Method, system and equipment for detecting thermophysical performance parameters of ice and snow materials and medium

Abstract

The invention provides an ice and snow material thermophysical performance parameter detection method, system, device and medium, and relates to the technical field of material parameter detection.The method comprises the steps that hardware data of a to-be-detected ice and snow material module is obtained, and the to-be-detected ice and snow material module comprises a to-be-detected ice and snow material, an isolation layer and a heat insulation layer; the ice and snow material to be detected, the isolation layer and the heat insulation layer are connected in sequence; according to a steady-state heat transfer theory and the hardware data, generating a heat insulation layer thickness of the heat insulation layer; according to the steady-state heat transfer theory and the thickness of the thermal insulation layer, thermal physical performance parameters of the thermal insulation layer are generated; and according to the hardware data, the steady-state heat transfer theory, the thickness of the thermal insulation layer and the thermal physical performance parameters of the thermal insulation layer, generating the thermal physical performance parameters of the ice and snow material to be tested. The detection method provided by the invention can simply and efficiently complete the detection of the thermal physical performance parameters of the ice and snow material under the conditions of the detection equipment and the hot room temperature of the detection equipment which meet the national standard.

Description

Technical Field

[0001] The present invention relates to the technical field of material parameter detection, and in particular to a method, system, equipment and medium for detecting the thermophysical property parameters of ice and snow materials. Background Art

[0002] Ice shell buildings are a type of building constructed using ice and snow materials in cold regions. They are used as venues for human activities in cold regions, such as exhibitions, performances, bars and restaurants, etc. They have great market value in cultural heritage and the tourism industry.

[0003] In the existing technology, the design and construction of ice shell buildings are mostly focused on the building mechanics structure, with less research and testing on the thermal physics of materials. In addition, the existing detection equipment and methods require that the cold side ambient temperature of the material is below 0°C and the hot side ambient temperature is above 0°C in order to detect the thermal physical performance parameters of the material. As for the ice and snow materials of ice shell buildings, due to their own particularity, they can only exist in a relatively low temperature environment below 0°C. When the temperature is above 0°C, the ice and snow materials will melt, thereby affecting the detection accuracy of the thermal physical performance parameters of the ice and snow materials. Therefore, the existing detection equipment and methods cannot obtain accurate thermal physical performance parameters, resulting in the internal thermal environment design of the ice shell building having no basis, no technical support, and the expansion of internal functions is also greatly restricted. Summary of the Invention

[0004] The problem solved by the present invention is how to improve the detection accuracy of the thermophysical performance parameters of ice and snow materials and obtain accurate thermophysical performance parameters.

[0005] To solve the above problems, in a first aspect, the present invention provides a method for detecting thermophysical performance parameters of ice and snow materials, comprising:

[0006] Acquiring hardware data of an ice and snow material module to be tested, wherein the ice and snow material module to be tested includes an ice and snow material to be tested, an isolation layer, and a thermal insulation layer, wherein the ice and snow material to be tested, the isolation layer, and the thermal insulation layer are sequentially connected;

[0007] generating a thickness of the thermal insulation layer according to steady-state heat transfer theory and the hardware data;

[0008] generating thermal physical performance parameters of the insulation layer according to the steady-state heat transfer theory and the thickness of the insulation layer;

[0009] The thermophysical performance parameters of the ice and snow material to be tested are generated according to the hardware data, the steady-state heat transfer theory, the thickness of the thermal insulation layer and the thermophysical performance parameters of the thermal insulation layer.

[0010] Optionally, generating the thickness of the thermal insulation layer according to the steady-state heat transfer theory and the hardware data includes:

[0011] Based on the steady-state heat transfer theory, a thickness formula group is constructed according to the hardware data, wherein the hardware data includes the isolation layer thickness of the isolation layer, the isolation layer thermal conductivity of the isolation layer, and the first insulation layer thermal conductivity of the insulation layer;

[0012] The thickness formula group includes:

[0013]

[0014] Among them, d 隔离 is the thickness of the isolation layer, λ 隔离 is the thermal conductivity of the isolation layer, d 绝热 is the thickness of the thermal insulation layer, λ 绝热.0 is the first thermal insulation layer thermal conductivity of the thermal insulation layer, t0 is the hot side surface temperature of the ice and snow material to be measured, t1 is the hot side surface temperature of the thermal insulation layer, t2 is the cold side surface temperature of the ice and snow material to be measured, and q0 is the first heat flux density;

[0015] generating a thickness range of the thermal insulation layer according to the thickness formula group;

[0016] The thickness of the thermal insulation layer is determined according to the thermal insulation layer thickness range and preset rules.

[0017] Optionally, the thermal physical performance parameters of the thermal insulation layer include thermal resistance of the thermal insulation layer and thermal conductivity of the second thermal insulation layer. Generating the thermal physical performance parameters of the thermal insulation layer according to the steady-state heat transfer theory and the thickness of the thermal insulation layer includes:

[0018] A first detection environment is established using a wall heat transfer coefficient detection system and a first preset environmental condition, wherein the first detection environment includes a second heat flux density, a hot side surface temperature of the insulation layer, and a cold side surface temperature of the insulation layer;

[0019] Based on the steady-state heat transfer theory, a heat conduction formula for the heat insulating layer is constructed according to the first detection environment and the thickness of the heat insulating layer. The heat conduction formula for the heat insulating layer includes:

[0020]

[0021] Among them, R 绝热 is the thermal resistance of the insulation layer, d 绝热 is the thickness of the thermal insulation layer, λ 绝热.1 is the thermal conductivity of the second insulation layer, t1 is the hot side surface temperature of the insulation layer, t3 is the cold side surface temperature of the insulation layer, and q1 is the second heat flux density;

[0022] The thermal physical performance parameters of the thermal insulation layer are generated according to the thermal conductivity formula of the thermal insulation layer.

