Thermal resistance evaluation method, device and medium for thermal insulation material based on digital twinning

Abstract

The application discloses a kind of based on digital twinning heat insulation material thermal resistance evaluation method, equipment and medium, it is related to thermal resistance evaluation technical field, including, according to boundary heat potential response sequence and thickness slice attribute table calculation heat capacity lag deduction quantity, calculate lateral leakage proportion, based on steady-state apparent thermal resistance, heat capacity lag deduction quantity and lateral leakage proportion calculate target heat insulation material effective thermal resistance, according to thermal resistance pass contract map target heat insulation material effective thermal resistance is distributed, generates edge thermal resistance set and target thermal resistance lock chain, record target heat insulation material effective thermal resistance, lateral leakage proportion, edge thermal resistance set and target thermal resistance lock chain are associated, generate heat insulation material thermal resistance evaluation result;The application constructs boundary heat potential response sequence, and improves the accuracy and micro analytical ability of heat insulation material thermal resistance evaluation.

Description

Technical Field

[0001] This invention relates to the field of thermal resistance assessment technology, and in particular to a method, device and medium for assessing the thermal resistance of thermal insulation materials based on digital twins. Background Technology

[0002] With the deep penetration of digital twin technology into the fields of industrial internet and intelligent manufacturing, its application potential in materials science and thermophysical performance characterization is becoming increasingly prominent. As a core component of key equipment such as aerospace, new energy battery packs, and high-temperature industrial furnaces, the accurate assessment of thermal resistance performance of thermal insulation materials directly determines the thermal safety margin and energy efficiency level. In recent years, with the iterative upgrades of sensor technology and data acquisition, modern thermal analysis instruments have good temperature measurement accuracy and heat flow control stability, which can meet the basic performance testing requirements of most engineering materials under standard atmospheric pressure and normal temperature environments. In addition, numerical simulation methods based on finite element analysis have been widely used in the early stage of thermal design. By establishing simplified geometric models, the heat conduction process of materials can be visualized and simulated, providing important theoretical references for understanding the macroscopic thermal field distribution, which constitutes the technical cornerstone of current thermal insulation performance evaluation.

[0003] Nevertheless, existing thermal resistance assessment methods still have room for improvement. First, they lack the ability to analyze the distribution of thermal resistance at each layer along the thickness direction within the material, making it impossible to identify specific "weak thermal resistance layers" or "heat leakage channels" in multilayer structures. Second, when dealing with unsteady-state heat conduction processes, they often ignore the influence of the material's thermal capacity hysteresis effect on the thermal response time, and it is difficult to quantify the proportion of lateral heat loss to longitudinal heat flow, resulting in a large deviation in the steady-state apparent thermal resistance calculated under non-ideal adiabatic boundary conditions. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for evaluating the thermal resistance of insulation materials based on digital twins to solve the problem of large deviations in the calculated steady-state apparent thermal resistance.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for evaluating the thermal resistance of insulation materials based on digital twins, comprising, Collect the sample geometry data, material layer data, sample boundary data and edge protection layer data of the target thermal insulation material, construct a structural twin of the thermal insulation material, cut the target thermal insulation material along the thickness direction, and obtain the thickness slice attribute table. Based on the structural twin and thickness slice attribute table of the thermal insulation material, after constructing the thermal resistance pass contract diagram, a double boundary thermal potential loading is performed on the target thermal insulation material to obtain the boundary thermal potential response sequence. The steady-state evaluation interval is selected based on the boundary thermal potential response sequence, and the steady-state apparent thermal resistance is calculated. The thermal capacity hysteresis deduction and lateral leakage ratio are calculated based on the boundary thermal potential response sequence and thickness slice attribute table. The effective thermal resistance of the target insulation material is calculated based on the steady-state apparent thermal resistance, the thermal capacity hysteresis deduction, and the lateral leakage ratio. The effective thermal resistance of the target insulation material is allocated according to the thermal resistance pass contract diagram to generate the edge thermal resistance set and the target thermal resistance closed chain. The effective thermal resistance, lateral leakage ratio, edge thermal resistance set, and target thermal resistance lock chain of the target thermal insulation material are linked to generate thermal resistance assessment results for the thermal insulation material.

[0007] As a preferred embodiment of the digital twin-based thermal resistance assessment method for thermal insulation materials described in this invention, the construction of the structural twin of the thermal insulation material specifically involves: The sample geometry data includes sample length, sample width, sample thickness, hot surface boundary area, and lateral area. The material layer data includes material layer number, material layer thickness, material layer composition type, porosity, reinforcing fiber ratio, and interfacial bonding ratio. The sample boundary data includes the hot surface boundary position, the cold surface boundary position, and the lateral boundary position; The edge protection layer data includes the lateral edge protection layer thickness, the lateral edge protection layer thermal conductivity, and the lateral boundary area; According to the thickness direction of the target thermal insulation material, the corresponding sample thickness, material layer thickness, hot surface boundary position and cold surface boundary position are spatially aligned to establish the thickness direction coordinate relationship between the hot surface boundary and the cold surface boundary. By associating and encapsulating the sample geometry data, material layer data, sample boundary data, and thickness direction coordinate relationship, a structural twin of the thermal insulation material is constructed.

[0008] As a preferred embodiment of the digital twin-based thermal resistance evaluation method for thermal insulation materials described in this invention, the step of obtaining the thickness slice attribute table specifically involves: Read the sample thickness, hot surface boundary position, cold surface boundary position and material layer thickness in the thermal insulation material structural twin, and set the cutting positions sequentially from the hot surface boundary position to the cold surface boundary position along the thickness direction of the target thermal insulation material; When the cutting position falls within the same material layer, the material region between two adjacent cutting positions is taken as a thickness slice. When the cutting position crosses the boundary of adjacent material layers, the boundary of the material layers is taken as the boundary of the thickness slice. Collect the slice number, slice start position, slice end position, slice thickness, slice projected area, material layer number, material layer composition type, porosity, reinforcing fiber ratio, and interface bonding ratio for each thickness slice, and arrange them in the thickness direction to obtain the thickness slice attribute table.

[0009] As a preferred embodiment of the digital twin-based thermal resistance assessment method for thermal insulation materials described in this invention, the step of obtaining the boundary thermal potential response sequence specifically includes: Based on the thickness slice attribute table, each thickness slice is used as a thermal resistance node, and the contact interface between two adjacent thickness slices is used as a thermal resistance connection edge. The thermal resistance nodes and thermal resistance connection edges are connected in the thickness direction from the hot surface boundary position to the cold surface boundary position to construct a thermal resistance access contract graph. Based on the hot and cold boundary positions in the thermal resistance contract diagram, the target insulation material is placed between the constant power hot surface loading plate and the constant temperature cold surface loading plate, and dual-boundary thermal potential loading is applied to the target insulation material. During the dual-boundary thermal potential loading process, the hot surface boundary temperature sequence, cold surface boundary temperature sequence, lateral boundary temperature sequence and input power sequence are collected and aligned according to the preset sampling period to obtain the boundary thermal potential response sequence.

