Method, system and equipment for evaluating heat insulation performance of heat insulation sponge and medium

Abstract

The invention relates to a heat insulation performance evaluation method, system and equipment of a heat insulation sponge and a medium, and belongs to the technical field of heat insulation material performance evaluation, and the performance evaluation method comprises the following steps: obtaining initial parameters of a to-be-detected heat insulation sponge sample; receiving and storing a temperature-heat transfer curve of the heat insulation sponge sample, generating a temperature-heat conductivity curve of the heat insulation sponge sample, and calculating to obtain thermal diffusion coefficient data; receiving heat map data, calibrating a temperature response based on a thermal diffusion coefficient, and generating a temperature distribution matrix; calculating temperature data of different depth layers in the heat insulation sponge sample; receiving a high-low temperature heat insulation attenuation curve obtained by the heat insulation sponge sample under the high-low temperature cycle test; receiving a humidity-temperature heat insulation attenuation data set obtained by the heat insulation sponge sample under a humidity-temperature cycle test; and obtaining heat insulation performance data according to the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation data set. The method can accurately evaluate the heat insulation performance of the heat insulation sponge material.

Description

Technical Field

[0001] The present application relates to the technical field of thermal insulation material performance evaluation, and in particular to a thermal insulation performance evaluation method, system, equipment and medium for thermal insulation sponge. Background Art

[0002] As an important thermal insulation material, thermal insulation sponge is widely used in construction, industrial equipment, transportation and other fields to achieve the goals of reducing heat loss, saving energy and improving safety of use. The thermal insulation performance of thermal insulation sponge mainly depends on its material structure and physical properties, such as porosity, pore size, material thickness, etc. These factors directly affect the thermal conductivity and thermal insulation effect of the material. However, since thermal insulation materials often need to face complex environmental conditions in actual applications, such as drastic temperature fluctuations and high humidity, the thermal insulation effect of thermal insulation sponge often decays after long-term use or in specific environments.

[0003] At present, the existing evaluation methods for thermal insulation materials mainly use basic physical parameters such as heat transfer and thermal conductivity under a single temperature difference to preliminarily judge the thermal insulation performance of the material. However, such evaluation methods often ignore the changes in the thermal insulation performance of the material under the superposition of multiple environmental factors, and are unable to evaluate the thermal insulation attenuation of the material under different environments.

[0004] In practical applications, the long-term thermal insulation performance of thermal insulation materials is particularly critical, especially in environments with large temperature differences. The sustainability of the thermal insulation effect of the material is directly related to the energy-saving effect and safety of its application. Therefore, how to accurately and comprehensively evaluate the thermal insulation performance of thermal insulation sponge materials under a variety of complex environmental conditions is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In order to accurately evaluate the thermal insulation performance of thermal insulation sponge materials, the present application provides a thermal insulation performance evaluation method, system, equipment and medium for thermal insulation sponge.

[0006] In the first aspect, the present application provides a method for evaluating the thermal insulation performance of a thermal insulation sponge, using the following technical solution: A method for evaluating the thermal insulation performance of a thermal insulation sponge, the performance evaluation method comprising: Obtaining initial parameters of the thermal insulation sponge sample to be tested; wherein the initial parameters include sample thickness, sample surface area, pore density and pore size of the thermal insulation sponge sample; Receive and store the heat transfer data of the thermal insulation sponge sample under different temperature difference conditions to obtain a temperature-heat transfer curve of the thermal insulation sponge sample; Based on the temperature-heat transfer curve and the sample thickness, the temperature-thermal conductivity curve of the thermal insulation sponge sample is generated by processing with a steady-state heat flow formula, and the thermal diffusion coefficient data is calculated; Receiving thermal map data of the surface temperature distribution of the thermal insulation sponge sample, and calibrating the temperature response based on the thermal diffusion coefficient to generate a temperature distribution matrix; Based on the temperature distribution matrix and the thermal diffusion coefficient, temperature data of different depth layers inside the thermal insulation sponge sample are calculated to generate a temperature grid data set; Receiving temperature change data of the thermal insulation sponge sample obtained under the high and low temperature cycle test, and combining the initial temperature distribution in the temperature grid data set to obtain a high and low temperature thermal insulation attenuation curve; Receiving temperature change data and humidity change data obtained by the thermal insulation sponge sample under the humidity-temperature cycle test, and comparing them with the high and low temperature thermal insulation attenuation curves, and obtaining a humidity-temperature thermal insulation attenuation data set according to the comparison results; The thermal insulation attenuation trend analysis is performed based on the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set to obtain the thermal insulation performance data of the thermal insulation sponge sample.

[0007] By adopting the above technical solution, a multi-level and comprehensive analysis of the temperature and humidity change data of the thermal insulation sponge samples to be tested can not only quantify the thermal insulation attenuation trend of the samples under multiple conditions such as high and low temperature and humidity environments, but also provide an evaluation of the thermal insulation durability and stability of the samples. This technical solution provides a scientific, data-driven reference system for the performance analysis of thermal insulation materials, making the selection, optimization and application of thermal insulation materials more reliable.

[0008] Optionally, the step of receiving thermal map data of the surface temperature distribution of the thermal insulation sponge sample and calibrating the temperature response based on the thermal diffusion coefficient to generate a temperature distribution matrix includes: Receiving thermal map data of surface temperature distribution of the thermal insulation sponge sample; Calculating the temperature response delay time of the surface of the thermal insulation sponge sample based on the thermal diffusion coefficient; Adjusting the temperature amplitude in the thermal map data based on the temperature response delay time to calibrate each temperature value in the thermal map data; Arranging the calibrated temperature values ​​into a temperature distribution matrix according to the original position information of the thermal map data; The calibration accuracy of the temperature distribution matrix is ​​quantified based on a statistical method, and the temperature distribution matrix is ​​verified according to the calibration accuracy.

[0009] Optionally, the step of calculating the temperature data of different depth layers inside the thermal insulation sponge sample based on the temperature distribution matrix and the thermal diffusion coefficient to generate a temperature grid data set includes: Determine the number of depth layers according to the sample thickness of the thermal insulation sponge sample and the preset layer spacing, and obtain a depth layer list; Based on the temperature distribution matrix and the thermal diffusion coefficient, the temperature of the first layer in the depth layer list is calculated according to the heat transfer equation; Based on the temperature of the first layer in the depth layer list, the temperature of each layer in the depth layer list is calculated in turn according to the heat transfer formula to obtain temperature data of all depth layers; The temperature data of all depth layers are arranged according to the depth layer list to construct a temperature grid data set.

[0010] Optionally, the step of receiving the temperature change data of the thermal insulation sponge sample obtained under the high and low temperature cycle test and combining the initial temperature distribution in the temperature grid data set to obtain the high and low temperature insulation attenuation curve includes: Receiving temperature change data of the thermal insulation sponge sample obtained under high and low temperature periodic cycle test; Based on the initial temperature distribution in the temperature grid data set, normalizing the temperature change data obtained from each temperature cycle test; Arranging the normalized temperature change data according to the depth layer list to obtain a temperature change matrix of different depth layers; Based on the temperature change matrix, calculate the temperature decay rate of each depth layer; The temperature decay rates of each depth layer in each temperature cycle test are arranged in chronological order to obtain the high and low temperature insulation decay curves.

[0011] Optionally, the step of receiving the temperature change data and humidity change data obtained by the thermal insulation sponge sample under the humidity-temperature cycle test, and comparing them with the high and low temperature thermal insulation attenuation curve, and obtaining the humidity-temperature thermal insulation attenuation data set according to the comparison result includes: Receiving temperature change data and humidity change data obtained by the thermal insulation sponge sample under a humidity-temperature periodic cycle test; Normalizing the temperature change data and the humidity change data; Arrange the normalized temperature change data and humidity change data according to the depth layer list to obtain humidity-temperature change matrices of different depth layers; Based on the humidity-temperature change matrix, the temperature attenuation rate of each depth layer is calculated to generate a corresponding temperature attenuation rate matrix; Comparing the temperature attenuation rate in the temperature attenuation rate matrix with the temperature attenuation rate in the high and low temperature insulation attenuation curve layer by layer to obtain a comparison result; Based on the comparison results, a humidity-temperature insulation attenuation data set is established.