[0023] Optionally, the thermophysical performance parameters of the ice and snow material include thermal resistance of the ice and snow material, thermal conductivity of the ice and snow material, and heat transfer coefficient of the outer surface of the ice and snow material. Generating the thermophysical performance parameters of the ice and snow material to be tested based on the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer, and the thermophysical performance parameters of the insulation layer includes:

[0024] A second testing environment is constructed using the wall heat transfer coefficient detection system and the second preset environmental conditions;

[0025] generating the thermal resistance and thermal conductivity of the ice and snow material according to the second detection environment, the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer, and the thermophysical performance parameters of the insulation layer;

[0026] The heat transfer coefficient of the outer surface of the ice and snow material is generated according to the second detection environment.

[0027] Optionally, generating the thermal resistance and thermal conductivity of the ice and snow material according to the second detection environment, the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer, and the thermophysical performance parameters of the insulation layer includes:

[0028] Based on the steady-state heat transfer theory, a thermal resistance formula for the ice and snow material is constructed according to the second detection environment, the hardware data, the thickness of the insulation layer, and the thermophysical performance parameters of the insulation layer, wherein the second detection environment includes a third heat flux density, the hot-side surface temperature of the insulation layer, and the cold-side surface temperature of the ice and snow material to be tested; and the hardware data includes the thermal resistance of the insulation layer, the material thickness of the ice and snow material to be tested, the insulation layer thickness of the insulation layer, and the insulation layer thermal conductivity of the insulation layer.

[0029] The thermal resistance formula of ice and snow materials includes:

[0030]

[0031] Among them, R 待测 is the thermal resistance of the ice and snow material, R 隔离 is the thermal resistance of the isolation layer, R 绝热 is the thermal resistance of the insulation layer, d 待测 is the material thickness of the ice and snow material to be tested, λ 待测 is the thermal conductivity of the ice and snow material, d 隔离 is the thickness of the isolation layer, λ 隔离 is the thermal conductivity of the isolation layer, d 绝热 is the thickness of the thermal insulation layer, λ 绝热.1is the thermal conductivity of the second thermal insulation layer of the thermal insulation layer, t1 is the hot side surface temperature of the thermal insulation layer, t2 is the cold side surface temperature of the ice and snow material to be tested, and q2 is the third heat flux density;

[0032] The thermal resistance of the ice and snow material and the thermal conductivity of the ice and snow material are generated according to the thermal resistance formula of the ice and snow material.

[0033] Optionally, generating the heat transfer coefficient of the outer surface of the ice and snow material according to the second detection environment includes:

[0034] Constructing a heat transfer coefficient formula for the outer surface of the ice and snow material according to the second detection environment, wherein the second detection environment includes a third heat flux density, a cold side surface temperature of the ice and snow material to be tested, and a cold side ambient temperature of the ice and snow material to be tested;

[0035] The heat transfer coefficient formula of the outer surface of the ice and snow material includes:

[0036]

[0037] Wherein, α is the heat transfer coefficient of the outer surface of the ice and snow material, t2 is the surface temperature of the cold side of the ice and snow material to be measured, t4 is the ambient temperature of the cold side of the ice and snow material to be measured, and q2 is the third heat flux density;

[0038] The heat transfer coefficient of the outer surface of the ice and snow material is generated according to the heat transfer coefficient formula of the outer surface of the ice and snow material.

[0039] Optionally, according to the thickness range of the thermal insulation layer and a preset rule, the thickness of the thermal insulation layer includes:

[0040] The minimum value of the insulation layer thickness range is selected as the insulation layer thickness.

[0041] In a second aspect, the present invention provides a system for detecting thermophysical performance parameters of ice and snow materials, comprising:

[0042] an acquisition module, configured to acquire hardware data of a test ice and snow material module, wherein the test ice and snow material module comprises the test ice and snow material, an isolation layer, and a thermal insulation layer, wherein the test ice and snow material, the isolation layer, and the thermal insulation layer are sequentially connected;

[0043] A thickness module, for generating the thickness of the insulation layer according to steady-state heat transfer theory and the hardware data;

[0044] an insulation layer parameter module, for generating thermal physical performance parameters of the insulation layer according to the steady-state heat transfer theory and the thickness of the insulation layer;

[0045] The material parameter module is used to generate the thermophysical performance parameters of the ice and snow material to be tested based on the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer and the thermophysical performance parameters of the insulation layer.

[0046] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for detecting the thermal physical performance parameters of ice and snow materials as described above is implemented.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for detecting the thermophysical performance parameters of ice and snow materials as described above is implemented.