[0010] As a preferred embodiment of the digital twin-based thermal resistance evaluation method for thermal insulation materials described in this invention, the calculation of steady-state apparent thermal resistance specifically involves: Read the hot surface boundary temperature sequence, cold surface boundary temperature sequence, and input power sequence from the boundary thermal potential response sequence, and calculate the hot surface boundary temperature change rate and cold surface boundary temperature change rate according to the sampling time. The continuous sampling period during which the hot surface boundary temperature change rate is less than the preset hot surface stability threshold, the cold surface boundary temperature change rate is less than the preset cold surface stability threshold, and the input power sequence remains constant is defined as the steady-state evaluation interval. Within the steady-state evaluation interval, the average values ​​of the hot surface boundary temperature sequence and the cold surface boundary temperature sequence are calculated respectively to obtain the average temperature of the hot surface boundary and the average temperature of the cold surface boundary. The surface heat flux density is calculated based on the average input power and the hot surface boundary area within the steady-state evaluation interval of the input power sequence. The steady-state apparent thermal resistance is obtained by calculating the difference between the average temperature of the hot surface boundary and the average temperature of the cold surface boundary, and then quotienting it with the surface heat flux density.

[0011] As a preferred embodiment of the thermal resistance evaluation method for insulation materials based on digital twins described in this invention, the calculation of the lateral leakage ratio specifically involves: Read the hot surface boundary temperature sequence, cold surface boundary temperature sequence, lateral boundary temperature sequence and input power sequence from the boundary thermal potential response sequence, and read the thickness, slice projected area, material layer composition type, porosity, reinforcing fiber ratio and interface bonding ratio of each thickness slice in the thickness slice attribute table; The equivalent density, equivalent specific heat capacity, and equivalent thermal conductivity of each thickness slice are matched according to the material layer composition type, porosity, reinforcing fiber ratio, and interfacial bonding ratio. The heat capacity of each thickness slice is calculated based on its thickness, equivalent density, and equivalent specific heat capacity, and the heat capacities of each thickness slice are summed to form the total heat capacity in the thickness direction. The thermal resistance of each thickness slice is calculated based on its thickness, projected area, and equivalent thermal conductivity. The thermal resistances of each thickness slice are then summed to form the cumulative thermal resistance of the thickness slices. Based on the temperature change trends of the hot and cold surface boundary temperature sequences during the initial heating stage, the thermal lag time in the thickness direction is calculated, and the thermal capacity lag deduction is calculated based on the total heat capacity in the thickness direction and the thermal lag time in the thickness direction. Within the steady-state evaluation interval, the difference between the hot surface boundary temperature sequence and the cold surface boundary temperature sequence is calculated, and the heat flow in the thickness direction is calculated by combining the cumulative thermal resistance of the thickness slice. The lateral equivalent thermal resistance is calculated based on the lateral boundary area, lateral edge layer thickness, and lateral edge layer thermal conductivity in the thermal insulation material structural twin. The lateral heat flow is calculated based on the temperature difference between the lateral boundary temperature sequence and the hot surface boundary temperature sequence, as well as the lateral equivalent thermal resistance. The lateral heat flow is then compared with the average input power of the input power sequence within the steady-state evaluation interval to obtain the lateral leakage ratio.

[0012] As a preferred embodiment of the digital twin-based thermal resistance evaluation method for thermal insulation materials described in this invention, the generation of the edge thermal resistance set and the target thermal resistance closed chain specifically includes: Read the steady-state apparent thermal resistance, thermal capacity hysteresis deduction, and lateral leakage ratio. After deducting the thermal capacity hysteresis deduction from the steady-state apparent thermal resistance, correct it according to the lateral leakage ratio to obtain the effective thermal resistance of the target insulation material. Read the thermal resistance connection edges and corresponding thickness slice attributes in the thermal resistance pass contract diagram, calculate the structural hindrance weight of each thermal resistance connection edge based on the thickness slice attributes, and allocate the effective thermal resistance of the target thermal insulation material to each thermal resistance connection edge according to the proportion of the structural hindrance weight of each thermal resistance connection edge, thereby generating an edge thermal resistance set. Read the edge thermal resistance set sequentially from the hot side boundary to the cold side boundary in the thickness direction. Merge the edges with consecutive edge thermal resistances greater than the average edge thermal resistance into candidate thermal resistance lock chains, and take the candidate thermal resistance lock chain with the largest cumulative edge thermal resistance value as the target thermal resistance lock chain.

[0013] As a preferred embodiment of the thermal resistance evaluation method for thermal insulation materials based on digital twins described in this invention, the generation of thermal resistance evaluation results for thermal insulation materials refers to the extraction of the effective thermal resistance, lateral leakage ratio, edge thermal resistance set, and target thermal resistance closed chain of the target thermal insulation material, and then associating and storing them with the structural twin of the thermal insulation material.

[0014] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the method for evaluating the thermal resistance of thermal insulation materials based on digital twins as described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for evaluating the thermal resistance of thermal insulation materials based on digital twins as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By constructing a structural twin of the thermal insulation material and generating a thickness slice attribute table, a high-fidelity digital mapping of the microstructure of heterogeneous materials is achieved, solving the problem of coarse modeling. Combining the thermal resistance traffic contract diagram and double-boundary thermal potential loading, the heat conduction path is clarified using a graph theory expression method. The dynamic thermal response of the material is accurately captured by the combination of constant power and constant temperature loading. In terms of data processing, the invention innovatively uses the thermal capacity hysteresis deduction and the lateral leakage ratio to double correct the steady-state apparent thermal resistance, effectively eliminating the measurement errors caused by the material's own thermal capacity heat absorption and lateral heat dissipation, thereby calculating the effective thermal resistance of the target thermal insulation material that is closer to the material's essential properties. Finally, by generating a target thermal resistance closed chain, not only can the overall thermal insulation performance of the material be accurately quantified, but also the "thermal resistance weak layer" or "thermal leakage channel" in the multi-layer structure can be located, improving the accuracy and microscopic analysis capability of thermal resistance assessment of thermal insulation materials. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for evaluating the thermal resistance of insulation materials based on digital twins.

[0019] Figure 2 A flowchart for obtaining steady-state apparent thermal resistance.

[0020] Figure 3A flowchart for generating thermal resistance assessment results for insulation materials.

[0021] Figure 4 A flowchart for obtaining the thickness slice attribute table. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for evaluating the thermal resistance of thermal insulation materials based on digital twins, including the following steps: S1: Collect the sample geometry data, material layer data, sample boundary data and edge protection layer data of the target thermal insulation material, construct a structural twin of the thermal insulation material, cut the target thermal insulation material along the thickness direction, and obtain the thickness slice attribute table.

[0026] S1.1: It should be noted that the target thermal insulation material is placed on a flat measurement reference surface, and a 3D scanner is used to scan the outer surface of the target thermal insulation material to obtain point cloud data of the outer surface of the target thermal insulation material. After coordinate correction and noise point removal of the outer surface point cloud data, the side surface in contact with the constant power hot surface loading plate is identified according to the thickness direction of the target thermal insulation material to obtain the hot surface boundary, and the coordinates of the outer edge points of the hot surface boundary are extracted. The two-dimensional contour point set of the hot surface boundary is formed by arranging the outer edge points circumferentially. The side surface in contact with the constant temperature cold surface loading plate is identified according to the thickness direction of the target thermal insulation material to obtain the cold surface boundary; the area of ​​the hot surface boundary is calculated based on the two-dimensional contour point set of the hot surface boundary, using the following formula: ; in, Represents the area of ​​the thermal boundary. Indicates the number of points on the two-dimensional contour. Indicates the first The x and y coordinates of a two-dimensional contour point.

