradient curve and isotherm distribution data are generated.

[0080] The following is an example of part of the monitoring data of the heat source parameter adjustment in the automatic calibration cycle:

[0081]

[0082]

[0083] In the above data, as the number of calibrations increases, the amplitude of the slope fluctuation value exceeding the critical value gradually decreases, the axial angle offset correspondingly decreases, the deviation rate gradually decreases, and the action depth compensation is adjusted accordingly, until all indicators meet the standard, and the calibration cycle ends.

[0084] Example 5: The analysis of heat conduction uniformity is based on the heat conduction rate set, which contains heat conduction rate data at different axial positions in the measured material. These data come from the simulation of the temperature field evolution process under the action of the steady heat source by the heat flow gradient analysis device, covering multiple key areas of the material, including the surface layer, the middle layer, the inner layer, and the interface of different layers, ensuring that the overall situation of heat conduction inside the material can be fully reflected.

[0085] The analysis process first extracts the thermal conductivity rate values at all axial positions from the thermal conductivity rate set, which can exhibit certain fluctuations, and the degree of fluctuation is related to the structural uniformity of the material, the rationality of the heat source distribution, and the environmental thermodynamic conditions. Then, the maximum and minimum values among these thermal conductivity rate values are calculated, and the difference between the two is the thermal conductivity rate range. The size of the range directly reflects the difference in thermal conductivity rate at different positions within the material. The larger the range, the more obvious the difference in thermal conductivity performance of each part of the material; the smaller the range, the more uniform the distribution of thermal conductivity within the material.

[0086] In order to quantitatively evaluate the thermal conductivity uniformity, a preset interval needs to be set, which is determined according to the type of the material, the application scenario, and the test accuracy requirement, and usually includes three parts: lower limit, middle section, and upper limit. The lower limit of the preset interval is the critical value for judging whether the thermal conductivity uniformity is qualified, the middle section is the range of basic qualification but with optimization space, and the upper limit is the ideal uniformity standard.

[0087] When the calculated thermal conductivity rate range is at the lower limit of the preset interval, the thermal conductivity uniformity level is marked as unqualified. This means that the thermal conductivity rate at different positions within the material is too different, which may be due to the existence of serious internal structural non-uniformity in the material, such as a large number of unevenly distributed pores, cracks, or significant composition differences in different regions, resulting in a large difference in the degree of obstruction to heat flow during transmission. At this time, the heat conduction process is difficult to stabilize, which may affect the accuracy of the test results of the thermal conductivity coefficient of the material.

[0088] When the thermal conductivity rate range is in the middle section of the preset interval, the thermal conductivity uniformity level is marked as medium. In this case, there is a certain difference in the thermal conductivity rate within the material, but it has not reached the level that affects the basic accuracy of the test. The reason for this may be that the material has a small number of uniformly distributed pores, or the composition of the material has a slight fluctuation in the local region, causing the heat flow transmission to be affected in some areas, but the overall still maintains a relatively stable conduction trend.

[0089] When the thermal conductivity rate range is lower than the upper limit of the preset interval, the thermal conductivity uniformity level is marked as excellent. This indicates that the thermal conductivity rate at different positions within the material is small, and the heat flow can be uniformly transmitted within the material. This is usually because the material structure is uniform, there are no obvious pores, cracks, or composition differences, and the heat generated by the heat source can diffuse stably in all directions, forming a uniform temperature field distribution.

[0090] Through the judgment of the uniformity level of heat conduction, the distribution of heat conduction inside the material can be clearly understood, which provides a reference for whether the heat source configuration needs to be adjusted. If the level is unqualified or medium, it may be necessary to reevaluate the arrangement position, intensity distribution and other parameters of the heat source to improve the uniformity of heat conduction inside the material; if the level is excellent, it indicates that the current heat source configuration and material state can meet the test requirements, and the calculation and analysis of the thermal conductivity coefficient can be continued based on this.

[0091] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0092] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An automatic method for testing the thermal conductivity of materials based on the heat flux gradient method, characterized in that, Includes the following steps: S1: Construct a test environment topology network, collect the three-dimensional structural parameters of the material under test and the environmental thermodynamic parameters in real time, and input them into the thermal field simulation system to generate a heat flow distribution model inside the material; S2: Analyze the internal cavity distribution characteristics of the material based on the three-dimensional structural parameters, match them with the cavity characteristics-heat source layout mapping table to determine the steady-state heat source layout scheme, and activate the heat flow gradient analysis device when it is determined that multiple heat source layouts are needed. S3: Call the heat conduction parameter database and extract the heat source power parameters and temperature acquisition parameters based on the environmental thermodynamic parameters. The heat source power parameters include the heat source distance, heat source axial angle, heat source intensity and depth of action. The temperature acquisition parameters include the thermocouple type and temperature acquisition frequency. S4: The heat flow gradient analysis device simulates the temperature field evolution process under the action of a steady-state heat source in the heat flow distribution model, outputs the axial temperature gradient curve, isothermal surface distribution data and heat conduction rate set, analyzes the temperature gradient stability, heat source parameter matching degree and heat conduction uniformity, and generates parameter optimization instructions to adjust the heat source configuration.

2. The automatic testing method for the thermal conductivity of materials based on the heat flux gradient method according to claim 1, characterized in that, The specific construction process of the heat flow distribution model includes: A1: Perform data format conversion on the three-dimensional structural parameters of the material under test and the environmental thermodynamic parameters to adapt it to the thermodynamic calculation engine, and construct a continuous material layered thermal conductivity model through a three-dimensional modeling tool; A2: In the thermal field simulation system, the material layered thermal conductivity model is divided into different thermal conductivity regions. Based on the environmental thermodynamic parameters, each region is assigned a thermal conductivity attribute value and the local mesh is refined to form a heat flow distribution model that includes thermal property parameters.