[0048] The beneficial effects of the method, system, equipment and medium for detecting the thermophysical performance parameters of ice and snow materials of the present invention are:

[0049] The thermal insulation layer of the module of ice and snow materials to be tested can prevent the problem of melting of the ice and snow materials to be tested due to the temperature of one side of the ice and snow materials being above 0°C during testing. As a result, the module of ice and snow materials to be tested can simply and efficiently complete the thermal physical performance parameter testing of ice and snow materials under the conditions of testing equipment that meets national standards, such as wall heat transfer coefficient testing systems and other testing equipment, and the room temperature of the thermal chamber of the testing equipment. It has extremely high applicability and can obtain high-precision test results. The isolation layer of the module of ice and snow materials to be tested can isolate the ice and snow materials to be tested and the thermal insulation layer, so as to prevent the thermal insulation layer from being damp due to the characteristics of the ice and snow materials to be tested, thereby affecting the insulation of the insulation board. Thermal effect improves the accuracy of the detection of thermal physical performance parameters of ice and snow materials; since a larger insulation board thickness will cause the hot side surface temperature of the ice and snow material to be tested to be too low, which is not conducive to the detection of the thermal physical performance parameters of ice and snow materials, the optimal insulation layer thickness is obtained through the steady-state heat transfer theory and the hardware data of the ice and snow material module to be tested, so that the insulation layer can better reduce and control the hot side surface temperature of the ice and snow material to be tested; and then the steady-state heat transfer theory and the insulation layer thickness are used to generate the thermal physical performance parameters of the insulation layer, so that finally through the hardware data, steady-state heat transfer theory, insulation layer thickness and insulation layer thermal physical performance parameters, accurate ice and snow material thermal physical performance parameters of the ice and snow material to be tested are obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic flow chart of a method for detecting thermophysical performance parameters of ice and snow materials provided by an embodiment of the present invention;

[0051] Figure 2 A schematic structural diagram of an ice and snow material module to be tested provided in an embodiment of the present invention;

[0052] Figure 3 This is a structural diagram of the ice and snow material thermophysical performance parameter detection system provided by an embodiment of the present invention.

[0053] Description of reference numerals:

[0054] 1- Ice and snow material to be tested, 2- Isolation layer, 3- Thermal insulation layer. DETAILED DESCRIPTION

[0055] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0056] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0057] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0058] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0059] In the existing technology, the methods for detecting the thermophysical performance parameters of materials mainly include four methods: large-scale equipment detection, on-site testing, instrument detection, and inference estimation.

[0060] Large-scale equipment testing refers to the use of wall heat transfer coefficient testing systems that comply with national standards, such as the "Determination of Steady-State Heat Transfer Properties of Adiabatic Materials - Calibration and Guarded Hot Box Method" (GBT 13475) or the "Heat Flow Meter Method for Determination of Steady-State Thermal Resistance and Related Properties of Insulating Materials" (GBT 10295), to test the heat transfer coefficient and surface heat transfer coefficient of the material. After the test is completed, the thermal conductivity of the tested material can be derived based on the steady-state heat transfer theory for use in building thermal engineering and thermal environment design. However, since the national standards are aimed at conventional building materials and do not take into account the special circumstances of ice and snow materials, and since the temperature on the hot chamber or hot box side of such equipment is higher than 0°C, the conditions for testing ice and snow materials are not met. In addition, the cost of developing equipment for testing ice and snow materials is high, and the input-output ratio is relatively low. It is not feasible to modify or develop new testing equipment specifically for the testing of thermal physical performance parameters of ice and snow materials.

[0061] On-site testing involves measuring the thermophysical properties of ice and snow materials used in ice shell construction on-site. However, on-site testing is inevitably subject to numerous uncontrollable factors, resulting in low accuracy. More importantly, on-site testing is subject to diurnal fluctuations in ambient temperature, making it unstable. Such fluctuations are considered unsteady-state heat transfer in heat transfer theory. However, these on-site tests typically use a relatively long timeframe (typically over 96 hours) and perform averaging, approximating a non-steady-state heat transfer problem to a steady-state one. This inherently introduces significant bias. Furthermore, since ice and snow materials only exist in winter and last for a short period, periods with minimal ambient temperature fluctuations are often difficult to find, further exacerbating these fluctuations. In this case, the bias from approximating unsteady conditions to steady-state conditions is even more significant. Therefore, the accuracy of thermophysical properties such as thermal conductivity obtained through on-site testing is generally low.

[0062] Instrumental testing refers to the detection of thermal conductivity based on the principle of dynamic heat transfer, using instruments such as the Hot Disk thermal conductivity tester, which has high accuracy. However, ice and snow materials are mixtures. For example, composite ice materials made by mixing, stirring, and pouring paper fibers and water in a certain proportion are difficult to process into "homogeneous" materials within a small scale. In theory, thermal conductivity testers are not suitable for testing the thermal conductivity of building materials that are a mixture of multiple components, that is, they are not suitable for non-uniform materials such as ice and snow materials. On the other hand, when using various thermal conductivity testers to test the thermal conductivity of ice and snow materials, it is necessary to ensure that the ambient temperature is below 0°C. Compared with ensuring that the ambient temperature is above 0°C, ensuring the ambient temperature is below 0°C is more expensive. In summary, instrumental testing is not suitable for testing the thermal physical performance parameters of ice and snow materials.