[0027] After identifying the circumferential outer surface located between the hot and cold surface boundaries as the lateral boundaries, a lateral unfolding coordinate system is established based on the thickness direction coordinate relationship. The lateral unfolding coordinate system uses the direction from the hot surface boundary to the cold surface boundary as the longitudinal coordinate, and the cumulative arc length of the outer edge points of the lateral boundary along the continuous boundary path on the same thickness coordinate section as the transverse coordinate. Outer edge points on the lateral boundary are sequentially intercepted along the thickness direction, and the three-dimensional spatial distance between adjacent outer edge points within the same thickness coordinate section is calculated. This distance is then accumulated along the continuous boundary path to form the transverse coordinate of the corresponding outer edge point. The thickness direction coordinate corresponding to the same outer edge point is used as the longitudinal coordinate to form a set of lateral boundary unfolding points. Based on the connection relationship between the corresponding outer edge points of two adjacent thickness coordinate sections, the set of lateral boundary unfolding points is divided into multiple lateral unfolding units. The transverse side length of each lateral unfolding unit is determined by the three-dimensional spatial distance between adjacent outer edge points within the same thickness coordinate section, and the longitudinal side length of each lateral unfolding unit is determined by the thickness direction distance between two adjacent thickness coordinate sections, ensuring that the unfolded area of ​​the lateral unfolding unit is consistent with the area of ​​the corresponding local curved surface of the lateral boundary. The lateral area is obtained by summing the unfolded areas of all lateral unfolded units.

[0028] Precision calipers, laser rangefinders, or 3D measuring instruments are used to measure the length, width, and thickness of the sample along the length, width, and thickness directions of the target thermal insulation material, respectively, to generate sample geometric data. The positions of each material layer are identified along the thickness direction of the target thermal insulation material. The interlayer interface positions are read using micro-CT scanning, cross-sectional microscopic observation, or material process records. Each material layer is assigned a material layer number, and the material layer thickness is calculated based on the positions of adjacent interlayer interfaces. The material layer composition type is then read from the material formulation record.

[0029] Miniature CT scanning or cross-sectional microscopy were used to acquire cross-sectional images of the target thermal insulation material along its thickness. These images were then divided into corresponding material layer image regions according to the material layer number. Each material layer image region was converted to grayscale to obtain a grayscale image. Based on the grayscale histogram of the grayscale image, thresholds for pore segmentation, reinforcing fiber segmentation, and interface bonding segmentation were determined. The pore segmentation threshold corresponds to the grayscale boundary between low-grayscale void regions and the matrix region in the material layer grayscale image. The reinforcing fiber segmentation threshold... The threshold corresponds to the grayscale boundary value between the fiber region and the matrix region in the grayscale image of the material layer, and the interface bonding segmentation threshold corresponds to the grayscale boundary value between the bonded region and the non-bonded region in the boundary region of adjacent material layers. When the grayscale value of the material layer grayscale image is normalized to 0~255, the preferred value for the pore segmentation threshold is 50, the preferred value for the reinforcing fiber segmentation threshold is 180, and the preferred value for the interface bonding segmentation threshold is 120. When there is a deviation between the grayscale valley value in the grayscale histogram and the preferred value, the grayscale valley value in the grayscale histogram is used as the corresponding segmentation threshold.

[0030] Regions in the grayscale image of the material layer whose grayscale values ​​meet the pore segmentation threshold range and have closed boundaries are identified as pore regions. Regions in the grayscale image of the material layer whose grayscale values ​​meet the reinforcing fiber segmentation threshold range and have a slender and continuous shape are identified as reinforcing fiber regions. Regions in the boundary region of adjacent material layers whose grayscale values ​​meet the interface bonding segmentation threshold range and are located near the interlayer interface are identified as interface bonding regions. The pixel areas of pore regions, reinforcing fiber regions, interface bonding regions, and material layer image regions are statistically analyzed. The ratio of the pixel area of ​​pore regions to the pixel area of ​​material layer image regions is calculated to obtain the pore ratio. The ratio of the pixel area of ​​reinforcing fiber regions to the pixel area of ​​material layer image regions is calculated to obtain the reinforcing fiber ratio. The ratio of the pixel area of ​​interface bonding regions to the pixel area of ​​material layer image regions is calculated to obtain the interface bonding ratio. The material layer number, material layer thickness, material layer composition type, pore ratio, reinforcing fiber ratio, and interface bonding ratio are linked and recorded to form material layer data.

[0031] The positions of the hot surface boundary and the cold surface boundary in the thickness direction are recorded as the hot surface boundary position, the cold surface boundary and the lateral boundary in the thickness direction are recorded as the lateral boundary position, thus forming the sample boundary data. The lateral edge protection layer covering the lateral boundary positions is measured using a thickness gauge or calipers. The thermal conductivity of the edge protection layer is read from the edge protection layer material test report, and the lateral boundary area is calculated based on the lateral boundary contour covered by the edge protection layer, thus forming the edge protection layer data.

[0032] Furthermore, the thickness direction refers to the heat transfer direction from the hot surface boundary to the cold surface boundary, which is the main heat transfer path. It is determined in three-dimensional space along the normal of the sample thickness and is perpendicular to the plane containing the hot and cold surface boundaries.

[0033] The material formulation record is generated by reading the preparation process document of the target thermal insulation material or by performing component analysis on the target thermal insulation material. Specifically, when the target thermal insulation material has a preparation process document, the raw material name, raw material mass fraction, binder type, reinforcing fiber type, foaming agent type, curing temperature, and curing time corresponding to each material layer are read from the preparation process document, and the reading results are collected according to the material layer number to generate the material formulation record. When the target thermal insulation material does not have a preparation process document, samples are taken from each material layer along the thickness direction of the target thermal insulation material, and the sampled material layer samples are subjected to component analysis and microstructure observation to identify the matrix material, pore structure, reinforcing fiber, and interface bonding area in the material layer sample. The material formulation record is generated based on the component analysis results and microstructure observation results. The material formulation record includes the material layer number, material layer composition type, matrix material name, reinforcing fiber type, pore formation method, and interface bonding material type.

[0034] The edge protection layer material test report is generated by conducting thermophysical property tests on lateral edge protection layer material samples. Specifically, edge protection layer material samples are cut from the lateral edge protection layer material used to cover the lateral boundary, and the material name, sample thickness, and sample area of ​​the edge protection layer material sample are recorded. The edge protection layer material sample is placed in a thermal conductivity testing device, and a stable heat flux is applied to the edge protection layer material sample at a preset test temperature (e.g., 25°C). The temperature difference between the two sides of the edge protection layer material sample and the heat flux density passing through the edge protection layer material sample are collected. The thermal conductivity of the edge protection layer is calculated based on the sample thickness, the temperature difference between the two sides, and the heat flux density, using the following formula: ; in, This indicates the thermal conductivity of the edge protection layer. This indicates the temperature difference between the two sides of the edge protection layer material sample. This indicates the heat flux density through the edge protection layer material sample. This indicates the thickness of the edge protection layer material sample.