[0012] Optionally, the step of performing thermal insulation attenuation trend analysis according to the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set to obtain thermal insulation performance data of the thermal insulation sponge sample includes: Normalizing the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set; Compare the normalized high and low temperature thermal insulation attenuation curves with the humidity-temperature thermal insulation attenuation data set layer by layer to obtain a difference matrix; Based on the difference matrix, the thermal insulation decay trend curve of the thermal insulation sponge sample is calculated by a trend fitting algorithm, and a thermal insulation decay trend model is obtained by fitting; Based on the thermal insulation attenuation trend model, the comprehensive thermal insulation attenuation rate under high and low temperature conditions and humidity-temperature conditions is calculated; The thermal insulation performance data of the thermal insulation sponge sample is obtained according to the thermal insulation decay trend model and the comprehensive thermal insulation decay rate.

[0013] Optionally, after the step of obtaining the humidity-temperature insulation attenuation data set according to the comparison result, the method further includes: Inputting the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set into a pre-constructed finite element model to obtain a finite element microstructure model; Adjusting the internal parameters of the finite element microstructure model based on the temperature grid data set to obtain a microstructure simulation model of the thermal insulation sponge sample; Adjusting the pore density and pore size parameters in the microstructure simulation model, calculating the thermal insulation performance data of the model according to each set of parameter combinations, and generating a thermal insulation performance optimization data set; Based on the thermal insulation performance optimization data set, the optimal parameter combination of the thermal insulation performance data is selected to generate structural optimization recommendation information for the thermal insulation sponge sample.

[0014] In a second aspect, the present application provides a thermal insulation performance evaluation system for thermal insulation sponge, which adopts the following technical solution: A thermal insulation performance evaluation system for a thermal insulation sponge, the performance evaluation system comprising: A parameter acquisition module, used to acquire initial parameters of the thermal insulation sponge sample to be tested; wherein the initial parameters include sample thickness, sample surface area, pore density and pore size of the thermal insulation sponge sample; A first data processing module is used to receive and store the heat transfer data of the thermal insulation sponge sample under different temperature difference conditions to obtain a temperature-heat transfer curve of the thermal insulation sponge sample; A second data processing module is used to generate a temperature-thermal conductivity curve of the thermal insulation sponge sample based on the temperature-heat transfer curve and the sample thickness through a steady-state heat flow formula, and calculate the thermal diffusion coefficient data; A temperature distribution matrix generation module is used to receive the thermal map data of the surface temperature distribution of the thermal insulation sponge sample, and generate a temperature distribution matrix based on the thermal diffusion coefficient calibrated temperature response; A temperature grid data set generation module, used to calculate the temperature data of different depth layers inside the thermal insulation sponge sample based on the temperature distribution matrix and the thermal diffusion coefficient, and generate a temperature grid data set; The third data processing module is used to receive the temperature change data obtained by the thermal insulation sponge sample under the high and low temperature cycle test, and obtain the high and low temperature insulation attenuation curve in combination with the initial temperature distribution in the temperature grid data set; A fourth data processing module is used to receive the temperature change data and humidity change data obtained by the thermal insulation sponge sample under the humidity-temperature cycle test, and compare them with the high and low temperature thermal insulation attenuation curves, and obtain a humidity-temperature thermal insulation attenuation data set according to the comparison results; The thermal insulation performance data generation module is used to perform thermal insulation attenuation trend analysis based on the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set to obtain the thermal insulation performance data of the thermal insulation sponge sample.

[0015] In a third aspect, the present application provides a computer device, which adopts the following technical solution: A computer device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the methods in the first aspect.

[0017] In summary, the present application includes at least one of the following beneficial technical effects: The present application provides a quantitative thermal insulation performance analysis method for thermal insulation materials in complex environments, which can not only support the optimized design of materials, but also be applied in the fields of construction, industry, etc. to ensure the stable thermal insulation performance of materials in different environments. Through a scientific evaluation system and data guidance, it provides a reliable basis for material selection and research and development, and promotes the efficient application and long-term performance improvement of thermal insulation materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a first flow chart of a method for evaluating the thermal insulation performance of a thermal insulation sponge according to one embodiment of the present application.

[0019] Figure 2 It is a second flow chart of the method for evaluating the thermal insulation performance of the thermal insulation sponge according to one embodiment of the present application.

[0020] Figure 3 It is a third flow chart of the thermal insulation performance evaluation method of the thermal insulation sponge according to one embodiment of the present application.

[0021] Figure 4 It is a fourth flow chart of a method for evaluating the thermal insulation performance of a thermal insulation sponge according to one embodiment of the present application.

[0022] Figure 5 It is a fifth flow chart of a method for evaluating the thermal insulation performance of a thermal insulation sponge according to one embodiment of the present application.

[0023] Figure 6 It is a sixth flow chart of a method for evaluating the thermal insulation performance of a thermal insulation sponge according to one embodiment of the present application.

[0024] Figure 7 It is the seventh flow chart of the thermal insulation performance evaluation method of the thermal insulation sponge according to one embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-7 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] In the field of thermal insulation materials, thermal insulation performance evaluation has always been a key link in material research and development and application. Traditional thermal insulation performance evaluation methods usually only focus on the thermal conductivity and thermal diffusivity of materials under standard experimental conditions, which is difficult to fully reflect the actual performance of materials in complex environments. However, the use environment of thermal insulation materials often faces a variety of extreme conditions, such as high and low temperature cycles, humidity changes, etc. These environmental factors will have a significant impact on the thermal insulation performance of the material, causing it to exhibit different thermal conduction and attenuation characteristics in actual applications.

[0027] In addition, the microstructure of thermal insulation materials (such as pore density and pore size) plays a vital role in thermal insulation performance. Traditional evaluation methods are difficult to deeply analyze the relationship between its microstructure and thermal insulation performance. Therefore, how to dynamically evaluate the thermal insulation performance of thermal insulation materials under various environmental factors, analyze the microstructural characteristics of the materials, and optimize the material structure to achieve the best thermal insulation effect has become an important demand in the current research on thermal insulation material technology.

[0028] Based on this, an embodiment of the present application discloses a method for evaluating the thermal insulation performance of an insulation sponge.

[0029] Reference Figure 1 , a method for evaluating the thermal insulation performance of a thermal insulation sponge, the performance evaluation method comprising: Step S101, obtaining initial parameters of the thermal insulation sponge sample to be tested; Among them, the initial parameters include the sample thickness, sample surface area, pore density and pore size of the thermal insulation sponge sample; Specifically, the initial parameters of the sample (thickness, pore density and pore size) are important basic parameters of thermal insulation performance. Porosity and pore size directly affect the thermal insulation capacity of the material. These characteristics determine the distribution of air inside the material and its heat conduction path, which helps the accuracy of subsequent data processing steps and ensures that the calculated thermal conductivity and thermal diffusion coefficient accurately reflect the true properties of the material.

[0030] Step S102, receiving and storing heat transfer data of the thermal insulation sponge sample under different temperature difference conditions, and obtaining a temperature-heat transfer curve of the thermal insulation sponge sample; Among them, thermal insulation performance is usually measured by the change of heat transfer with temperature difference. The temperature-heat transfer curve is used to describe the heat transfer of the sample under different temperature difference conditions, reflecting the basic thermal insulation behavior of the material.

[0031] For example, the sample is placed in an environment with a temperature difference of 10°C, 20°C, and 30°C, and the corresponding heat transfer values ​​are measured to be 0.1W, 0.2W, and 0.3W, respectively. Thus, the corresponding temperature-heat transfer curve can be obtained, which can be used to calculate the thermal conductivity of the material in subsequent steps, laying a data foundation for the quantitative analysis of thermal insulation performance.