[0063] Inferred estimation refers to estimating based on existing data on the material. For example, the thermal conductivity, heat capacity, and other thermophysical parameters of pure ice can be obtained by consulting literature. The thermal conductivity of composite ice formed by adding materials such as paper fibers and sawdust to water can also be estimated based on existing data. However, due to the complex structure of the mixture, the accuracy of the estimate is limited. The thermophysical properties of ice and snow materials, in addition to being related to the snow formation process, are also closely related to the density of the compressed material. Therefore, parameters such as thermal conductivity of ice and snow materials are often extrapolated based on density, but this bias in the results remains difficult to eliminate.

[0064] like Figure 1 As shown, an embodiment of the present invention provides a method for detecting thermophysical performance parameters of ice and snow materials, comprising:

[0065] S1, obtaining hardware data of an ice and snow material module to be tested, wherein the ice and snow material module to be tested comprises an ice and snow material 1 to be tested, an isolation layer 2 and an insulation layer 3, and the ice and snow material 1 to be tested, the isolation layer 2 and the insulation layer 3 are connected in sequence.

[0066] Specifically, ice and snow materials refer to materials used to build ice shell buildings, such as composite ice materials. The method for detecting the thermophysical performance parameters of ice and snow materials of the present invention is to build a detection environment through existing detection equipment such as a wall heat transfer coefficient detection system to complete the detection of the thermophysical performance parameters of ice and snow materials. Figure 2 The ice and snow material module to be tested shown includes the ice and snow material to be tested 1, an isolation layer 2 and an insulation layer 3. The cross-sectional shapes of the ice and snow material to be tested 1, the isolation layer 2 and the insulation layer 3 are adapted to each other. The isolation layer 2 is used to isolate the ice and snow material to be tested 1 and the insulation layer 3 to prevent the insulation layer 3 from getting damp due to the ice and snow material to be tested 1. The insulation layer 3 is used to reduce and control the hot side surface temperature of the ice and snow material to be tested to ensure that the temperature is below 0°C, thereby completing the detection.

[0067] For example, the detection environment includes a cold room temperature to simulate outdoor temperature conditions, which can be set to about -15°C, and a hot room temperature to simulate indoor temperature conditions, which can be set to about 20°C. The isolation layer 2 can be made of a material with sufficient strength and a large thermal conductivity coefficient, such as a steel plate, so that the isolation layer 2 can bear the load of the ice and snow material 1 to be tested, and is convenient for pouring and processing into the ice and snow material module to be tested. At the same time, a large thermal conductivity coefficient means strong thermal conductivity, which has little effect on parameter detection and is conducive to improving detection accuracy. The insulation layer 3 can also be a thermal insulation layer. The insulation layer 3 can be made of materials with low compression rate, low water absorption rate, smooth appearance and other characteristics, such as extruded board, polystyrene board, polyurethane board, rock wool board, ceramic thermal insulation material and silicate thermal insulation material.

[0068] In one embodiment, before obtaining the hardware data of the ice and snow material module to be tested, the method further includes:

[0069] Obtain the ice and snow material 1 to be tested, the isolation layer 2 and the thermal insulation layer 3;

[0070] The ice and snow material 1 to be tested is poured or pressed on one side of the isolation layer 2, and the thermal insulation layer 3 is arranged on the other side of the isolation layer 2 to construct the ice and snow material module to be tested.

[0071] Specifically, if winter outdoor temperature conditions permit, the ice and snow material 1 to be tested can be poured or pressed outdoors onto one side of the isolation layer 2. The dimensions of the ice and snow material 1, isolation layer 2, and insulation layer 3 can be set according to actual conditions, with the length and width of the ice and snow material 1 to be tested being no less than 10 times its thickness. This simplifies steady-state heat transfer into a one-dimensional heat transfer problem, facilitating subsequent calculations. For example, the thickness of the isolation layer 2 can be set to 5 mm, and the thickness of the ice and snow material 1 can be set to 100 mm.

[0072] It should be noted that the wall heat transfer coefficient detection system refers to testing equipment that complies with national standards. Any similar testing equipment can be used in the present method for testing the thermophysical performance parameters of ice and snow materials, without limitation. National standards include "GBT 13475: Determination and calibration of steady-state heat transfer properties of insulation and the guarded hot box method" and "GBT 10295: Determination of steady-state thermal resistance and related properties of insulation materials by heat flow meter method."

[0073] S2, generating the thickness of the thermal insulation layer 3 according to the steady-state heat transfer theory and the hardware data.

[0074] Exemplarily, the hardware data includes the insulation layer thickness of the insulation layer 2 , the insulation layer thermal conductivity of the insulation layer 2 , and the first insulation layer thermal conductivity of the insulation layer 3 .

[0075] In one embodiment, generating the thickness of the thermal insulation layer 3 according to the steady-state heat transfer theory and the hardware data includes:

[0076] Based on the steady-state heat transfer theory, a thickness formula group is constructed according to the hardware data, wherein the hardware data includes the insulation layer thickness of the insulation layer 2, the insulation layer thermal conductivity of the insulation layer 2, and the thermal conductivity of the first insulation layer of the insulation layer 3;

[0077] The thickness formula group includes:

[0078]

[0079] Among them, d 隔离 is the thickness of the isolation layer 2, λ 隔离 is the thermal conductivity of the isolation layer 2, d 绝热 is the thickness of the thermal insulation layer, λ绝热.0 is the thermal conductivity of the first thermal insulation layer 3, t0 is the hot side surface temperature of the ice and snow material 1 to be tested, t1 is the hot side surface temperature of the thermal insulation layer 3, t2 is the cold side surface temperature of the ice and snow material 1 to be tested, and q0 is the first heat flux density;

[0080] generating a thickness range of the thermal insulation layer according to the thickness formula group;

[0081] The thickness of the thermal insulation layer is determined according to the thermal insulation layer thickness range and preset rules.

