Method for evaluating heat transfer protection effect of pipeline temperature insulation coating

Abstract

This disclosure provides a performance evaluation method for the heat transfer protection effect of pipeline insulation coatings. By introducing an equivalent thermal conductivity model based on microporous structure, a more accurate characterization of the initial thermal insulation performance of the coating is achieved, overcoming the calculation bias caused by relying solely on the inherent thermal conductivity of the material. Secondly, by coupling a performance degradation factor, the model possesses dynamic prediction capabilities, accurately simulating the thermal insulation performance degradation law of the coating throughout its entire life cycle, providing crucial data support for pipeline energy efficiency assessment and preventive maintenance. Finally, by defining a comprehensive thermal protection effectiveness index as the optimization target and utilizing intelligent algorithms to back-optimize construction process parameters, this invention elevates traditional "experience-based design" to "precise optimization design based on performance prediction," thereby maximizing material cost savings, reducing energy consumption, and significantly improving the long-term safety and economy of pipeline systems while ensuring thermal protection effectiveness.

Description

Technical Field

[0001] This invention relates to the field of pipeline technology, and in particular to a method for evaluating the performance of thermal insulation coatings for pipelines. Background Technology

[0002] In industrial sectors such as petrochemicals, district heating, and long-distance pipelines, the thermal insulation performance of pipeline systems is a core factor determining their energy efficiency, operational safety, and economy. As a crucial barrier isolating the internal fluids of the pipeline from heat exchange with the external environment, the quality of the thermal protection effect of the insulation coating directly impacts significant energy losses and operating costs. Currently, engineering practice relies heavily on static thermal conductivity data provided by material suppliers for performance evaluation of insulation coatings, and design calculations are based on simplified steady-state heat transfer models. However, the actual thermal conductivity of the coating is significantly affected by its microstructure (such as porosity and filler distribution), and its performance degrades during long-term service due to environmental stresses (such as thermal cycling, humidity, ultraviolet radiation, and chemical corrosion), leading to increased thermal conductivity and decreased insulation effectiveness. This time-varying degradation effect is ignored in traditional static design models, resulting in a significant discrepancy between performance predictions during the design phase and the actual operating energy consumption of the pipeline, posing potential risks to energy management and safe production.

[0003] To address these challenges, existing technologies attempt to reserve safety margins by increasing insulation layer thickness or using higher-performance materials, but this undoubtedly increases initial investment and material costs. Furthermore, some research focuses on accelerated aging tests of coatings, but these tests are time-consuming, costly, and the results are difficult to quantify directly and accurately as long-term impacts on overall pipeline heat loss. Therefore, there is an urgent need in this field for a more precise and dynamic calculation method. This method can not only accurately predict the thermal protection performance of the coating throughout its entire lifespan during the design phase, but also establish a quantitative relationship between coating material composition, construction process parameters, and their long-term insulation performance. This would provide a scientific basis for optimizing coating selection, thickness design, and maintenance strategies, enabling a shift from "extensive safety margin design" to "precise performance-oriented design." Summary of the Invention

[0004] The first aspect of this disclosure provides a method for evaluating the performance of heat transfer protection of pipe insulation coatings, comprising the following steps:

[0005] S1. Obtain pipeline operating parameters, environmental parameters, and basic physical properties of the thermal insulation coating material;

[0006] S2. Based on the aforementioned basic physical property parameters, establish a pipeline heat loss calculation model that considers the equivalent thermal conductivity of the coating microstructure.

[0007] S3. Introduce a coating performance degradation factor based on time-varying conditions to modify the pipeline heat loss calculation model and obtain the pipeline dynamic heat loss rate.

[0008] S4. Define and calculate the comprehensive thermal protection performance index of the thermal insulation coating to quantitatively evaluate its thermal protection effect.

[0009] S5. Taking the comprehensive thermal protection performance index as the optimization target, iterative calculations are performed by adjusting the construction process parameters or structural parameters of the thermal insulation coating. When the index reaches a preset threshold, the optimal coating performance parameters and corresponding process parameters are output.

[0010] In conjunction with the first aspect, in step S2, the calculation model considering the equivalent thermal conductivity of the coating microstructure is implemented through the following formula:

[0011]

[0012] in, The equivalent thermal conductivity of the insulation coating, The inherent thermal conductivity of the coating solid substrate, The volume fraction of porosity in the coating. and The structural correction coefficients, which are related to pore shape and distribution, are obtained by fitting experimental data.