[0035] Record the material name, sample thickness, sample area, test temperature, temperature difference between the two sides, heat flux density, and thermal conductivity of the edge protection layer material sample, and generate an edge protection layer material test report.

[0036] S1.2: It should be noted that the sample thickness, material layer thickness, hot surface boundary position, and cold surface boundary position of the target thermal insulation material are read. The hot surface boundary position is used as the starting point of the thickness direction coordinates, and the cold surface boundary position is used as the ending point. A thickness direction reference axis is established in the direction pointing from the hot surface boundary position to the cold surface boundary position. The sample thickness is mapped onto the thickness direction reference axis, ensuring that the starting end of the sample thickness coincides with the hot surface boundary position, and the ending end of the sample thickness coincides with the cold surface boundary position. The starting and ending positions of each material layer are sequentially marked on the thickness direction reference axis according to the thickness of each material layer, and the starting and ending positions of each material layer are restricted to between the hot and cold surface boundaries. When the cumulative value of each material layer thickness is consistent with the sample thickness, the starting, ending, hot, and cold surface boundary positions of each material layer are linked and recorded to establish a thickness direction coordinate relationship between the hot and cold surface boundaries. The sample geometric data, material layer data, sample boundary data, and thickness direction coordinate relationship are linked and encapsulated to form a structural twin of the thermal insulation material.

[0037] S1.3: It should be noted that the sample thickness, hot surface boundary position, cold surface boundary position, and material layer thickness in the thermal insulation material structural twin are read. The hot surface boundary position is used as the starting position for segmentation, and the cold surface boundary position is used as the ending position for segmentation. The thickness of each material layer is read sequentially from the hot surface boundary position to the cold surface boundary position. According to the arrangement order of the thickness of each material layer in the thickness direction coordinate relationship, the starting position, ending position, and material layer boundary position of each material layer are marked as the basic segmentation position. Between the starting position and ending position of each material layer, intermediate segmentation positions are set according to the preset slice thickness interval, and the intermediate segmentation positions do not exceed the ending position of the corresponding material layer. The basic segmentation positions and intermediate segmentation positions are arranged in the thickness direction order from the hot surface boundary position to the cold surface boundary position to obtain the segmentation position of the target thermal insulation material along the thickness direction.

[0038] Furthermore, the slice thickness interval is set based on the thickness of the thinnest material layer in the target insulation material, the difference in thermal conductivity of the material layers, and the calculation accuracy requirements. It is generally set to 1 to 5 mm to ensure that the thickness slice can accurately reflect the structural characteristics and thermal conductivity of each material layer.

[0039] S1.4: It should be noted that, in the thickness direction from the hot side boundary to the cold side boundary, two adjacent cutting positions are read sequentially, and the two adjacent cutting positions are respectively used as the current cutting start position and the current cutting end position; the material layer number corresponding to the current cutting start position and the current cutting end position is read. When the current cutting start position and the current cutting end position correspond to the same material layer number, the material region between the current cutting start position and the current cutting end position is taken as a thickness slice; when the current cutting start position and the current cutting end position correspond to different material layer numbers, the material layer boundary position between the current cutting start position and the current cutting end position is read, the material region between the current cutting start position and the material layer boundary position is taken as a thickness slice, and the material region between the material layer boundary position and the current cutting end position is further divided according to the material layer number, so that each thickness slice is located inside the same material layer.

[0040] S1.5: It should be noted that each thickness slice generated along the thickness direction of the target thermal insulation material should be read one by one, and a unique slice number should be assigned to each thickness slice; the slice start position, slice end position, and slice thickness of each thickness slice should be recorded in sequence, and the slice projection area should be calculated based on the effective heat transfer cross section of the thickness slice in the direction from the hot surface boundary position to the cold surface boundary position. Specifically, the direction from the hot surface boundary position to the cold surface boundary position is taken as the thickness direction, and the plane where the hot surface boundary is located is taken as the projection reference plane; for regular flat plate samples, the thickness slice is directly orthogonally projected along the thickness direction to the projection reference plane to obtain the thickness slice projection contour; for irregular samples, oblique layers, or warped surfaces, the three-dimensional outer contour of the thickness slice between the slice start position and the slice end position is extracted based on the outer surface point cloud data, and the three-dimensional outer contour is orthogonally projected along the thickness direction to the projection reference plane to obtain the thickness slice projection contour; when the projection contour areas corresponding to the slice start position and the slice end position of the same thickness slice are inconsistent, the slice projection area is calculated separately. The starting projected area corresponding to the starting position of the slice and the ending projected area corresponding to the ending position of the slice are calculated, and the average of the starting projected area and the ending projected area is taken as the slice projected area. The two-dimensional contour point set in the projection contour of the thickness slice is extracted, and the starting projected area or the ending projected area is calculated using the polygon area formula according to the arrangement order of each two-dimensional contour point in the two-dimensional contour point set. The slice number, slice starting position, slice ending position, slice thickness, slice projected area, material layer number, material layer composition type, porosity, reinforcing fiber ratio and interface bonding ratio corresponding to each thickness slice are arranged in the thickness direction to form a thickness slice attribute table.

[0041] S2: Based on the thermal insulation material structural twin and thickness slice attribute table, after constructing the thermal resistance pass contract diagram, perform double boundary thermal potential loading on the target thermal insulation material to obtain the boundary thermal potential response sequence.

[0042] S2.1: It should be noted that the slice number, slice start position, slice end position, slice thickness, slice projected area, material layer number, material layer composition type, porosity, reinforcing fiber ratio, and interface bonding ratio corresponding to each thickness slice in the thickness slice attribute table should be read. Following the thickness direction from the hot side boundary to the cold side boundary, each thickness slice should be mapped to a thermal resistance node, and the slice number, slice thickness, slice projected area, material layer number, material layer composition type, porosity, reinforcing fiber ratio, and interface bonding ratio of the corresponding thickness slice should be written into the corresponding thermal resistance node. According to the arrangement relationship between two adjacent thickness slices, the contact interface between two adjacent thickness slices should be mapped to a thermal resistance connection edge, and the slice end position and slice start position corresponding to two adjacent thickness slices should be written into the corresponding thermal resistance connection edge. The thermal resistance nodes and thermal resistance connection edges should be connected sequentially according to the thickness direction from the hot side boundary to the cold side boundary to form a thermal resistance access contract diagram.

[0043] S2.2: It should be noted that before the dual-boundary thermal loading begins, the hot surface temperature sensor is attached to the hot surface boundary of the target thermal insulation material, the cold surface temperature sensor is attached to the cold surface boundary of the target thermal insulation material, the lateral temperature sensor is attached to the lateral boundary of the target thermal insulation material, the power acquisition unit is connected to the power supply circuit of the constant power hot surface loading plate, and the clamp pressure sensor is set at the clamping position between the constant power hot surface loading plate and the constant temperature cold surface loading plate.