[0032] Step S103, based on the temperature-heat transfer curve and the sample thickness, a temperature-thermal conductivity curve of the thermal insulation sponge sample is generated by processing with a steady-state heat flow formula, and thermal diffusion coefficient data is calculated; Among them, thermal conductivity is a key indicator to measure the heat transfer capacity of materials, which describes the heat conduction rate of materials under stable temperature conditions. Specifically, the calculation of thermal conductivity is usually based on the steady-state heat flow formula. By combining parameters such as temperature difference, heat transfer and material thickness, a temperature-thermal conductivity curve can be generated. Using the sample thickness, the thermal diffusion coefficient of the material is further calculated to characterize its transient heat conduction capacity.

[0033] Step S104, receiving thermal map data of the surface temperature distribution of the thermal insulation sponge sample, and calibrating the temperature response based on the thermal diffusion coefficient to generate a temperature distribution matrix; Among them, the thermal map data of the surface temperature distribution provides the temperature distribution map of the sample under heating conditions. The thermal map data is calibrated by the thermal diffusion coefficient, the delay and amplitude of the temperature response are adjusted, and the temperature distribution matrix of the sample is generated to accurately reflect the temperature distribution of its surface.

[0034] For example, assuming that the thermal map data shows that the temperature distribution at different points on the sample surface is [30°C, 32°C, 34°C], after calibration by the thermal diffusion coefficient, the temperature distribution matrix data is adjusted to [29.8°C, 32.1°C, 34.2°C] to more accurately reflect the actual thermal insulation effect of the material, providing reliable surface data for further analysis of the temperature distribution at different depths.

[0035] Step S105, calculating the temperature data of different depth layers inside the thermal insulation sponge sample based on the temperature distribution matrix and the thermal diffusion coefficient, and generating a temperature grid data set; Among them, according to the surface temperature distribution and thermal diffusion coefficient, the heat transfer equation is used to calculate the temperature data at different depths inside the sample layer by layer, and a temperature grid data set is gradually generated from the surface to the inside. This grid data set shows the temperature gradient inside the sample.

[0036] For example, assuming that the surface temperature of the sample is 30°C, the thermal diffusion coefficient is calculated layer by layer, and the temperatures of different internal depth layers (1mm, 2mm, 3mm, etc.) are obtained to be [29°C, 28°C, 27°C]. This provides a complete temperature grid data set to accurately reflect the temperature distribution inside the sample, providing data support for the multi-level evaluation of the thermal insulation effect and a reference for the benchmark temperature of subsequent attenuation tests.

[0037] Step S106, receiving the temperature change data of the thermal insulation sponge sample obtained under the high and low temperature cycle test, combining the initial temperature distribution in the temperature grid data set, and obtaining the high and low temperature insulation attenuation curve; Among them, the high and low temperature cycle test data records the temperature changes of the insulation sponge samples in a cyclic temperature difference environment. By combining the temperature change data with the temperature grid data set, it is possible to generate high and low temperature insulation attenuation curves based on the temperatures of different depth layers, so as to analyze the thermal insulation performance attenuation of the material under cyclic temperature difference conditions, revealing the stability of its thermal insulation performance and providing a reference for the durability of insulation materials under extreme temperatures.

[0038] For example, assuming that the initial temperature grid of the thermal insulation sponge sample in the high and low temperature cycle test is [30°C, 29°C, 28°C], after multiple cycles, the rate of change of the internal temperature grid slows down, thereby forming a temperature attenuation curve.

[0039] Step S107, receiving the temperature change data and humidity change data obtained by the thermal insulation sponge sample under the humidity-temperature cycle test, and comparing them with the high and low temperature thermal insulation attenuation curves, and obtaining a humidity-temperature thermal insulation attenuation data set according to the comparison results; Among them, since humidity can affect the thermal conductivity of materials, by comparing the humidity-temperature cycle test data with the high and low temperature attenuation curves, the impact of humidity changes on the thermal insulation performance of materials can be observed, and a humidity-temperature thermal insulation attenuation data set can be obtained to further evaluate the performance stability of materials in humid environments.

[0040] For example, the temperature decay rate of a sample may accelerate in a high humidity environment. Assuming that the comparison curve shows that increased humidity will cause the thermal insulation performance of the sample to decrease by 5%, humidity-temperature thermal insulation decay data can be generated to reflect the thermal insulation durability of the material under different humidity levels, providing support for material selection in humidity-sensitive environments.

[0041] Step S108, performing thermal insulation attenuation trend analysis based on the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set to obtain thermal insulation performance data of the thermal insulation sponge sample.

[0042] Specifically, based on the comparison of high and low temperature insulation attenuation curves and humidity-temperature insulation attenuation data sets, the attenuation trend algorithm can be used to calculate the insulation attenuation trend of the material, thereby obtaining the insulation performance data of the sample to quantify the insulation performance attenuation of the sample under various environmental factors, and provide a scientific basis for the stability and applicability of the material in long-term insulation applications. For example, after calculating the attenuation trend, the comprehensive attenuation rate of the insulation performance of the insulation sponge sample was 0.8% / year, which reflects the long-term insulation stability of the insulation sponge.

[0043] In the above implementation, the temperature and humidity change data of the thermal insulation sponge sample to be tested are analyzed at multiple levels and in an all-round manner, which can not only quantify the thermal insulation attenuation trend of the sample under multiple conditions such as high and low temperature and humidity environment, but also provide an evaluation of the thermal insulation durability and stability of the sample. This technical solution provides a scientific, data-driven reference system for the performance analysis of thermal insulation materials, making the selection, optimization and application of thermal insulation materials more reliable.

[0044] As an implementation method for obtaining thermal diffusion coefficient data, the specific steps include; Step S1031, based on the temperature-thermal conductivity curve and the surface area data of the thermal insulation sponge sample, the heat flux density q under steady-state conditions is calculated according to the following formula (1); (1); Where Q represents the heat transfer, and A represents the sample surface area; Step S1032, based on the heat flux density and the temperature-thermal conductivity curve, calculate the thermal conductivity data k according to the following steady-state heat flux formula (2); (2); Where d represents the sample thickness, ΔT represents the temperature difference; Based on the thermal conductivity data and sample thickness data, the thermal diffusion coefficient α is calculated according to the following formula (3): (3); Where ρ is the sample density (i.e., apparent density, which can be calculated based on the mass and volume of the thermal insulation sponge sample); c p is the specific heat capacity of the sample, which can be obtained by querying the material database according to the composition of the thermal insulation sponge sample.

[0045] Reference Figure 2 As an implementation of step S103, the steps of receiving the thermal map data of the surface temperature distribution of the thermal insulation sponge sample and calibrating the temperature response based on the thermal diffusion coefficient to generate the temperature distribution matrix include: Step S201, receiving thermal map data of surface temperature distribution of thermal insulation sponge samples; Among them, the surface of the thermal insulation sponge sample will show different temperature distributions under heating conditions. Thermal map data is usually collected by infrared imaging equipment to measure the temperature values ​​at different locations on the sample surface. These raw thermal map data represent uncalibrated temperature distributions. Due to the influence of thermal diffusion, these data may contain time response delays and amplitude errors. Assume that the temperature readings of the thermal map data at different points on the sample surface are [30°C, 32°C, 34°C], and these values ​​correspond to the temperatures at different surface coordinates.

[0046] Step S202, calculating the temperature response delay time of the surface of the thermal insulation sponge sample based on the thermal diffusion coefficient; Specifically, the delay time of the temperature response is the time difference for the temperature to be transferred to each location on the surface of the material. This delay depends on the thermal diffusion coefficient of the material, which characterizes the speed at which the material conducts heat. The thermal diffusion coefficient α is used to calculate the delay time t of the temperature response. delay , which can make the temperature change in the thermal diffusion process get reasonable time calibration. The calculation formula of temperature delay time is: (4); Where d is the distance between two adjacent measurement points in the heat map.

[0047] Step S203, adjusting the temperature amplitude in the thermal map data based on the temperature response delay time to calibrate each temperature value in the thermal map data; The temperature amplitude calibration is based on the response delay time. By calibrating the temperature values ​​in the thermal map data one by one, each temperature value is ensured to be closer to the actual surface temperature. During the thermal diffusion process, the temperature response is usually delayed and weakened. Using the delay time to correct the temperature amplitude in the thermal map data can eliminate the temperature distortion caused by thermal diffusion.