[0013] In conjunction with the first aspect, the calculation model for pipeline heat loss in step S2 is as follows:

[0014] ,

[0015] in, This represents the total heat loss of the pipeline per unit time. For the length of the pipe, and These are the fluid temperature inside the pipe and the ambient temperature, respectively. and These are the inner radius of the pipe and the outermost radius of the outermost insulation layer, respectively. For the first The outer radius of the thermal insulation coating layer and These are the convective heat transfer coefficients inside and outside the pipe, respectively. For the first The equivalent thermal conductivity of the insulation coating.

[0016] In conjunction with the first aspect, the performance degradation factor η(t) in step S3 is calculated by the following formula:

[0017] ,

[0018] in, For coating service life; This is the initial performance factor of the coating. This refers to the residual performance factor after the coating performance has stabilized due to degradation. The dynamic heat loss rate is a degradation rate constant related to the weather resistance of the coating material, expressed as a property degradation factor. This is obtained by multiplying the correction factor into the heat loss calculation model.

[0019] In conjunction with the first aspect, in step S4, the formula for calculating the comprehensive thermal protection effectiveness index Ψ is:

[0020] ,

[0021] in, and The heat loss rates are respectively those of the standard coating and the optimized coating. and The expected service life of the standard coating and the optimized coating are respectively. and Let be the weight coefficient, and satisfy... Its specific value is determined by the economic and safety requirements of the medium being transported in the pipeline.

[0022] In conjunction with the first aspect, the expected service life By degrading the coating performance to a critical threshold The corresponding time The coating lifetime is determined by taking the inverse function of the performance degradation factor equation.

[0023] In conjunction with the first aspect, in step S5, the construction process parameters to be optimized include the coating spraying thickness, the drying and curing temperature profile, and the particle size distribution of functional fillers in the coating system.

[0024] The iterative calculation employs a genetic algorithm or a particle swarm optimization algorithm to perform global optimization within a set parameter space.

[0025] In conjunction with the first aspect, the method further includes step S6:

[0026] The optimized coating performance parameters and process parameters are used as inputs to drive the automated spraying equipment to perform the construction of pipeline insulation coating, and the key parameters during the construction process are fed back to the calculation model to achieve closed-loop control.

[0027] A second aspect of this disclosure provides an electronic device, comprising:

[0028] One or more processors;

[0029] A storage unit is used to store one or more programs that, when executed by one or more processors, enable the one or more processors to implement a performance evaluation method for the heat transfer protection effect of the pipe insulation coating.

[0030] A third aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it enables a performance evaluation method for the heat transfer protection effect of the pipe insulation coating.

[0031] Beneficial Effects: This disclosure provides a performance evaluation method for the heat transfer protection effect of pipeline insulation coatings. By introducing an equivalent thermal conductivity model based on microporous structure, it achieves a more accurate characterization of the initial thermal insulation performance of the coating, overcoming the calculation bias caused by relying solely on the inherent thermal conductivity of the material. Secondly, by coupling a performance degradation factor, the model possesses dynamic prediction capabilities, accurately simulating the thermal insulation performance degradation law of the coating throughout its entire life cycle, providing crucial data support for pipeline energy efficiency assessment and preventive maintenance. Finally, by defining a comprehensive thermal protection efficiency index as the optimization target and utilizing intelligent algorithms to back-optimize construction process parameters, this invention elevates traditional "experience-based design" to "precise optimization design based on performance prediction," thereby maximizing material cost savings, reducing energy consumption, and significantly improving the long-term safety and economy of pipeline systems while ensuring thermal protection effectiveness. Attached Figure Description

[0032] Figure 1 This is a schematic flowchart illustrating a method for evaluating the performance of a pipe insulation coating heat transfer protection effect according to an embodiment of the present disclosure.

[0033] Figure 2 An electronic device as described in the disclosed embodiments. Detailed Implementation

[0034] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those disclosed herein.

[0035] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0036] Figure 1 A method for evaluating the heat transfer protection effect of a pipe insulation coating according to an embodiment of this disclosure includes the following steps:

[0037] S1. Obtain pipeline operating parameters, environmental parameters, and basic physical properties of the thermal insulation coating material;

[0038] Step S1 is the basic data acquisition stage, which requires comprehensive acquisition of pipeline operating parameters (such as medium temperature and flow rate), environmental parameters (such as ambient temperature and humidity), and basic physical property parameters of the insulation coating material (such as thermal conductivity and porosity characteristics of the solid substrate). These parameters are the data foundation for all subsequent calculations and analyses, and their accuracy and completeness directly affect the reliability of the final optimization results.