[0044] Read the positions of the hot and cold sides of the thermal resistance contract diagram, and place the target insulation material along the thickness direction so that the hot side boundary is attached to the constant power hot side loading plate and the cold side boundary is attached to the constant temperature cold side loading plate. While keeping the positions of the hot and cold sides fixed, apply a constant input power to the hot side boundary through the constant power hot side loading plate and maintain a constant temperature of the cold side boundary through the constant temperature cold side loading plate. Cover the lateral edge with the lateral edge layer so that the coverage area of ​​the lateral edge layer corresponds to the area of ​​the lateral boundary, and keep the lateral edge layer attached to the lateral boundary, thereby performing dual-boundary thermal potential loading on the target insulation material.

[0045] During the dual-boundary thermal potential loading process, the hot surface boundary temperature value output by the hot surface temperature sensor, the cold surface boundary temperature value output by the cold surface temperature sensor, the lateral boundary temperature value output by the lateral temperature sensor, and the input power value output by the power acquisition unit are synchronously read according to a preset sampling period. The hot surface boundary temperature value corresponding to each sampling moment is arranged sequentially to form a hot surface boundary temperature sequence, the cold surface boundary temperature value corresponding to each sampling moment is arranged sequentially to form a cold surface boundary temperature sequence, the lateral boundary temperature value corresponding to each sampling moment is arranged sequentially to form a lateral boundary temperature sequence, and the input power value corresponding to each sampling moment is arranged sequentially to form an input power sequence. The hot surface boundary temperature sequence, cold surface boundary temperature sequence, lateral boundary temperature sequence, and input power sequence are aligned according to the same sampling moment to obtain the boundary thermal potential response sequence.

[0046] Furthermore, the sampling period is set according to the thermal response characteristics of the target insulation material. For thin-layer high thermal conductivity materials, it can be set to 0.5 seconds; for conventional thickness insulation materials, it can be set to 1 second; and for thick-layer or low thermal conductivity materials, it can be set to 5 seconds, so as to ensure that the change sequence of hot surface boundary temperature, cold surface boundary temperature, lateral boundary temperature, input power and fixture pressure can be captured completely and accurately.

[0047] S3: Select the steady-state evaluation interval based on the boundary thermal potential response sequence and calculate the steady-state apparent thermal resistance.

[0048] S3.1: It should be noted that the hot surface boundary temperature sequence, cold surface boundary temperature sequence and input power sequence in the boundary thermal potential response sequence are read, and the ratio of the hot surface boundary temperature difference to the time difference between adjacent sampling times is calculated as the hot surface boundary temperature change rate according to the continuous sampling time generated according to the preset sampling period. At the same time, the ratio of the cold surface boundary temperature difference to the time difference between adjacent sampling times is calculated as the cold surface boundary temperature change rate.

[0049] S3.2: It should be noted that, according to the continuous sampling time generated by the preset sampling period, the hot surface boundary temperature change rate, the cold surface boundary temperature change rate, and the input power sequence are read one by one; when the hot surface boundary temperature change rate corresponding to multiple consecutive sampling times is less than the preset hot surface stability threshold, the cold surface boundary temperature change rate is less than the preset cold surface stability threshold, and the input power difference between adjacent sampling times in the input power sequence is less than the preset power fluctuation threshold, the time period corresponding to multiple consecutive sampling times is marked as a candidate stable period; when the duration of the candidate stable period reaches the preset stable duration, the candidate stable period is used as the steady-state evaluation interval.

[0050] Furthermore, the hot surface stability threshold is set based on the measurement resolution and steady-state determination accuracy of the hot surface boundary temperature sequence, with a preferred value of 0.02℃ / min.

[0051] The cold surface stability threshold is set based on the measurement resolution of the cold surface boundary temperature sequence and the heat penetration response hysteresis characteristics, for example, a value of 0.02℃ / min.

[0052] The power fluctuation threshold is set based on the output stability of the constant power hot surface loading plate and the acquisition error of the input power sequence, and is set to ±1% of the average input power.

[0053] The stabilization duration is set based on the temperature fluctuation period and sampling period after the target insulation material reaches thermal equilibrium, and is set to 10 min.

[0054] S3.3: It should be noted that within the steady-state evaluation interval, the average values ​​of the hot surface boundary temperature sequence and the cold surface boundary temperature sequence are calculated separately to obtain the average temperature of the hot surface boundary and the average temperature of the cold surface boundary. The input power values ​​corresponding to each sampling time within the steady-state evaluation interval are read from the input power sequence. These input power values ​​are accumulated according to the sampling time sequence, and the accumulated result is divided by the number of sampling points within the steady-state evaluation interval to obtain the average input power within the steady-state evaluation interval. The effective metering area is determined based on the contact range between the constant power hot surface loading plate and the hot surface boundary. When the constant power hot surface loading plate completely covers the hot surface boundary, the area of ​​the hot surface boundary is used as the effective metering area; when the constant power hot surface loading plate does not completely cover the hot surface boundary, the area covered by the constant power hot surface loading plate on the hot surface boundary is used as the effective metering area. The average input power is divided by the effective metering area to obtain the surface heat flux density. The difference between the average temperature of the hot surface boundary and the average temperature of the cold surface boundary is calculated and then divided by the surface heat flux density to obtain the steady-state apparent thermal resistance.

[0055] S4: Calculate the lateral leakage ratio after calculating the thermal capacity hysteresis deduction based on the boundary thermal potential response sequence and thickness slice attribute table.

[0056] S4.1: It should be noted that the hot-side boundary temperature sequence, cold-side boundary temperature sequence, lateral boundary temperature sequence, and input power sequence in the boundary thermal potential response sequence should be read, and the slice thickness, slice projected area, material layer composition type, porosity, reinforcing fiber ratio, and interface bonding ratio of each thickness slice in the thickness slice attribute table should be read. For each thickness slice, the basic density, basic specific heat capacity, and basic thermal conductivity should first be matched in the preset material thermal property table according to the material layer composition type; the basic density, basic specific heat capacity, and basic thermal conductivity should be corrected according to the porosity, reinforcing fiber ratio, and interface bonding ratio, using the following formula: ; ; ; in, Indicates the first The equivalent density of a slice of a certain thickness Indicates the first The equivalent specific heat capacity of a slice of a certain thickness Indicates the first The equivalent thermal conductivity of a slice of each thickness. Indicates the first Each thickness slice corresponds to the basic density of the material layer composition type. Indicates the first The thickness of each slice corresponds to the basic specific heat capacity of the material layer composition type. Indicates the first The basic thermal conductivity of the material layer composition type corresponding to each thickness slice Indicates the first Pore ​​percentage of slices of different thicknesses Indicates the first The percentage of reinforcing fibers in slices of varying thicknesses. Indicates the first The percentage of interfacial bonding in slices of different thicknesses.

[0057] Furthermore, the material thermal property table is compiled by consulting the material supplier's manual to obtain the basic density, basic specific heat capacity, and basic thermal conductivity corresponding to each material layer composition type.