[0048] Specifically, the calibration process mainly considers the influence of thermal diffusion on temperature distribution. The original temperature value in the thermal map can be adjusted by the following formula (5): , the temperature value after calibration It can be expressed as: (5); in, It is an adjustment value calculated based on the response delay time and thermal diffusion coefficient and is used to calibrate the actual temperature response.

[0049] Step S204, arranging the calibrated temperature values ​​into a temperature distribution matrix according to the original position information of the thermal map data; Among them, the calibrated temperature values ​​are arranged in the coordinate order of the original thermal map data to form a temperature distribution matrix. The temperature distribution matrix is ​​an accurate description of the temperature distribution on the material surface, and displays the temperature at different locations on the sample surface in the form of a two-dimensional array to facilitate subsequent analysis.

[0050] Step S205: quantify the calibration accuracy of the temperature distribution matrix based on a statistical method, and verify the temperature distribution matrix according to the calibration accuracy.

[0051] Among them, statistical methods (such as mean square error, standard deviation, etc.) can be used to quantitatively analyze the temperature values ​​in the temperature distribution matrix to evaluate the accuracy of the calibrated temperature distribution matrix. By comparing the temperature distribution differences before and after calibration, it can be verified whether the calibration effect meets expectations and ensure the reliability of the data.

[0052] Specifically, the mean square error between the calibrated temperature value and the original measured temperature value can be calculated to quantify the accuracy of the calibration, and the standard deviation of the calibrated temperature distribution can be calculated to measure the uniformity of the temperature distribution.

[0053] In the above implementation, the thermal map data of the thermal insulation sponge sample is received, the response delay time is calculated using the thermal diffusion coefficient, the thermal map data is accurately calibrated, and finally a high-precision temperature distribution matrix is ​​generated and its calibration accuracy is verified. This technical solution effectively eliminates the temperature distortion caused by thermal diffusion, so that the obtained temperature distribution data is more consistent with the real thermal response of the material, thereby providing a reliable data basis for the subsequent thermal insulation performance analysis, ensuring that the evaluation of the thermal insulation performance of the thermal insulation sponge under complex environmental conditions is more accurate and scientific.

[0054] Reference Figure 3 As an implementation of step S105, based on the temperature distribution matrix and the thermal diffusion coefficient, the temperature data of different depth layers inside the thermal insulation sponge sample are calculated, and the steps of generating a temperature grid data set include: Step S301, determining the number of depth layers according to the sample thickness of the thermal insulation sponge sample and the preset layer spacing, and obtaining a depth layer list; In order to calculate the temperature of different depth layers inside the sample, the distribution of the depth layers needs to be calculated first. The sample thickness represents the total depth of the material. The interlayer spacing is preset to improve the accuracy of the temperature distribution. The interlayer spacing is the distance between each layer. The required number of layers can be calculated based on the sample thickness and the preset interlayer spacing, and these depth layers are arranged in order to form a depth layer list.

[0055] Specifically, the calculation formula for the number of depth layers is: number of layers = sample thickness / interlayer spacing; assuming that the thickness of the thermal insulation sponge sample is 10 mm and the interlayer spacing is 1 mm, the resulting depth layer list is [0,1,2,...,10] mm.

[0056] Step S302, based on the temperature distribution matrix and the thermal diffusion coefficient, the temperature of the first layer in the depth layer list is calculated according to the heat transfer equation; The first layer in the depth layer list is adjacent to the sample surface. In one embodiment of the present application, the temperature of the first layer (1 mm depth from the surface) is calculated based on the temperature distribution matrix (surface temperature data) and the thermal diffusion coefficient. The thermal diffusion coefficient (α) is used to describe the rate at which heat is conducted in the material. The temperature change can be calculated layer by layer through the heat transfer equation.

[0057] Specifically, the heat transfer equation can be expressed as: (6); Among them, T n is the temperature of the current depth layer, T n-1 is the temperature of the previous depth layer, represents the temperature difference, Δz is the preset interlayer spacing, and α is the thermal diffusion coefficient of the material.

[0058] Based on the above formula, the calculation formula for the temperature of the first layer is: (7); Among them, T 表面 is the surface temperature in the temperature distribution matrix, Represents the temperature change between the surface and the first layer.

[0059] Step S303, based on the temperature of the first layer in the depth layer list, the temperature of each layer in the depth layer list is calculated in turn according to the heat transfer formula to obtain temperature data of all depth layers; The temperature is calculated layer by layer from the first layer to the deepest layer, and the temperature calculation of each layer is based on the temperature and thermal diffusion parameters of the previous layer. The heat transfer formula is gradually applied to each layer to form a temperature distribution from the surface to the deep layer. This layer-by-layer calculation method ensures that the temperature of each depth layer can accurately reflect the heat conduction process of the material.

[0060] Step S304: Arrange the temperature data of all depth layers according to the depth layer list to construct a temperature grid data set.

[0061] The temperature data of all depth layers are arranged in depth order to construct a complete temperature grid dataset. The temperature grid dataset provides a structured data model for the temperature distribution from the surface to the deep layer inside the sample, reflects the temperature gradient and heat conduction characteristics of the sample, and provides data support for the multi-level analysis and evaluation of thermal insulation performance.

[0062] In the above implementation, the temperature data of different depth layers inside the sample are calculated layer by layer based on the temperature distribution matrix and the thermal diffusion coefficient, and finally a temperature grid dataset is generated. The temperature grid dataset shows the temperature gradient inside the thermal insulation sponge sample in a quantitative form, truly reflects the thermal insulation effect of the material, and provides a scientific basis for subsequent performance evaluation and optimization design. This dataset not only supports multi-level thermal insulation effect analysis, but can also be used as an experimental benchmark to provide a reference for the durability evaluation of thermal insulation materials in various application scenarios.

[0063] Reference Figure 4As an implementation of step S106, the steps of receiving the temperature change data of the thermal insulation sponge sample obtained under the high and low temperature cycle test and combining the initial temperature distribution in the temperature grid data set to obtain the high and low temperature insulation attenuation curve include: Step S401, receiving temperature change data of a thermal insulation sponge sample obtained under a high and low temperature periodic cycle test; Among them, in the high and low temperature cycle test, the insulation sponge sample will be affected by the changes in different temperature cycles, causing the temperature inside the sample to change with the high and low temperature fluctuations of the cycle. The temperature data during the test process can capture the response of the material at different temperature stages.

[0064] It should be noted that this step provides complete temperature change data for subsequent analysis, especially the temperature distribution of the material in different temperature cycle stages (such as high temperature stage and low temperature stage). These data reflect the material's reaction rate to temperature fluctuations and the trend of internal temperature distribution changes.

[0065] For example, assuming that in a high-low temperature cycle, the temperature of the sample surface is 40°C in the high temperature stage and 20°C in the low temperature stage, the temperature data of different depth layers inside also changes accordingly, and the temperature changes at each depth are recorded as [40°C, 35°C, 30°C] to [20°C, 18°C, 15°C].

[0066] Step S402, normalizing the temperature change data obtained from each temperature cycle test based on the initial temperature distribution in the temperature grid data set; Among them, in order to facilitate the analysis of the test data, the temperature change data obtained from each cycle test is compared with the temperature data in the initial temperature distribution. The temperature change data obtained from the test is standardized based on the temperature distribution in the initial temperature grid data set through normalization processing to eliminate the interference of different initial temperatures on the data and ensure that the temperature change data of different cycles can be consistently compared.

[0067] For example, assuming that the initial temperature grid data set is [30°C, 28°C, 26°C], and the temperature change data of a certain test in the high temperature cycle stage is [40°C, 36°C, 33°C], then the normalized temperature change data is [10°C, 8°C, 7°C], showing the temperature rise in the cycle.