3. The automatic testing method for the thermal conductivity of materials based on the heat flux gradient method according to claim 1, characterized in that, The specific process for determining the steady-state heat source layout scheme includes: The equivalent diameter, distribution density, and minimum spacing between cavities inside the material are extracted from the three-dimensional structural parameters. The main cavity group and the secondary cavity group are divided according to the preset cavity classification rules. The maximum distribution density of the main cavity group and the minimum spacing of the secondary cavity group are calculated respectively. The maximum distribution density of the main cavity group and the minimum spacing of the secondary cavity group are synchronously input into the cavity feature-heat source layout mapping table to obtain the number of main heat source configurations and the number of auxiliary heat source configurations. When the sum of the number of main heat source configurations and the number of auxiliary heat source configurations is zero, a heat source-free layout scheme is output; otherwise, the heat flow gradient analysis device is activated and a multi-level heat source configuration scheme is output.

4. The automatic testing method for the thermal conductivity of materials based on the heat flux gradient method according to claim 3, characterized in that, The specific content of the cavity classification rules includes: Cavities in the tested material with axial angles less than a preset threshold and equivalent diameters within the same range are grouped into the same cavity group, and the average distribution density of this cavity group is used as the main classification feature value.

5. The automatic testing method for the thermal conductivity of materials based on the heat flux gradient method according to claim 3, characterized in that, The heat conduction parameter database includes heat source power configuration logic and temperature acquisition configuration logic, wherein the heat source power configuration logic is as follows: (1) Match the distribution density of the main cavity group with the density-power correction coefficient table, and multiply the obtained correction coefficient by the reference heat source intensity value to generate the actual heat source intensity. (2) When the heat source is located in the projection area of ​​the main cavity group, the axial angle of the main cavity group is used as the axial angle of the heat source; when the heat source is located in the projection area of ​​the secondary cavity group, the direction of the axial angle of the secondary cavity group offset by a preset angle is used as the axial angle of the heat source. (3) Extract the specific heat capacity of the material from the environmental thermodynamic parameters, query the specific heat capacity-deep action relationship table to obtain the reference depth of action, and generate the final depth of action by superimposing the material thickness compensation value.

6. The automatic testing method for the thermal conductivity of materials based on the heat flux gradient method according to claim 5, characterized in that, The specific content of the temperature acquisition configuration logic includes: The environmental thermodynamic parameters are matched with the thermocouple characteristic database to obtain the compatible thermocouple model and its reference acquisition frequency. The thermal influence radius is calculated based on the heat source intensity and the depth of influence. The temperature acquisition frequency adjustment coefficient is determined by multiplying the thermal influence radius by the reference acquisition frequency, and the final acquisition frequency of the thermocouple is corrected accordingly.

7. The automatic testing method for the thermal conductivity of materials based on the heat flux gradient method according to claim 1, characterized in that, The specific methods for analyzing the stability of the temperature gradient include: Extract the slope change characteristics of the axial temperature gradient curve. When the slope fluctuation value is continuously lower than the preset threshold and the isothermal surface distribution data shows a concentric diffusion pattern, it is determined that the temperature gradient stability meets the standard. When a slope abrupt change is detected that exceeds the critical value or when there is a discontinuous break in the isothermal surface distribution data, the temperature gradient stability is marked as abnormal and the heat source intensity calibration process is triggered.

8. The automatic testing method for the thermal conductivity of materials based on the heat flux gradient method according to claim 1, characterized in that, The specific analysis methods for the matching degree of the heat source parameters include: The theoretical thermal conductivity of the material is obtained from the aforementioned environmental thermodynamic parameters. Combined with the measured thermal conductivity in the set of thermal conductivity rates, the deviation rate between the theoretical isothermal surface spacing and the measured isothermal surface spacing is calculated. When the deviation rate is lower than the preset ratio threshold, the matching degree of the heat source parameters is confirmed to be up to standard; otherwise, a heat source axial angle adjustment command is generated.

9. The automatic testing method for the thermal conductivity of materials based on the heat flux gradient method according to claim 1, characterized in that, The specific methods for analyzing the uniformity of heat conduction include: The range of heat conduction rates at different axial positions in the set of heat conduction rates is statistically analyzed. When the range is at the lower limit of a preset interval, the heat conduction uniformity level is marked as unqualified. When the range is in the middle of the preset range, the heat conduction uniformity level is marked as medium. When the range is lower than the upper limit of the preset range, the heat conduction uniformity level is marked as excellent.

10. The temperature gradient stability analysis result according to claim 1 and the heat source parameter matching degree analysis result according to claim 8, characterized in that, An automatic calibration cycle is triggered when the temperature gradient stability flag is abnormal or the heat source parameter mismatch is not met. Specifically, this includes: The axial angle and depth of action of the heat source power parameters are used as the basic adjustment variables, and the axial angle offset of the heat source is calculated based on the extent to which the slope fluctuation value exceeds the critical value. The depth of action compensation is generated based on the deviation rate between the theoretical isothermal surface spacing and the measured isothermal surface spacing, and the heat source configuration parameters in the heat flux gradient analysis device are adjusted synchronously. The temperature field evolution process was re-simulated under the updated heat source configuration parameters. The temperature gradient stability and heat source parameter matching degree were iteratively verified until the standard axial temperature gradient curve and isothermal surface distribution data were generated.

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