[0058] S4.2: It should be noted that the thickness, projected area, equivalent density, and equivalent specific heat capacity of each thickness slice in the thickness slice attribute table should be read one by one. For each thickness slice, the corresponding slice thickness and projected area should be multiplied to calculate the slice volume. The slice volume and equivalent density should be multiplied to calculate the slice mass. The slice mass and equivalent specific heat capacity should be multiplied to calculate the slice heat capacity. The heat capacity values ​​of each thickness slice should be accumulated sequentially along the thickness direction to obtain the total heat capacity of the target insulation material along the thickness direction. At the same time, the thickness, projected area, and equivalent thermal conductivity of each thickness slice should be read one by one. The equivalent thermal conductivity and projected area should be multiplied to calculate the slice thermal conductivity area correlation value. The thickness and thermal conductivity area correlation value of each thickness slice should be divided to obtain the thermal resistance value of each thickness slice. The thermal resistance values ​​of each thickness slice should be accumulated sequentially along the thickness direction to form the cumulative thermal resistance of the thickness slice.

[0059] S4.3: It should be noted that the hot surface boundary temperature sequence and the cold surface boundary temperature sequence in the boundary thermal potential response sequence are read, and the preset time period (e.g., 300s) after the start of the dual-boundary thermal potential loading is taken as the initial heating stage; the hot surface boundary temperature value corresponding to the start time of the initial heating stage is read as the initial hot surface boundary temperature, and the cold surface boundary temperature value corresponding to the start time of the initial heating stage is read as the initial cold surface boundary temperature; the hot surface boundary temperature value in the hot surface boundary temperature sequence is read one by one according to the sampling time sequence, and the initial hot surface boundary temperature is subtracted from the hot surface boundary temperature value at each sampling time to obtain the hot surface boundary temperature rise at the corresponding sampling time; at the same time, the cold surface boundary temperature value in the cold surface boundary temperature sequence is read one by one according to the sampling time sequence, and the initial cold surface boundary temperature is subtracted from the cold surface boundary temperature value at each sampling time to obtain the cold surface boundary temperature rise at the corresponding sampling time.

[0060] During the initial heating phase, the maximum hot surface boundary temperature value in the hot surface boundary temperature sequence is read, and the difference between the maximum hot surface boundary temperature value and the initial hot surface boundary temperature is taken as the maximum temperature rise of the hot surface boundary; the maximum cold surface boundary temperature value in the cold surface boundary temperature sequence is read, and the difference between the maximum cold surface boundary temperature value and the initial cold surface boundary temperature is taken as the maximum temperature rise of the cold surface boundary; the maximum temperature rise of the hot surface boundary is multiplied by a preset temperature rise ratio to obtain the target temperature rise of the hot surface boundary; the maximum temperature rise of the cold surface boundary is multiplied by a preset temperature rise ratio to obtain the target temperature rise of the cold surface boundary.

[0061] When the temperature rise at the hot surface boundary first reaches the target temperature rise at the hot surface boundary, the corresponding sampling time is recorded as the hot surface response time. When the temperature rise at the cold surface boundary first reaches the target temperature rise at the cold surface boundary, the corresponding sampling time is recorded as the cold surface response time. The time difference between the cold surface response time and the hot surface response time is taken as the thermal hysteresis time in the thickness direction. The total heat capacity in the thickness direction is read, and the product of the total heat capacity in the thickness direction and the temperature difference between the average temperature of the hot surface boundary and the average temperature of the cold surface boundary is used to calculate the quotient with the thermal hysteresis time in the thickness direction to obtain the thermal capacity hysteresis power. The ratio of the thermal capacity hysteresis power to the average input power is taken as the thermal capacity hysteresis ratio. The steady-state apparent thermal resistance is multiplied by the thermal capacity hysteresis ratio to obtain the thermal capacity hysteresis deduction.

[0062] Furthermore, the temperature rise ratio is set to 10% based on the temperature response amplitude of the hot and cold surface boundary temperature sequences during the initial heating stage and the temperature sensor measurement noise.

[0063] S4.4: It should be noted that within the steady-state evaluation interval, the temperature values ​​at the corresponding sampling times of the hot and cold surface boundary temperature sequences are read one by one, and the difference between the hot and cold surface boundary temperatures at each sampling time is calculated as the instantaneous thickness-direction temperature difference; the slice thickness and equivalent thermal conductivity of each thickness slice are read, and the slice thickness and equivalent thermal conductivity are compared to obtain the thickness slice surface thermal resistance of each thickness slice; the surface thermal resistances of each thickness slice are accumulated in the thickness direction order to form the cumulative surface thermal resistance of the thickness slice; the instantaneous thickness-direction temperature difference at each sampling time within the steady-state evaluation interval is compared with the cumulative surface thermal resistance of the thickness slice to obtain the instantaneous thickness-direction surface heat flux density at the corresponding sampling time; the average of the instantaneous thickness-direction surface heat flux densities at all sampling times within the steady-state evaluation interval is calculated to obtain the thickness-direction surface heat flux density.

[0064] S4.5: It should be noted that the lateral boundary area, lateral edge layer thickness, and lateral edge layer thermal conductivity of the insulation material structural twin are read; the lateral edge layer thermal conductivity is multiplied by the lateral boundary area to obtain the lateral heat conduction area correlation value; the lateral edge layer thickness is divided by the lateral heat conduction area correlation value to obtain the lateral equivalent thermal resistance; within the steady-state evaluation interval, the lateral boundary temperature value in the lateral boundary temperature sequence and the hot surface boundary temperature value in the hot surface boundary temperature sequence are read one by one according to the sampling time, and the difference between the hot surface boundary temperature value and the lateral boundary temperature value is calculated to obtain the lateral temperature difference corresponding to each sampling time; the lateral temperature difference corresponding to each sampling time is divided by the lateral equivalent thermal resistance to obtain the instantaneous lateral heat flow corresponding to each sampling time; the instantaneous lateral heat flow corresponding to each sampling time within the steady-state evaluation interval is averaged to obtain the lateral heat flow; the average input power of the input power sequence within the steady-state evaluation interval is read, and the ratio of the lateral heat flow to the average input power is calculated to obtain the lateral leakage ratio.

[0065] S5: Calculate the effective thermal resistance of the target insulation material based on steady-state apparent thermal resistance, thermal capacity hysteresis deduction, and lateral leakage ratio. Allocate the effective thermal resistance of the target insulation material according to the thermal resistance pass contract diagram to generate edge thermal resistance set and target thermal resistance closed chain.

[0066] S5.1: It should be noted that the effective thermal resistance of the target insulation material is calculated by reading the steady-state apparent thermal resistance, the thermal capacity hysteresis deduction, and the lateral leakage ratio. The formula is as follows: ; in, Indicates the effective thermal resistance of the target insulation material. Indicates the steady-state apparent thermal resistance. This indicates the hysteresis deduction of heat capacity. This indicates the percentage of lateral leakage.

[0067] Furthermore, during the calculation process, the heat capacity hysteresis deduction is less than the steady-state apparent thermal resistance, and the lateral leakage ratio is greater than or equal to 0 and less than 1, in order to ensure that the effective thermal resistance of the target insulation material is positive and to avoid the denominator being zero.