[0068] Step S403, arranging the normalized temperature change data according to the depth layer list to obtain a temperature change matrix of different depth layers; Specifically, the normalized temperature change data is organized and arranged according to the different depth levels of the material (such as 1mm, 2mm, etc.) to form a temperature change matrix, which systematically represents the temperature response changes of the sample in each layer. Among them, the temperature change matrix of different depth layers intuitively shows the temperature distribution difference of the sample in the entire depth layer, providing hierarchical temperature data for the subsequent attenuation rate calculation.

[0069] Step S404, calculating the temperature decay rate of each depth layer based on the temperature change matrix; Among them, the temperature decay rate indicates the degree of weakening of the temperature change of the material after multiple high and low temperature cycles. According to the temperature change matrix obtained in each cycle, the attenuation rate of the temperature change in each depth layer is calculated to quantify the thermal insulation decay of the material in the high and low temperature cycle.

[0070] Specifically, the calculation formula is: ,By calculating the attenuation rate, we can systematically understand whether the thermal insulation performance of ,the material at different depth layers decays with the number of cycles, which is ,critical for evaluating the thermal insulation stability of the material in ,long-term use.

[0071] For example, assuming that the normalized temperature change data of the first cycle is [10°C, 8°C, 7°C], and the normalized temperature change data of the second cycle is [9.5°C, 7.6°C, 6.5°C], the temperature attenuation rates of each depth layer are 0.5°C, 0.4°C, and 0.5°C, respectively, indicating that the temperature change of the material gradually weakens.

[0072] Step S405, arranging the temperature decay rates of each depth layer in each temperature cycle test in chronological order to obtain a high and low temperature insulation decay curve.

[0073] Among them, the temperature decay rate of each cycle test is arranged in chronological order and plotted into a curve to generate a high and low temperature insulation decay curve. Among them, the horizontal axis of the curve represents time or the number of cycles, and the vertical axis represents the temperature decay rate of each depth layer, thus reflecting the thermal insulation performance decay trend of the sample with the increase of the number of cycles.

[0074] It is understandable that in long-term testing, a stable attenuation curve indicates that the thermal insulation performance of the material is stable, and a sharp decline in the curve indicates that the thermal insulation performance of the material decays rapidly over time. For example, if the high and low temperature thermal insulation attenuation curves of the thermal insulation sponge sample show a gentle downward trend, it indicates that the thermal insulation performance of the material has good durability.

[0075] In the above implementation, starting from the high and low temperature cycle test data, through normalization processing, depth level arrangement and attenuation rate calculation, the attenuation curve of the thermal insulation material is finally formed, and the thermal insulation performance of the material under different depth layers and multiple temperature cycle conditions is quantified. This technical solution can not only comprehensively reveal the long-term thermal insulation capacity of the thermal insulation material, but also provide clear data on the thermal insulation attenuation trend, which is convenient for evaluating the stability of the material under long-term extreme conditions, and provides reliable data support and scientific basis for the selection, optimization and application of thermal insulation materials.

[0076] Reference Figure 5 As an implementation method of step S107, the temperature change data and humidity change data obtained by the thermal insulation sponge sample under the humidity-temperature cycle test are received, and compared with the high and low temperature thermal insulation attenuation curves, and the step of obtaining the humidity-temperature thermal insulation attenuation data set according to the comparison results includes: Step S501, receiving temperature change data and humidity change data of the thermal insulation sponge sample obtained under a humidity-temperature periodic cycle test; In the humidity-temperature cycle test, the thermal insulation sponge sample is exposed to an environment where humidity and temperature change alternately, and the temperature of each layer inside will change accordingly, while recording the effect of humidity on the thermal insulation performance of the material. This step provides the raw data of the temperature response inside the material under humidity-temperature cycle conditions, as well as the change in temperature distribution inside the sample in an environment with changing humidity, laying a data foundation for the subsequent analysis of the effect of humidity on thermal insulation performance.

[0077] Exemplarily, in the humidity-temperature cycle test, the surface temperature of the sample is 40°C and the humidity is 80% in the high temperature and high humidity stage, and the surface temperature of the sample is 20°C and the humidity is 40% in the low temperature and low humidity stage. The temperature changes at different depth layers are recorded as [40°C, 36°C, 33°C] to [20°C, 18°C, 15°C].

[0078] Step S502, normalizing the temperature change data and the humidity change data; Among them, in order to unify the comparison data, the temperature and humidity change data need to be normalized based on the initial state (initial humidity and initial temperature); normalization can remove the impact of the initial temperature and humidity differences on the data, making the data in different cycle stages comparable.

[0079] For example, assuming that the initial temperature of the insulation sponge sample under the humidity-temperature cycle test is 30°C and the humidity is 50%. When the temperature rises to 40°C and the humidity rises to 80%, the normalized temperature change is 10°C and the humidity change is 30%.

[0080] Step S503, arranging the normalized temperature change data and humidity change data according to the depth layer list to obtain a humidity-temperature change matrix of different depth layers; Among them, the normalized temperature change data and humidity change data are arranged according to the depth level of the sample to generate a humidity-temperature change matrix to show the temperature response of different depth layers in a humidity change environment, providing data support for calculating the temperature attenuation rate layer by layer.

[0081] Step S504, based on the humidity-temperature change matrix, calculate the temperature attenuation rate of each depth layer and generate a corresponding temperature attenuation rate matrix; Specifically, the calculation formula is: , by calculating the temperature data in the humidity-temperature thermal insulation performance change matrix layer by layer, the temperature attenuation rate matrix under the humidity-temperature cycle test is obtained. Among them, the temperature attenuation rate matrix reveals the thermal insulation performance attenuation of the material at different depths under the humidity-temperature cycle conditions, providing a basis for the subsequent comparison with the high and low temperature attenuation rate.

[0082] Step S505, comparing the temperature decay rate in the temperature decay rate matrix with the temperature decay rate in the high and low temperature insulation decay curve layer by layer to obtain a comparison result; Among them, the humidity-temperature attenuation rate is compared with the temperature attenuation rate of the high and low temperature attenuation curve layer by layer to determine the specific impact of humidity on thermal insulation attenuation. By comparing layer by layer, the analysis can be refined to determine the enhancement or weakening effect of humidity on the thermal insulation performance of different depth layers. For example, if the high and low temperature attenuation rate matrix shows that the attenuation rate of each depth layer is [2%, 1.5%, 1%], and the attenuation rate of each layer of the humidity-temperature attenuation rate matrix is ​​[3%, 2.5%, 2%], then the thermal insulation attenuation rate of each layer under the humidity environment increases by 1%, 0.5% and 1% respectively, indicating that the humidity condition exacerbates the thermal insulation attenuation.

[0083] Step S506: establishing a humidity-temperature insulation attenuation data set based on the comparison result.

[0084] Among them, the layer-by-layer comparison results are summarized to generate a humidity-temperature thermal insulation attenuation dataset. The dataset quantifies the impact of humidity changes on the thermal insulation performance of the material, providing reliable data support for the performance analysis of the material under humid conditions.

[0085] For example, assuming that the comparison results show that the temperature decay rate of the sample increases by 5% when the humidity is 80%, the dataset records the weakening of the thermal insulation performance of the sample caused by the increase in humidity, providing quantitative analysis support for the application of materials in humidity-sensitive environments.

[0086] In the above implementation, a quantitative evaluation of the temperature attenuation characteristics of the thermal insulation sponge samples under a humidity-temperature cycle environment is achieved. The humidity-temperature thermal insulation attenuation data set finally generated can effectively reveal the degree of weakening of the thermal insulation performance caused by humidity changes, especially in humid environments. The thermal insulation durability and stability of the material are scientifically and quantitatively evaluated, providing accurate decision-making support for the applicability of the material in specific environments.

[0087] Reference Figure 6 As an implementation method of step S108, the step of performing thermal insulation attenuation trend analysis based on the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set to obtain the thermal insulation performance data of the thermal insulation sponge sample includes: Step S601, normalizing the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set; Among them, since the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set respectively represent the attenuation of thermal insulation performance under different environmental conditions, the data needs to be normalized to unify the benchmark value to ensure that the two data can be effectively compared on the same scale.