[0039] S2. Based on the aforementioned basic physical property parameters, establish a pipeline heat loss calculation model that considers the equivalent thermal conductivity of the coating microstructure.

[0040] In step S2, the calculation model considering the equivalent thermal conductivity of the coating microstructure is implemented through the following formula:

[0041]

[0042] in, The equivalent thermal conductivity of the insulation coating, The inherent thermal conductivity of the coating solid substrate, The volume fraction of porosity in the coating. and The structural correction coefficients, which are related to pore shape and distribution, are obtained by fitting experimental data.

[0043] In step S2, the model for calculating pipeline heat loss is as follows:

[0044] ,

[0045] in, This represents the total heat loss of the pipeline per unit time. For the length of the pipe, and These are the fluid temperature inside the pipe and the ambient temperature, respectively. and These are the inner radius of the pipe and the outermost radius of the outermost insulation layer, respectively. For the first The outer radius of the thermal insulation coating layer and These are the convective heat transfer coefficients inside and outside the pipe, respectively. For the first The equivalent thermal conductivity of the insulation coating.

[0046] After obtaining the basic parameters, step S2 focuses on establishing the core heat loss calculation model. The core innovation of this model lies in introducing the calculation of equivalent thermal conductivity considering the coating's microstructure. Specifically, this is achieved through the formula...

[0047] ,

[0048] To achieve this, in which The equivalent thermal conductivity of the insulation coating, The inherent thermal conductivity of the coating solid substrate, The volume fraction of porosity in the coating. and The structural correction coefficient, related to pore shape and distribution, was obtained through fitting experimental data. This formula can more accurately characterize the true thermal insulation performance of porous coating materials, overcoming the bias caused by using only the nominal thermal conductivity of the material.

[0049] Subsequently, based on this equivalent thermal conductivity, the total heat loss of the pipeline is constructed. ,

[0050] in, This represents the total heat loss of the pipeline per unit time. For the length of the pipe, and These are the fluid temperature inside the pipe and the ambient temperature, respectively. and These are the inner radius of the pipe and the outermost radius of the outermost insulation layer, respectively. For the first The outer radius of the thermal insulation coating layer and These are the convective heat transfer coefficients inside and outside the pipe, respectively. For the first The equivalent thermal conductivity of the insulation coating.

[0051] This model comprehensively considers pipe size, temperature difference, multi-layer insulation structure, and internal and external convection heat transfer conditions, forming a quantitative tool for evaluating the initial thermal insulation performance of the coating.

[0052] S3. Introduce a coating performance degradation factor based on time-varying conditions to modify the pipeline heat loss calculation model and obtain the pipeline dynamic heat loss rate.

[0053] The performance degradation factor η(t) in step S3 is calculated by the following formula:

[0054] ,

[0055] in, For coating service life; This is the initial performance factor of the coating. This refers to the residual performance factor after the coating performance has stabilized due to degradation. The dynamic heat loss rate is a degradation rate constant related to the weather resistance of the coating material, expressed as a property degradation factor. This is obtained by multiplying the correction factor into the heat loss calculation model.

[0056] To upgrade the static model to a dynamic model that can reflect long-term performance, step S3 introduces a coating performance degradation factor based on time-varying conditions:

[0057] ,

[0058] in, For coating service life; This is the initial performance factor of the coating. This refers to the residual performance factor after the coating performance has stabilized due to degradation. The dynamic heat loss rate is a degradation rate constant related to the weather resistance of the coating material, expressed as a property degradation factor. This is obtained by multiplying the correction factor into the heat loss calculation model.

[0059] By multiplying this degradation factor as a correction coefficient into the heat loss calculation model in step S2, the dynamic heat loss rate of the pipeline as it changes over service time can be obtained, thereby enabling long-term prediction of coating performance degradation and its energy impact.

[0060] S4. Define and calculate the comprehensive thermal protection performance index of the thermal insulation coating to quantitatively evaluate its thermal protection effect.

[0061] In step S4, the formula for calculating the comprehensive thermal protection effectiveness index Ψ is:

[0062] ,

[0063] in, and The heat loss rates are respectively those of the standard coating and the optimized coating. and The expected service life of the standard coating and the optimized coating are respectively. and Let be the weight coefficient, and satisfy... Its specific value is determined by the economic and safety requirements of the medium being transported in the pipeline.