[0068] S5.2: It should be noted that the thermal resistance connection edges and corresponding thickness slice attributes of each thermal resistance connection edge in the thermal resistance contract diagram should be read one by one. The thickness slice attributes include thickness, slice projected area, and equivalent thermal conductivity (obtained from the preset material thermal property table). Based on the thickness, slice projected area, and equivalent thermal conductivity of the thickness slice, the slice thermal resistance of the corresponding thermal resistance connection edge should be calculated using the following formula: ; in, Indicates the first The thermal resistance of the slice along the thermal resistance connection edge. Indicates the first The thickness of the thermal resistance connection edge corresponds to the thickness of the slice. Indicates the first The equivalent thermal conductivity of the slice corresponding to the thickness of the thermal resistance connection edge. Indicates the first The projected area of ​​the slice corresponding to the thickness of the thermal resistance connection edge.

[0069] The slice thermal resistance of each thermal resistance connection edge is used as the structural retardation weight of the corresponding thermal resistance connection edge. The structural retardation weights of all thermal resistance connection edges are summed to obtain the total structural retardation weight. The structural retardation weight of each thermal resistance connection edge is divided by the total structural retardation weight to obtain the normalized weight ratio of each thermal resistance connection edge. The effective thermal resistance of the target thermal insulation material is multiplied by the normalized weight ratio of each thermal resistance connection edge to obtain the edge thermal resistance value of each thermal resistance connection edge. The edge thermal resistance value of each thermal resistance connection edge is written into the corresponding thermal resistance connection edge in the thermal resistance convention diagram, and the edge thermal resistance values ​​of each thermal resistance connection edge are arranged in the thickness direction from the hot side boundary to the cold side boundary to generate the edge thermal resistance set.

[0070] S5.3: It should be noted that the edge thermal resistance set is read, and the edge thermal resistance values ​​corresponding to each thermal resistance connection edge in the edge thermal resistance set are arranged in the thickness direction from the hot surface boundary to the cold surface boundary; the average edge thermal resistance is calculated by averaging all the edge thermal resistance values ​​in the edge thermal resistance set; the edge thermal resistance value corresponding to each thermal resistance connection edge is compared with the average edge thermal resistance in the thickness direction one by one; when the edge thermal resistance values ​​corresponding to adjacent thermal resistance connection edges are all greater than the average edge thermal resistance, the adjacent thermal resistance connection edges are continuously merged into candidate thermal resistance closed chains; the edge thermal resistance values ​​corresponding to each thermal resistance connection edge in each candidate thermal resistance closed chain are accumulated to obtain the accumulated edge thermal resistance value of each candidate thermal resistance closed chain; compared with... The cumulative edge thermal resistance values ​​of each candidate thermal resistance lock chain are compared, and the candidate thermal resistance lock chain with the largest cumulative edge thermal resistance value is selected as the target thermal resistance lock chain. The target thermal resistance lock chain is used to characterize the thermal conduction bottleneck region of the target thermal insulation material along the thickness direction. When the thermal resistance connection edge corresponding to the target thermal resistance lock chain is located inside the same material layer, the target thermal resistance lock chain corresponds to a high thermal resistance layer segment. When the thermal resistance connection edge corresponding to the target thermal resistance lock chain is located at the interface of adjacent material layers, the target thermal resistance lock chain corresponds to an interface hindrance region. When the porosity of the thickness slice where the thermal resistance connection edge corresponding to the target thermal resistance lock chain is located is higher than the porosity of the adjacent thickness slice, the target thermal resistance lock chain corresponds to a defect area indication location.

[0071] After reading the effective thermal resistance, lateral leakage ratio, edge thermal resistance set, and target thermal resistance lock chain of the target thermal insulation material, it is associated with and stored with the structural twin of the thermal insulation material to obtain the thermal resistance evaluation result of the thermal insulation material.

[0072] This embodiment also provides a computer device applicable to the thermal resistance evaluation method of thermal insulation materials based on digital twins, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the thermal resistance evaluation method of thermal insulation materials based on digital twins as proposed in the above embodiment.

[0073] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0074] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for evaluating the thermal resistance of thermal insulation materials based on digital twins as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0075] In summary, this invention achieves high-fidelity digital mapping of the microstructure of heterogeneous materials by constructing a structural twin of the thermal insulation material and generating a thickness slice attribute table, thus solving the problem of coarse modeling. By combining a thermal resistance traffic contract diagram and dual-boundary thermal potential loading, the heat conduction path is clearly defined using a graph-based representation. Furthermore, the dynamic thermal response of the material is accurately captured through the combination of constant power and constant temperature loading. In data processing, the invention innovatively uses a dual correction of steady-state apparent thermal resistance using thermal capacity hysteresis deduction and lateral leakage ratio, effectively eliminating measurement errors caused by the material's own thermal capacity absorption and lateral heat dissipation. This allows for the calculation of the effective thermal resistance of the target thermal insulation material that more closely approximates its intrinsic properties. Finally, by generating a target thermal resistance closed-loop chain, not only can the overall thermal insulation performance of the material be accurately quantified, but also the "weak thermal resistance layer" or "thermal leakage channel" in the multi-layer structure can be located, improving the accuracy and microscopic analytical capability of thermal resistance assessment for thermal insulation materials.

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating the thermal resistance of insulation materials based on digital twins, characterized in that: include, Collect the sample geometry data, material layer data, sample boundary data and edge protection layer data of the target thermal insulation material, construct a structural twin of the thermal insulation material, cut the target thermal insulation material along the thickness direction, and obtain the thickness slice attribute table. Based on the structural twin and thickness slice attribute table of the thermal insulation material, after constructing the thermal resistance pass contract diagram, a double boundary thermal potential loading is performed on the target thermal insulation material to obtain the boundary thermal potential response sequence. The steady-state evaluation interval is selected based on the boundary thermal potential response sequence, and the steady-state apparent thermal resistance is calculated. The thermal capacity hysteresis deduction and lateral leakage ratio are calculated based on the boundary thermal potential response sequence and thickness slice attribute table. The effective thermal resistance of the target insulation material is calculated based on the steady-state apparent thermal resistance, the thermal capacity hysteresis deduction, and the lateral leakage ratio. The effective thermal resistance of the target insulation material is allocated according to the thermal resistance pass contract diagram to generate the edge thermal resistance set and the target thermal resistance closed chain. The effective thermal resistance, lateral leakage ratio, edge thermal resistance set, and target thermal resistance lock chain of the target thermal insulation material are linked to generate thermal resistance assessment results for the thermal insulation material.

2. The method for evaluating the thermal resistance of insulation materials based on digital twins as described in claim 1, characterized in that: The construction of the thermal insulation material structural twin specifically involves: The sample geometry data includes sample length, sample width, sample thickness, hot surface boundary area, and lateral area. The material layer data includes material layer number, material layer thickness, material layer composition type, porosity, reinforcing fiber ratio, and interfacial bonding ratio. The sample boundary data includes the hot surface boundary position, the cold surface boundary position, and the lateral boundary position; The edge protection layer data includes the lateral edge protection layer thickness, the lateral edge protection layer thermal conductivity, and the lateral boundary area; According to the thickness direction of the target thermal insulation material, the corresponding sample thickness, material layer thickness, hot surface boundary position and cold surface boundary position are spatially aligned to establish the thickness direction coordinate relationship between the hot surface boundary and the cold surface boundary. By associating and encapsulating the sample geometry data, material layer data, sample boundary data, and thickness direction coordinate relationship, a structural twin of the thermal insulation material is constructed.