[0082] Specifically, the hardware data refers to the dimensions and correlation coefficients of the ice and snow material 1, isolation layer 2, and thermal insulation layer 3 to be tested. Steady-state heat transfer theory states that the total thermal resistance of a multilayer material is equal to the sum of the thermal resistances of each layer. Based on this steady-state heat transfer theory, a set of thermal conductivity relationship formulas for the ice and snow material module to be tested can be generated. These thermal conductivity relationship formulas include:

[0083]

[0084] By combining the three formulas in the thermal conductivity relationship formula group, we can get the thickness formula group.

[0085] Among them, R 待测 is the thermal resistance of the ice and snow material 1 to be tested, R 隔离 is the thermal resistance of the isolation layer, R 绝热 is the thermal resistance of the thermal insulation layer 3, d 待测 is the material thickness of the ice and snow material 1 to be tested, λ 预估 is the estimated thermal conductivity of the ice and snow material 1 to be tested, d 隔离 is the thickness of the isolation layer, λ 隔离 is the thermal conductivity of the isolation layer, d 绝热 is the thickness of the thermal insulation layer, λ 绝热.0 is the thermal conductivity of the first insulation layer, t0 is the hot side surface temperature of the ice and snow material 1 to be tested, t1 is the hot side surface temperature of the insulation layer 3, t2 is the cold side surface temperature of the ice and snow material 1 to be tested, and q0 is the first heat flux density; where λ 预估 The actual data should be unknown, but it is an estimated value used to calculate the optimal insulation layer thickness. 隔离 、R 绝热 d 待测 d 隔离 ,λ 隔离 ,λ 绝热.0 , t1, t2 and q0 can all be obtained by searching existing data or observation. t0 is the surface temperature of the hot side of the ice and snow material 1 to be tested, which is unknown. Figure 2As shown, t0 is also the temperature between the ice and snow material 1 to be tested and the isolation layer 2. In order to prevent the ice and snow material 1 to be tested from melting, t0 has a value range, that is, t0 is less than or equal to 0°C. At the same time, considering other uncertainty factors, the present invention sets t0≤-5.

[0086] In one embodiment, according to the thickness range of the thermal insulation layer and a preset rule, the thickness of the thermal insulation layer includes:

[0087] The minimum value of the insulation layer thickness range is selected as the insulation layer thickness.

[0088] Specifically, according to the thickness formula group, the thickness range of the insulation layer is:

[0089]

[0090] According to the thickness formula group, when the insulation layer thickness d 绝热 When the thickness of the insulation layer d is greater, the hot side surface temperature t0 of the ice and snow material 1 to be tested is smaller. On the contrary, when the thickness of the insulation layer d is greater, the hot side surface temperature t0 of the ice and snow material 1 to be tested is smaller. 绝热 The smaller the value, the greater the hot side surface temperature t0 of the ice and snow material 1 to be tested. Since the present invention first sets the value range of t0, that is, limits the maximum value of t0, the thickness of the insulation layer corresponds to the minimum value of the insulation layer thickness range.

[0091] S3, generating thermal physical performance parameters of the thermal insulation layer according to the steady-state heat transfer theory and the thickness of the thermal insulation layer.

[0092] Specifically, the steady-state heat transfer theory means that the "total thermal resistance of a multilayer material is equal to the sum of the thermal resistances of each layer of material." By using the steady-state heat transfer theory and the thickness of the insulation layer in the wall heat transfer coefficient detection system, the thermal physical performance parameters of the insulation layer can be obtained.

[0093] In one embodiment, the thermal physical performance parameters of the thermal insulation layer include the thermal resistance of the thermal insulation layer and the thermal conductivity of the second thermal insulation layer. The thermal physical performance parameters of the thermal insulation layer are generated according to the steady-state heat transfer theory and the thickness of the thermal insulation layer, including:

[0094] A first detection environment is constructed using a wall heat transfer coefficient detection system and a first preset environmental condition, wherein the first detection environment includes a second heat flux density, a hot side surface temperature of the insulation layer (3), and a cold side surface temperature of the insulation layer (3);

[0095] Based on the steady-state heat transfer theory, a heat conduction formula for the heat insulating layer is constructed according to the first detection environment and the thickness of the heat insulating layer. The heat conduction formula for the heat insulating layer includes:

[0096]

[0097] Among them, R 绝热is the thermal resistance of the insulation layer, d 绝热 is the thickness of the thermal insulation layer, λ 绝热.1 is the thermal conductivity of the second insulation layer, t1 is the hot side surface temperature of the insulation layer 3, t3 is the cold side surface temperature of the insulation layer 3, and q1 is the second heat flux density;

[0098] The thermal physical performance parameters of the thermal insulation layer are generated according to the thermal conductivity formula of the thermal insulation layer.