[0088] For example, assuming that the temperature attenuation rate in the high and low temperature attenuation curve is [0.5%, 0.4%, 0.3%], and the temperature attenuation rate in the humidity-temperature attenuation data set is [0.7%, 0.6%, 0.5%], the two sets of data are adjusted to the same benchmark through normalization processing, for example, normalized to the form of relative attenuation rate.

[0089] Step S602, comparing the normalized high and low temperature thermal insulation attenuation curves with the humidity-temperature thermal insulation attenuation data set layer by layer to obtain a difference matrix; Among them, the difference matrix shows the additional attenuation under humidity change conditions compared to the single temperature change conditions, reflecting the additional impact of humidity on thermal insulation performance. It can quantify the impact of humidity on thermal insulation performance at different temperatures and provide quantitative difference data for subsequent curve fitting.

[0090] For example, assuming that the high and low temperature attenuation rate matrix is ​​[0.5%, 0.4%, 0.3%], and the humidity-temperature attenuation rate matrix is ​​[0.7%, 0.6%, 0.5%], the difference matrix is ​​[0.2%, 0.2%, 0.2%], indicating that the humidity condition adds an additional 0.2% attenuation.

[0091] Step S603, based on the difference matrix, calculating the thermal insulation decay trend curve of the thermal insulation sponge sample by a trend fitting algorithm, and fitting to obtain a thermal insulation decay trend model; Specifically, the data in the difference matrix is ​​used for trend fitting analysis (such as linear regression, exponential decay model, etc.) to obtain the thermal insulation decay trend curve. The selection of the fitting algorithm needs to consider the data characteristics and decay behavior, such as whether it is linear decay or exponential decay. The fitted thermal insulation decay trend model can describe the decay trend of the sample under high and low temperature and humidity conditions through mathematical formulas, and provide a visual curve of the thermal insulation decay behavior under different environments.

[0092] As one of the embodiments of the present application, in the fitting analysis process, if the decay trend of the difference matrix data conforms to the exponential model, the fitted decay model curve is in the form of: (8); Among them, parameters a and b represent the initial decay rate and the coefficient of change of the decay rate, respectively.

[0093] Step S604, based on the thermal insulation attenuation trend model, calculating the comprehensive thermal insulation attenuation rate under high and low temperature conditions and humidity-temperature conditions; Among them, the thermal insulation attenuation trend model is used to calculate the comprehensive thermal insulation attenuation rate under long-term high and low temperature cycles and humidity-temperature conditions. The comprehensive thermal insulation attenuation rate reflects the average thermal insulation performance attenuation rate of the sample under multiple environmental conditions and is a key indicator of the long-term stability of the material.

[0094] For example, if the annual attenuation rate under high and low temperature conditions is 0.5% and the annual attenuation rate under humidity-temperature conditions is 0.8%, the comprehensive attenuation rate is 0.65% / year.

[0095] Step S605, obtaining thermal insulation performance data of the thermal insulation sponge sample according to the thermal insulation decay trend model and the comprehensive thermal insulation decay rate.

[0096] Among them, the thermal insulation performance data of the samples are summarized and generated according to the obtained thermal insulation attenuation trend model and comprehensive thermal insulation attenuation rate. The thermal insulation performance data includes the thermal insulation attenuation rate under different environmental factors, which fully reflects the long-term thermal insulation characteristics of the material for evaluating the thermal insulation application performance of the material.

[0097] In the above implementation, a comprehensive analysis of the thermal insulation attenuation trend under multiple environmental factors is achieved. After normalization, high and low temperature and humidity difference calculation, attenuation trend fitting and layer-by-layer calculation of the comprehensive attenuation rate, the thermal insulation stability of the thermal insulation material in long-term use is ensured to be quantitatively evaluated. The thermal insulation performance data finally generated can provide a scientific basis for the long-term applicability of thermal insulation materials and support the optimal selection and design of materials under complex environmental conditions such as high temperature and humidity.

[0098] Reference Figure 7As an implementation of step S107, after the step of obtaining the humidity-temperature insulation attenuation data set according to the comparison result, the method further includes: Step S701, inputting the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set into a pre-built finite element model to obtain a finite element microstructure model; The finite element model is a numerical simulation technology that can decompose the heat transfer and attenuation process of the material into gridded calculation units through discretization. The temperature change and heat conduction rate of each grid unit can be determined by the input data, so as to construct the three-dimensional heat conduction characteristics of the sponge. In the embodiments of the present application, the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set can reflect the thermal insulation performance of the thermal insulation sponge sample under different environmental conditions. By inputting these data into the finite element model, simulating the heat conduction path and attenuation rate of the internal pore distribution of the sample, a microstructure model is constructed, so that the internal structure and pore distribution of the thermal insulation sponge and other characteristics can be further analyzed based on the actual attenuation data.

[0099] It can be understood that the finite element microstructure model can further simulate the internal heat transfer path and conduction characteristics of the sponge in the simulation model, provide a visualization and data-based model of the heat conduction of the sample in the actual environment, and provide a real basis for subsequent parameter optimization.

[0100] Step S702, adjusting the internal parameters of the finite element microstructure model based on the temperature grid data set to obtain a microstructure simulation model of the thermal insulation sponge sample; Among them, the temperature grid data set contains the temperature distribution at different depths of the sample. By comparing the temperature information in the temperature grid data set and adjusting the internal parameters of the finite element microstructure model (such as thermal conductivity and thermal diffusion coefficient, etc.), the simulation results of the model can be made closer to the actual temperature distribution. This adjustment can refine the simulation model to make it more consistent with the actual thermal insulation performance of the sample.

[0101] In the embodiment of the present application, the temperature parameters can be set in different depth layers of the model, the performance of the heat conduction path in the simulation model can be observed, and it can be compared with the temperature grid data set layer by layer, and fine-tuned to achieve consistency. For example, the surface grid temperature is set to 32°C, and the next layer depth is 31°C, and the temperature distribution of the model is adjusted in sequence to ensure consistency with the actual temperature grid, thereby generating a microstructure simulation model that is more in line with the actual sample heat conduction characteristics, laying an accurate model foundation for subsequent parameter optimization and performance analysis.

[0102] Specifically, the model structure of the above-mentioned microstructure simulation model specifically includes: 1. Grid division layer: The three-dimensional structure of the thermal insulation sponge sample is discretized into small grid units. Each grid unit represents a microscopic volume of the material, which is composed of the pore structure and solid matrix of the material. Through grid division, the heat conduction process of the material is discretized, which facilitates the calculation of the heat transfer and temperature change of each small unit, and provides detailed heat conduction analysis.

[0103] 2. Pores and matrix distribution layer: In each grid unit, the ratio and spatial distribution of pores to solid matrix are further subdivided, and different combinations of pore density and pore size are set. The distribution of pores directly affects the heat conduction path, and the matrix is ​​responsible for heat conduction. Among them, pore density and pore size control the thermal insulation performance of the material. The higher the density and the smaller the pore size, the better the thermal insulation effect is. The simulation model can observe the impact of different microstructures on heat conduction by adjusting these parameters.

[0104] 3. Heat conduction path layer: The thermal conductivity and thermal diffusion coefficient of each grid unit are determined by the finite element calculation module. The heat conduction path layer is responsible for calculating the heat transfer path between the pores and the matrix, specifically considering the distribution of pores and the thermal conductivity of the matrix. This layer defines how heat is transferred inside the sample, which can effectively simulate the layer-by-layer diffusion of heat flow in the sponge structure and help predict the thermal insulation effect of the material.

[0105] 4. Temperature distribution layer: This layer records the temperature distribution of different depth layers and calibrates the temperature field distribution based on the input temperature grid data set to keep the temperature distribution of the simulation model consistent with the actual temperature change. The temperature distribution layer improves the accuracy of the model by adjusting the difference between the simulation results and the actual temperature data to ensure that the simulation results match the actual situation.