3. The method for evaluating the thermal resistance of insulation materials based on digital twins as described in claim 2, characterized in that: The specific steps for obtaining the thickness slice attribute table are as follows: Read the sample thickness, hot surface boundary position, cold surface boundary position and material layer thickness in the thermal insulation material structural twin, and set the cutting positions sequentially from the hot surface boundary position to the cold surface boundary position along the thickness direction of the target thermal insulation material; When the cutting position falls within the same material layer, the material region between two adjacent cutting positions is taken as a thickness slice. When the cutting position crosses the boundary of adjacent material layers, the boundary of the material layers is taken as the boundary of the thickness slice. Collect the slice number, slice start position, slice end position, slice thickness, slice projected area, material layer number, material layer composition type, porosity, reinforcing fiber ratio, and interface bonding ratio for each thickness slice, and arrange them in the thickness direction to obtain the thickness slice attribute table.

4. The method for evaluating the thermal resistance of insulation materials based on digital twins as described in claim 3, characterized in that: The acquisition of the boundary thermal potential response sequence specifically involves: Based on the thickness slice attribute table, each thickness slice is used as a thermal resistance node, and the contact interface between two adjacent thickness slices is used as a thermal resistance connection edge. The thermal resistance nodes and thermal resistance connection edges are connected in the thickness direction from the hot surface boundary position to the cold surface boundary position to construct a thermal resistance access contract graph. Based on the hot and cold boundary positions in the thermal resistance contract diagram, the target insulation material is placed between the constant power hot surface loading plate and the constant temperature cold surface loading plate, and dual-boundary thermal potential loading is applied to the target insulation material. During the dual-boundary thermal potential loading process, the hot surface boundary temperature sequence, cold surface boundary temperature sequence, lateral boundary temperature sequence and input power sequence are collected and aligned according to the preset sampling period to obtain the boundary thermal potential response sequence.

5. The method for evaluating the thermal resistance of insulation materials based on digital twins as described in claim 4, characterized in that: The calculation of steady-state apparent thermal resistance is specifically as follows: Read the hot surface boundary temperature sequence, cold surface boundary temperature sequence, and input power sequence from the boundary thermal potential response sequence, and calculate the hot surface boundary temperature change rate and cold surface boundary temperature change rate according to the sampling time. The continuous sampling period during which the hot surface boundary temperature change rate is less than the preset hot surface stability threshold, the cold surface boundary temperature change rate is less than the preset cold surface stability threshold, and the input power sequence remains constant is defined as the steady-state evaluation interval. Within the steady-state evaluation interval, the average values ​​of the hot surface boundary temperature sequence and the cold surface boundary temperature sequence are calculated respectively to obtain the average temperature of the hot surface boundary and the average temperature of the cold surface boundary. The surface heat flux density is calculated based on the average input power and the hot surface boundary area within the steady-state evaluation interval of the input power sequence. The steady-state apparent thermal resistance is obtained by calculating the difference between the average temperature of the hot surface boundary and the average temperature of the cold surface boundary, and then quotienting it with the surface heat flux density.

6. The method for evaluating the thermal resistance of insulation materials based on digital twins as described in claim 5, characterized in that: The calculation of the lateral leakage ratio is specifically as follows: Read the hot surface boundary temperature sequence, cold surface boundary temperature sequence, lateral boundary temperature sequence and input power sequence from the boundary thermal potential response sequence, and read the slice thickness, slice projected area, material layer composition type, porosity, reinforcing fiber ratio and interface bonding ratio of each thickness slice in the thickness slice attribute table; The equivalent density, equivalent specific heat capacity, and equivalent thermal conductivity of each thickness slice are matched according to the material layer composition type, porosity, reinforcing fiber ratio, and interfacial bonding ratio. The heat capacity of each thickness slice is calculated based on its thickness, equivalent density, and equivalent specific heat capacity, and the heat capacities of each thickness slice are summed to form the total heat capacity in the thickness direction. The thermal resistance of each thickness slice is calculated based on its thickness, projected area, and equivalent thermal conductivity. The thermal resistances of each thickness slice are then summed to form the cumulative thermal resistance of the thickness slices. Based on the temperature change trends of the hot and cold surface boundary temperature sequences during the initial heating stage, the thermal lag time in the thickness direction is calculated, and the thermal capacity lag deduction is calculated based on the total heat capacity in the thickness direction and the thermal lag time in the thickness direction. Within the steady-state evaluation interval, the difference between the hot surface boundary temperature sequence and the cold surface boundary temperature sequence is calculated, and the heat flow in the thickness direction is calculated by combining the cumulative thermal resistance of the thickness slice. The lateral equivalent thermal resistance is calculated based on the lateral boundary area, lateral edge layer thickness, and lateral edge layer thermal conductivity in the thermal insulation material structural twin. The lateral heat flow is calculated based on the temperature difference between the lateral boundary temperature sequence and the hot surface boundary temperature sequence, as well as the lateral equivalent thermal resistance. The lateral heat flow is then compared with the average input power of the input power sequence within the steady-state evaluation interval to obtain the lateral leakage ratio.

7. The method for evaluating the thermal resistance of insulation materials based on digital twins as described in claim 6, characterized in that: The generated edge thermal resistance set and the target thermal resistance closed chain are specifically as follows: Read the steady-state apparent thermal resistance, thermal capacity hysteresis deduction, and lateral leakage ratio. After deducting the thermal capacity hysteresis deduction from the steady-state apparent thermal resistance, correct it according to the lateral leakage ratio to obtain the effective thermal resistance of the target insulation material. Read the thermal resistance connection edges and corresponding thickness slice attributes in the thermal resistance pass contract diagram, calculate the structural hindrance weight of each thermal resistance connection edge based on the thickness slice attributes, and allocate the effective thermal resistance of the target thermal insulation material to each thermal resistance connection edge according to the proportion of the structural hindrance weight of each thermal resistance connection edge, thereby generating an edge thermal resistance set. Read the edge thermal resistance set sequentially from the hot side boundary to the cold side boundary in the thickness direction. Merge the edges with consecutive edge thermal resistances greater than the average edge thermal resistance into candidate thermal resistance lock chains, and take the candidate thermal resistance lock chain with the largest cumulative edge thermal resistance value as the target thermal resistance lock chain.

8. The method for evaluating the thermal resistance of insulation materials based on digital twins as described in claim 7, characterized in that: The generated thermal resistance evaluation result of the thermal insulation material refers to the extraction of the effective thermal resistance, lateral leakage ratio, edge thermal resistance set and target thermal resistance closed chain of the target thermal insulation material, and then the associated storage with the structural twin of the thermal insulation material.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for evaluating the thermal resistance of thermal insulation materials based on digital twins as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for evaluating the thermal resistance of thermal insulation materials based on digital twins as described in any one of claims 1 to 8.

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