[0099] Specifically, before testing the thermophysical performance parameters of the ice and snow material 1 to be tested, the thermophysical performance parameters of the insulation layer 3 in the ice and snow material module to be tested, namely the thermal resistance of the insulation layer and the thermal conductivity of the second insulation layer, are retested and used as input for the thermophysical performance parameter testing of the ice and snow material to improve testing accuracy. The first preset environmental conditions include a cold room temperature simulating outdoor temperature conditions and a hot room temperature simulating indoor temperature conditions; the first test environment constructed includes a cold room temperature set to -15°C and a hot room temperature set to 20°C. This first test environment allows the acquisition of data for t1, t3, and q1.

[0100] S4, generating the thermophysical performance parameters of the ice and snow material 1 to be tested according to the hardware data, the steady-state heat transfer theory, the thickness of the thermal insulation layer and the thermophysical performance parameters of the thermal insulation layer.

[0101] Specifically, in the wall heat transfer coefficient detection system, based on the steady-state heat transfer theory and through the hardware data insulation layer thickness and insulation layer thermophysical performance parameters, the thermophysical performance parameters of the ice and snow material can be obtained.

[0102] The present invention utilizes a thermal insulation layer on the ice and snow material module to prevent the material from melting due to temperatures above 0°C on one side of the material during testing. This allows the module to simply and efficiently complete testing of the thermophysical properties of the material under testing equipment that meets national standards and at room temperature in the thermal chamber of the testing equipment. This method is highly applicable and can produce highly accurate test results. Furthermore, the isolation layer of the ice and snow material module isolates the material from the thermal insulation layer, preventing the thermal insulation layer from becoming damp due to the characteristics of the material and affecting the insulation effect of the insulation board. This improves the accuracy of testing the thermophysical properties of the material. This method addresses the existing problems of large-scale equipment testing, on-site testing, instrument testing, and inferred estimation, which are characterized by an inability to detect ice and snow, high cost, and low accuracy.

[0103] In one embodiment, the thermophysical performance parameters of the ice and snow material include the thermal resistance of the ice and snow material, the thermal conductivity of the ice and snow material, and the heat transfer coefficient of the outer surface of the ice and snow material. The thermophysical performance parameters of the ice and snow material to be tested 1 are generated based on the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer, and the thermophysical performance parameters of the insulation layer, including:

[0104] A second testing environment is constructed using the wall heat transfer coefficient detection system and the second preset environmental conditions;

[0105] generating the thermal resistance and thermal conductivity of the ice and snow material according to the second detection environment, the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer, and the thermophysical performance parameters of the insulation layer;

[0106] The heat transfer coefficient of the outer surface of the ice and snow material is generated according to the second detection environment.

[0107] Specifically, a second testing environment was constructed using a wall heat transfer coefficient testing system. The second preset environmental conditions included a cold room temperature simulating outdoor conditions and a hot room temperature simulating indoor conditions. The second testing environment was set to -15°C for the cold room and 20°C for the hot room. Based on the obtained data and steady-state heat transfer theory, the thermophysical performance parameters of the ice and snow material were obtained.

[0108] Optionally, generating the thermal resistance and thermal conductivity of the ice and snow material according to the second detection environment, the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer, and the thermophysical performance parameters of the insulation layer includes:

[0109] Based on the steady-state heat transfer theory, a thermal resistance formula for the ice and snow material is constructed according to the second detection environment, the hardware data, the thickness of the insulation layer, and the thermophysical performance parameters of the insulation layer, wherein the second detection environment includes the third heat flux density, the hot side surface temperature of the insulation layer 3, and the cold side surface temperature of the ice and snow material 1 to be tested; the hardware data includes the thermal resistance of the insulation layer, the material thickness of the ice and snow material 1 to be tested, the insulation layer thickness of the insulation layer 2, and the insulation layer thermal conductivity of the insulation layer 2;

[0110] The thermal resistance formula of ice and snow materials includes:

[0111]

[0112] Among them, R 待测 is the thermal resistance of the ice and snow material, R 隔离 is the thermal resistance of the isolation layer, R 绝热 is the thermal resistance of the insulation layer, d 待测 is the material thickness of the ice and snow material 1 to be tested, λ 待测is the thermal conductivity of the ice and snow material, d 隔离 is the thickness of the isolation layer 2, λ 隔离 is the thermal conductivity of the isolation layer 2, d 绝热 is the thickness of the thermal insulation layer, λ 绝热.1 is the thermal conductivity of the second insulation layer of the insulation layer 3, t1 is the hot side surface temperature of the insulation layer 3, t2 is the cold side surface temperature of the ice and snow material 1 to be tested, and q2 is the third heat flux density;

[0113] The thermal resistance of the ice and snow material and the thermal conductivity of the ice and snow material are generated according to the thermal resistance formula of the ice and snow material.

[0114] Specifically, based on the steady-state heat transfer theory of multilayer materials, "the total thermal resistance is equal to the sum of the thermal resistances of each layer of material" and various data, a thermal resistance formula for ice and snow materials can be constructed. By substituting various known and tested data into the thermal resistance formula for ice and snow materials, the thermal resistance and thermal conductivity of ice and snow materials can be solved.