[0106] 5. Heat flow field and boundary condition layer: used to set heat flow boundary conditions, including heat source location, external environment temperature and sample surface heat dissipation conditions. The heat flow field describes the transfer process of heat energy from high temperature source to low temperature area. The boundary condition layer can truly simulate the heating conditions of thermal insulation materials in the actual environment. By simulating the changes in heat flow in different environments, the thermal insulation performance of materials under high temperature or humidity conditions can be predicted.

[0107] 6. Parameter adjustment and performance optimization layer: This layer allows the adjustment of parameters such as pore density and pore size in the simulation model, observes the changes in the thermal insulation performance of the material under different microstructure combinations, and calculates thermal conductivity, thermal diffusivity and other thermal insulation performance indicators under each parameter combination. By adjusting the ratio of pores and matrix, the optimal structural combination is identified to help optimize the material design to achieve the best thermal insulation performance.

[0108] Based on this, the microstructure simulation model of the embodiment of the present application comprehensively simulates the thermal conductivity characteristics of the thermal insulation sponge sample through grid division, pore and matrix distribution, heat conduction path, temperature distribution, thermal flow field and boundary conditions, and parameter adjustment hierarchical structure construction. This model can not only accurately display the thermal conduction path of the material in a complex environment, but also adjust the structural parameters to achieve performance optimization, thereby providing reliable data support for improving the thermal insulation performance of the material.

[0109] Step S703, adjusting the pore density and pore size parameters in the microstructure simulation model, calculating the thermal insulation performance data of the model according to each set of parameter combinations, and generating a thermal insulation performance optimization data set; Among them, pore density and pore size directly affect the thermal insulation effect of thermal insulation sponge. By adjusting these parameters in the simulation model, the thermal insulation performance of the material under different pore structure combinations can be observed and its thermal insulation performance data can be calculated. The performance data of these different parameter combinations are integrated to form a thermal insulation performance optimization data set to facilitate subsequent analysis of the optimal combination.

[0110] For example, in the microstructure simulation model, the pore density (such as 10%, 20%, 30%) and pore size (such as 100 microns, 200 microns, 300 microns) are gradually adjusted to simulate the thermal insulation performance of each parameter combination, and the thermal insulation data such as thermal conductivity and thermal diffusion coefficient of each combination are recorded to form a thermal insulation performance optimization data set, which helps to comprehensively analyze the effects of different pore densities and pore sizes on the thermal insulation performance and provide data support for selecting the optimal parameter combination.

[0111] Step S704, based on the thermal insulation performance optimization data set, select the optimal parameter combination of the thermal insulation performance data, and generate structural optimization suggestion information for the thermal insulation sponge sample.

[0112] Among them, by analyzing different parameter combinations in the thermal insulation performance optimization data set, the parameter combination with the best thermal insulation performance is selected, and the corresponding structural optimization recommendation information is generated, thereby providing quantifiable improvement suggestions for material design and manufacturing, ensuring that the thermal insulation sponge sample has the best performance in application.

[0113] Exemplarily, a combination of pore density and pore size with the highest thermal resistance and lowest thermal conductivity is screened out from the thermal insulation performance optimization data set, such as a combination of a pore density of 25% and a pore size of 150 microns. Optimization suggestion information is generated based on the optimal parameter combination, and it is recommended to set the structural parameters of the material to the above-mentioned optimal combination to achieve the best thermal insulation performance, so that the material has a more stable and lasting thermal insulation effect in the actual use environment.

[0114] In the above implementation, the thermal insulation performance of thermal insulation sponge materials under complex environmental conditions is evaluated. Through multi-step processing and simulation, not only can the attenuation trend of thermal insulation materials be quantified, but also the relationship between the internal structure of the material and the thermal insulation performance can be deeply analyzed using the microstructure simulation model, and finally scientific suggestions are provided for the optimization of the material structure. This technical solution significantly improves the accuracy of the thermal insulation durability evaluation of thermal insulation sponge materials, and provides comprehensive data support and optimization guidance for the design, selection and application of thermal insulation materials.

[0115] This application provides a quantitative performance analysis method for thermal insulation materials in complex environments, which can not only support the optimization design of materials, but also be applied in the fields of construction and industry to ensure the stable thermal insulation performance of materials in different environments. Through a scientific evaluation system and data guidance, it provides a reliable basis for material selection and research and development, and promotes the efficient application and long-term performance improvement of thermal insulation materials. The embodiment of the present application also discloses a thermal insulation performance evaluation system for thermal insulation sponge.

[0116] A thermal insulation performance evaluation system for thermal insulation sponge, comprising: A parameter acquisition module, used to acquire initial parameters of the thermal insulation sponge sample to be tested; wherein the initial parameters include sample thickness, sample surface area, pore density and pore size of the thermal insulation sponge sample; The first data processing module is used to receive and store the heat transfer data of the thermal insulation sponge sample under different temperature difference conditions to obtain the temperature-heat transfer curve of the thermal insulation sponge sample; The second data processing module is used to generate a temperature-thermal conductivity curve of the thermal insulation sponge sample based on the temperature-heat transfer curve and the sample thickness through a steady-state heat flow formula, and calculate the thermal diffusion coefficient data; A temperature distribution matrix generation module is used to receive the thermal map data of the surface temperature distribution of the thermal insulation sponge sample, calibrate the temperature response based on the thermal diffusion coefficient, and generate a temperature distribution matrix; A temperature grid data set generation module is used to calculate the temperature data of different depth layers inside the thermal insulation sponge sample based on the temperature distribution matrix and thermal diffusion coefficient, and generate a temperature grid data set; The third data processing module is used to receive the temperature change data of the thermal insulation sponge sample obtained under the high and low temperature cycle test, and obtain the high and low temperature insulation attenuation curve in combination with the initial temperature distribution in the temperature grid data set; The fourth data processing module is used to receive the temperature change data and humidity change data obtained by the thermal insulation sponge sample under the humidity-temperature cycle test, and compare them with the high and low temperature thermal insulation attenuation curves, and obtain the humidity-temperature thermal insulation attenuation data set according to the comparison results; The thermal insulation performance data generation module is used to perform thermal insulation attenuation trend analysis based on the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set to obtain the thermal insulation performance data of the thermal insulation sponge sample.

[0117] In the above implementation, accurate thermal insulation performance evaluation of thermal insulation materials under multiple environmental conditions is achieved through multi-level data collection and processing. By acquiring attenuation data in different environments, analyzing temperature distribution, and modeling attenuation trends, the system can generate comprehensive thermal insulation performance data, providing a high-precision evaluation method for the durability and stability of thermal insulation materials.

[0118] A thermal insulation performance evaluation system for a thermal insulation sponge in an embodiment of the present application can implement any of the above-mentioned methods for evaluating the thermal insulation performance of a thermal insulation sponge, and the specific working process of each module in the thermal insulation performance evaluation system for a thermal insulation sponge can refer to the corresponding process in the above-mentioned method embodiment.

[0119] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are only illustrative; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0120] The embodiment of the present application also discloses a computer device.

[0121] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for evaluating the thermal insulation performance of a thermal insulation sponge as described above is implemented.

[0122] The embodiment of the present application also discloses a computer-readable storage medium.

[0123] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the above-mentioned methods for evaluating the thermal insulation performance of a thermal insulation sponge.

[0124] Among them, computer-readable storage media can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus or device; the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0125] It should be noted that in the above embodiments, the description of each embodiment has different emphases, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A method for evaluating the thermal insulation performance of a thermal insulation sponge, characterized in that: The performance evaluation method includes: Obtaining initial parameters of the thermal insulation sponge sample to be tested; wherein the initial parameters include sample thickness, sample surface area, pore density and pore size of the thermal insulation sponge sample; Receive and store the heat transfer data of the thermal insulation sponge sample under different temperature difference conditions to obtain a temperature-heat transfer curve of the thermal insulation sponge sample; Based on the temperature-heat transfer curve and the sample thickness, the temperature-thermal conductivity curve of the thermal insulation sponge sample is generated by processing with a steady-state heat flow formula, and the thermal diffusion coefficient data is calculated; Receiving thermal map data of the surface temperature distribution of the thermal insulation sponge sample, and calibrating the temperature response based on the thermal diffusion coefficient to generate a temperature distribution matrix; Based on the temperature distribution matrix and the thermal diffusion coefficient, temperature data of different depth layers inside the thermal insulation sponge sample are calculated to generate a temperature grid data set; Receiving temperature change data of the thermal insulation sponge sample obtained under the high and low temperature cycle test, and combining the initial temperature distribution in the temperature grid data set to obtain a high and low temperature thermal insulation attenuation curve; Receiving temperature change data and humidity change data obtained by the thermal insulation sponge sample under the humidity-temperature cycle test, and comparing them with the high and low temperature thermal insulation attenuation curves, and obtaining a humidity-temperature thermal insulation attenuation data set according to the comparison results; The thermal insulation attenuation trend analysis is performed based on the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set to obtain the thermal insulation performance data of the thermal insulation sponge sample.

2. The method for evaluating the thermal insulation performance of a thermal insulation sponge according to claim 1, characterized in that: The steps of receiving the thermal map data of the surface temperature distribution of the thermal insulation sponge sample and calibrating the temperature response based on the thermal diffusion coefficient to generate a temperature distribution matrix include: Receiving thermal map data of surface temperature distribution of the thermal insulation sponge sample; Calculating the temperature response delay time of the surface of the thermal insulation sponge sample based on the thermal diffusion coefficient; Adjusting the temperature amplitude in the thermal map data based on the temperature response delay time to calibrate each temperature value in the thermal map data; Arranging the calibrated temperature values ​​into a temperature distribution matrix according to the original position information of the thermal map data; The calibration accuracy of the temperature distribution matrix is ​​quantified based on a statistical method, and the temperature distribution matrix is ​​verified according to the calibration accuracy.

3. The method for evaluating the thermal insulation performance of a thermal insulation sponge according to claim 1, characterized in that: The steps of calculating the temperature data of different depth layers inside the thermal insulation sponge sample based on the temperature distribution matrix and the thermal diffusion coefficient and generating a temperature grid data set include: Determine the number of depth layers according to the sample thickness of the thermal insulation sponge sample and the preset layer spacing, and obtain a depth layer list; Based on the temperature distribution matrix and the thermal diffusion coefficient, the temperature of the first layer in the depth layer list is calculated according to the heat transfer equation; Based on the temperature of the first layer in the depth layer list, the temperature of each layer in the depth layer list is calculated in turn according to the heat transfer formula to obtain temperature data of all depth layers; The temperature data of all depth layers are arranged according to the depth layer list to construct a temperature grid data set.

4. The method for evaluating the thermal insulation performance of a thermal insulation sponge according to claim 3, characterized in that: The steps of receiving the temperature change data of the thermal insulation sponge sample obtained under the high and low temperature cycle test and obtaining the high and low temperature insulation attenuation curve in combination with the initial temperature distribution in the temperature grid data set include: Receiving temperature change data of the thermal insulation sponge sample obtained under high and low temperature periodic cycle test; Based on the initial temperature distribution in the temperature grid data set, normalizing the temperature change data obtained from each temperature cycle test; Arranging the normalized temperature change data according to the depth layer list to obtain a temperature change matrix of different depth layers; Based on the temperature change matrix, calculate the temperature decay rate of each depth layer; The temperature decay rates of each depth layer in each temperature cycle test are arranged in chronological order to obtain the high and low temperature insulation decay curves.

5. The method for evaluating the thermal insulation performance of a thermal insulation sponge according to claim 4, characterized in that: The steps of receiving the temperature change data and humidity change data obtained by the thermal insulation sponge sample under the humidity-temperature cycle test, and comparing them with the high and low temperature thermal insulation attenuation curve, and obtaining the humidity-temperature thermal insulation attenuation data set according to the comparison result include: Receiving temperature change data and humidity change data obtained by the thermal insulation sponge sample under a humidity-temperature periodic cycle test; Normalizing the temperature change data and the humidity change data; Arrange the normalized temperature change data and humidity change data according to the depth layer list to obtain humidity-temperature change matrices of different depth layers; Based on the humidity-temperature change matrix, the temperature attenuation rate of each depth layer is calculated to generate a corresponding temperature attenuation rate matrix; Comparing the temperature attenuation rate in the temperature attenuation rate matrix with the temperature attenuation rate in the high and low temperature insulation attenuation curve layer by layer to obtain a comparison result; Based on the comparison results, a humidity-temperature insulation attenuation data set is established.

6. The method for evaluating the thermal insulation performance of a thermal insulation sponge according to claim 5, characterized in that: The step of performing thermal insulation attenuation trend analysis according to the high and low temperature thermal insulation attenuation curve and the humidity-temperature thermal insulation attenuation data set to obtain the thermal insulation performance data of the thermal insulation sponge sample comprises: Normalizing the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set; Compare the normalized high and low temperature thermal insulation attenuation curves with the humidity-temperature thermal insulation attenuation data set layer by layer to obtain a difference matrix; Based on the difference matrix, the thermal insulation decay trend curve of the thermal insulation sponge sample is calculated by a trend fitting algorithm, and a thermal insulation decay trend model is obtained by fitting; Based on the thermal insulation attenuation trend model, the comprehensive thermal insulation attenuation rate under high and low temperature conditions and humidity-temperature conditions is calculated; The thermal insulation performance data of the thermal insulation sponge sample is obtained according to the thermal insulation decay trend model and the comprehensive thermal insulation decay rate.

7. A method for evaluating the thermal insulation performance of a thermal insulation sponge according to any one of claims 1 to 6, characterized in that: After the step of obtaining the humidity-temperature insulation attenuation data set according to the comparison result, the following steps are also included: Inputting the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set into a pre-constructed finite element model to obtain a finite element microstructure model; Adjusting the internal parameters of the finite element microstructure model based on the temperature grid data set to obtain a microstructure simulation model of the thermal insulation sponge sample; Adjusting the pore density and pore size parameters in the microstructure simulation model, calculating the thermal insulation performance data of the model according to each set of parameter combinations, and generating a thermal insulation performance optimization data set; Based on the thermal insulation performance optimization data set, the optimal parameter combination of the thermal insulation performance data is selected to generate structural optimization recommendation information for the thermal insulation sponge sample.

8. A thermal insulation performance evaluation system for thermal insulation sponge, characterized in that: The performance evaluation system comprises: A parameter acquisition module, used to acquire initial parameters of the thermal insulation sponge sample to be tested; wherein the initial parameters include sample thickness, sample surface area, pore density and pore size of the thermal insulation sponge sample; A first data processing module is used to receive and store the heat transfer data of the thermal insulation sponge sample under different temperature difference conditions to obtain a temperature-heat transfer curve of the thermal insulation sponge sample; A second data processing module is used to generate a temperature-thermal conductivity curve of the thermal insulation sponge sample based on the temperature-heat transfer curve and the sample thickness through a steady-state heat flow formula, and calculate the thermal diffusion coefficient data; A temperature distribution matrix generation module is used to receive the thermal map data of the surface temperature distribution of the thermal insulation sponge sample, and generate a temperature distribution matrix based on the thermal diffusion coefficient calibrated temperature response; A temperature grid data set generation module, used to calculate the temperature data of different depth layers inside the thermal insulation sponge sample based on the temperature distribution matrix and the thermal diffusion coefficient, and generate a temperature grid data set; The third data processing module is used to receive the temperature change data obtained by the thermal insulation sponge sample under the high and low temperature cycle test, and obtain the high and low temperature insulation attenuation curve in combination with the initial temperature distribution in the temperature grid data set; A fourth data processing module is used to receive the temperature change data and humidity change data obtained by the thermal insulation sponge sample under the humidity-temperature cycle test, and compare them with the high and low temperature thermal insulation attenuation curves, and obtain a humidity-temperature thermal insulation attenuation data set according to the comparison results; The thermal insulation performance data generation module is used to perform thermal insulation attenuation trend analysis based on the high and low temperature thermal insulation attenuation curves and the humidity-temperature thermal insulation attenuation data set to obtain the thermal insulation performance data of the thermal insulation sponge sample.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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