[0115] Optionally, generating the heat transfer coefficient of the outer surface of the ice and snow material according to the second detection environment includes:

[0116] According to the second detection environment, a heat transfer coefficient formula for the outer surface of the ice and snow material is constructed, wherein the second detection environment includes a third heat flux density, the cold side surface temperature of the ice and snow material 1 to be tested, and the cold side environment temperature of the ice and snow material 1 to be tested;

[0117] The heat transfer coefficient formula of the outer surface of the ice and snow material includes:

[0118]

[0119] Wherein, α is the heat transfer coefficient of the outer surface of the ice and snow material, t2 is the surface temperature of the cold side of the ice and snow material 1 to be tested, t4 is the ambient temperature of the cold side of the ice and snow material 1 to be tested, and q2 is the third heat flux density;

[0120] The heat transfer coefficient of the outer surface of the ice and snow material is generated according to the heat transfer coefficient formula of the outer surface of the ice and snow material.

[0121] Specifically, based on the steady-state heat transfer theory and the second detection environment, a formula for the heat transfer coefficient of the outer surface of ice and snow materials is constructed. By substituting various known and detected data into the formula for the heat transfer coefficient of the outer surface of ice and snow materials, the heat transfer coefficient of the outer surface of ice and snow materials can be solved.

[0122] like Figure 3 As shown, another embodiment of the present invention provides a system for detecting thermophysical performance parameters of ice and snow materials, comprising:

[0123] An acquisition module is used to acquire hardware data of an ice and snow material module to be tested, wherein the ice and snow material module to be tested includes an ice and snow material 1 to be tested, an isolation layer 2, and an insulation layer 3, wherein the ice and snow material 1 to be tested, the isolation layer 2, and the insulation layer 3 are connected in sequence;

[0124] A thickness module, for generating the thickness of the insulation layer 3 according to the steady-state heat transfer theory and the hardware data;

[0125] an insulation layer parameter module, for generating thermal physical performance parameters of the insulation layer according to the steady-state heat transfer theory and the thickness of the insulation layer;

[0126] The material parameter module is used to generate the thermophysical performance parameters of the ice and snow material 1 to be tested based on the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer and the thermophysical performance parameters of the insulation layer.

[0127] Another embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for detecting the thermal physical performance parameters of ice and snow materials as described above is implemented.

[0128] Yet another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned method for detecting the thermophysical performance parameters of ice and snow materials is implemented.

[0129] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A method for detecting the thermophysical performance parameters of ice and snow materials, characterized in that: include: Acquiring hardware data of an ice and snow material module to be tested, wherein the ice and snow material module to be tested comprises an ice and snow material to be tested (1), an isolation layer (2), and a heat-insulating layer (3), wherein the ice and snow material to be tested (1), the isolation layer (2), and the heat-insulating layer (3) are connected in sequence; Generating the thickness of the thermal insulation layer (3) according to the steady-state heat transfer theory and the hardware data; generating thermal physical performance parameters of the insulation layer according to the steady-state heat transfer theory and the thickness of the insulation layer; The thermophysical performance parameters of the ice and snow material (1) to be tested are generated according to the hardware data, the steady-state heat transfer theory, the thickness of the thermal insulation layer and the thermophysical performance parameters of the thermal insulation layer.

2. The method for detecting thermophysical performance parameters of ice and snow materials according to claim 1, characterized in that: The step of generating the thickness of the thermal insulation layer (3) according to the steady-state heat transfer theory and the hardware data comprises: Based on the steady-state heat transfer theory, a thickness formula group is constructed according to the hardware data, wherein the hardware data includes the insulation layer thickness of the insulation layer (2), the insulation layer thermal conductivity of the insulation layer (2), and the first insulation layer thermal conductivity of the insulation layer (3); The thickness formula group includes: Among them, d 隔离 is the thickness of the isolation layer (2), λ 隔离 is the thermal conductivity of the isolation layer (2), d 绝热 is the thickness of the thermal insulation layer, λ 绝热.0 is the first thermal insulation layer thermal conductivity of the thermal insulation layer (3), t0 is the hot side surface temperature of the ice and snow material (1) to be measured, t1 is the hot side surface temperature of the thermal insulation layer (3), t2 is the cold side surface temperature of the ice and snow material (1) to be measured, and q0 is the first heat flux density; generating a thickness range of the thermal insulation layer according to the thickness formula group; The thickness of the thermal insulation layer is determined according to the thermal insulation layer thickness range and preset rules.

3. The method for detecting thermophysical performance parameters of ice and snow materials according to claim 1, characterized in that: The thermal physical performance parameters of the thermal insulation layer include the thermal resistance of the thermal insulation layer and the thermal conductivity of the second thermal insulation layer. The thermal physical performance parameters of the thermal insulation layer are generated according to the steady-state heat transfer theory and the thickness of the thermal insulation layer, including: A first detection environment is constructed using a wall heat transfer coefficient detection system and a first preset environmental condition, wherein the first detection environment includes a second heat flux density, a hot side surface temperature of the insulation layer (3), and a cold side surface temperature of the insulation layer (3); Based on the steady-state heat transfer theory, a heat conduction formula for the heat insulating layer is constructed according to the first detection environment and the thickness of the heat insulating layer. The heat conduction formula for the heat insulating layer includes: Among them, R 绝热 is the thermal resistance of the insulation layer, d 绝热 is the thickness of the thermal insulation layer, λ 绝热.1 is the thermal conductivity of the second thermal insulation layer, t1 is the hot side surface temperature of the thermal insulation layer (3), t3 is the cold side surface temperature of the thermal insulation layer (3), and q1 is the second heat flux density; The thermal physical performance parameters of the thermal insulation layer are generated according to the thermal conductivity formula of the thermal insulation layer.

4. The method for detecting thermophysical performance parameters of ice and snow materials according to claim 1, characterized in that: The thermophysical performance parameters of the ice and snow material include the thermal resistance of the ice and snow material, the thermal conductivity of the ice and snow material, and the heat transfer coefficient of the outer surface of the ice and snow material. The thermophysical performance parameters of the ice and snow material to be tested (1) are generated based on the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer, and the thermophysical performance parameters of the insulation layer, including: A second testing environment is constructed using the wall heat transfer coefficient detection system and the second preset environmental conditions; generating the thermal resistance and thermal conductivity of the ice and snow material according to the second detection environment, the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer, and the thermophysical performance parameters of the insulation layer; The heat transfer coefficient of the outer surface of the ice and snow material is generated according to the second detection environment.

5. The method for detecting thermophysical performance parameters of ice and snow materials according to claim 4, characterized in that: Generating the thermal resistance and thermal conductivity of the ice and snow material according to the second detection environment, the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer, and the thermophysical performance parameters of the insulation layer includes: Based on the steady-state heat transfer theory, according to the second detection environment, the hardware data, the thickness of the insulation layer and the thermal physical performance parameters of the insulation layer, a thermal resistance formula of the ice and snow material is constructed, wherein the second detection environment includes a third heat flux density, the hot side surface temperature of the insulation layer (3) and the cold side surface temperature of the ice and snow material (1) to be tested; the hardware data includes the thermal resistance of the insulation layer, the material thickness of the ice and snow material (1) to be tested, the insulation layer thickness of the insulation layer (2), and the insulation layer thermal conductivity of the insulation layer (2); The thermal resistance formula of ice and snow materials includes: Among them, R 待测 is the thermal resistance of the ice and snow material, R 隔离 is the thermal resistance of the isolation layer, R 绝热 is the thermal resistance of the insulation layer, d 待测 is the material thickness of the ice and snow material (1) to be tested, λ 待测 is the thermal conductivity of the ice and snow material, d 隔离 is the thickness of the isolation layer (2), λ 隔离 is the thermal conductivity of the isolation layer (2), d 绝热 is the thickness of the thermal insulation layer, λ 绝热.1 is the second thermal insulation layer thermal conductivity of the thermal insulation layer (3), t1 is the hot side surface temperature of the thermal insulation layer (3), t2 is the cold side surface temperature of the ice and snow material (1) to be measured, and q2 is the third heat flux density; The thermal resistance of the ice and snow material and the thermal conductivity of the ice and snow material are generated according to the thermal resistance formula of the ice and snow material.

6. The method for detecting thermophysical performance parameters of ice and snow materials according to claim 4, characterized in that: Generating the heat transfer coefficient of the outer surface of the ice and snow material according to the second detection environment includes: According to the second detection environment, a heat transfer coefficient formula for the outer surface of the ice and snow material is constructed, wherein the second detection environment includes a third heat flux density, the cold side surface temperature of the ice and snow material (1) to be tested, and the cold side environment temperature of the ice and snow material (1) to be tested; The heat transfer coefficient formula of the outer surface of the ice and snow material includes: Wherein, α is the heat transfer coefficient of the outer surface of the ice and snow material, t2 is the surface temperature of the cold side of the ice and snow material (1) to be tested, t4 is the ambient temperature of the cold side of the ice and snow material (1) to be tested, and q2 is the third heat flux density; The heat transfer coefficient of the outer surface of the ice and snow material is generated according to the heat transfer coefficient formula of the outer surface of the ice and snow material.

7. The method for detecting thermophysical performance parameters of ice and snow materials according to claim 1, characterized in that: According to the thickness range of the thermal insulation layer and the preset rules, the thickness of the thermal insulation layer includes: The minimum value of the insulation layer thickness range is selected as the insulation layer thickness.

8. A system for detecting the thermal physical properties of ice and snow materials, characterized in that: include: An acquisition module is used to acquire hardware data of an ice and snow material module to be tested, wherein the ice and snow material module to be tested comprises an ice and snow material to be tested (1), an isolation layer (2), and an insulation layer (3), wherein the ice and snow material to be tested (1), the isolation layer (2), and the insulation layer (3) are connected in sequence; A thickness module, for generating the thickness of the thermal insulation layer (3) according to the steady-state heat transfer theory and the hardware data; an insulation layer parameter module, for generating thermal physical performance parameters of the insulation layer according to the steady-state heat transfer theory and the thickness of the insulation layer; The material parameter module is used to generate the ice and snow material thermophysical performance parameters of the ice and snow material (1) to be tested based on the hardware data, the steady-state heat transfer theory, the thickness of the insulation layer and the thermophysical performance parameters of the insulation layer.

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. When the processor executes the computer program, the method for detecting the thermophysical performance parameters of ice and snow materials as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, the method for detecting the thermophysical performance parameters of ice and snow materials according to any one of claims 1 to 7 is implemented.

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