[0064] The expected service life By degrading the coating performance to a critical threshold The corresponding time The coating lifetime is determined by taking the inverse function of the performance degradation factor equation.

[0065] Building upon the established dynamic performance prediction capability, step S4 defines an index for comprehensively and quantitatively evaluating the effectiveness of thermal protection—the comprehensive thermal protection effectiveness index. ,

[0066] in, and The heat loss rates are respectively those of the standard coating and the optimized coating. and The expected service life of the standard coating and the optimized coating are respectively. and Let be the weight coefficient, and satisfy... Its specific value is determined by the economic and safety requirements of the medium being transported in the pipeline.

[0067] This index cleverly combines the improvements of the optimized solution compared to the standard solution in two aspects (energy saving and durability), with weighting coefficients allocated according to the economic and safety priorities of specific pipeline projects. The expected service life is obtained by determining the time it takes for the coating performance degradation factor to decay to a certain critical threshold, providing a clear quantitative basis for assessing coating life.

[0068] S5. Taking the comprehensive thermal protection performance index as the optimization target, iterative calculations are performed by adjusting the construction process parameters or structural parameters of the thermal insulation coating. When the index reaches a preset threshold, the optimal coating performance parameters and corresponding process parameters are output.

[0069] In step S5, the construction process parameters to be optimized include the coating thickness, the drying and curing temperature profile, and the particle size distribution of functional fillers in the coating system.

[0070] The iterative calculation employs a genetic algorithm or a particle swarm optimization algorithm to perform global optimization within a set parameter space.

[0071] Step S5 is the core of the entire method optimization. It uses the comprehensive thermal protection effectiveness index defined in step S4. Maximizing the performance is the optimization objective, achieved through iterative calculations by adjusting the construction process parameters of the thermal insulation coating (such as spray thickness, drying and curing temperature profile, and particle size distribution of functional fillers). This process typically employs intelligent optimization algorithms such as genetic algorithms and particle swarm optimization to perform global optimization within a pre-defined parameter space. When the index reaches the preset optimization threshold, the calculation stops and the optimal coating performance parameters and their corresponding process parameter combinations are output to guide production and construction.

[0072] The method further includes step S6:

[0073] The optimized coating performance parameters and process parameters are used as inputs to drive the automated spraying equipment to perform the construction of pipeline insulation coating, and the key parameters during the construction process are fed back to the calculation model to achieve closed-loop control.

[0074] This step takes the optimal process parameters output from step S5 as input and directly drives the automated spraying equipment to perform precise application of the pipe insulation coating. Simultaneously, the system feeds back key actual parameters monitored during construction (such as coating thickness and density) to the computational model in real time for model calibration and continuous optimization, forming an intelligent closed loop of "design-optimization-construction-feedback," significantly improving the accuracy and efficiency of engineering applications.

[0075] For example, taking a 1km long oil pipeline with an outer diameter of 219mm in an oil field as the application object, the medium inside the pipeline is high-temperature crude oil at a temperature of 120℃. The external ambient temperature is 20℃. The original design used a conventional microporous calcium silicate insulation layer with a thickness of 100mm. The present invention is now being used to apply the insulation coating (number of layers) to this pipe. =1) Optimize.

[0076] S1. Parameter Acquisition:

[0077] Obtain pipeline operation and environmental parameters: pipeline length L = 1000m, pipeline inner radius... =0.1m, original outer radius of insulation layer =0.219m. Convective heat transfer coefficient inside the pipe. =1000 W / (m²·K), external convection heat transfer coefficient =25 W / (m²·K). Obtain the basic physical properties of candidate thermal insulation coating materials (e.g., nano-aerogel composites):

[0078] Inherent thermal conductivity of solid substrate =0.02 W / (m·K). Analysis of scanning electron microscopy images revealed that the typical porosity of this coating material was... It is approximately 0.85.

[0079] S2. Establish a heat loss calculation model:

[0080] First, according to Calculate the equivalent thermal conductivity of the coating. Determine the structural correction coefficient by fitting experimental data. =0.15, =1.8. Substitute into the formula:

[0081] ≈0.021 W / (m·K),

[0082] Then, based on the pipeline heat loss calculation model, the original design ( =0.06 W / (m·K)) and the heat loss of the candidate coating at the same thickness. and .

[0083] S3. Introduce a performance degradation factor for dynamic correction:

[0084] Determine the performance degradation parameters of the nano-aerogel coating: initial performance factor =1.0, residual performance factor =0.9, degradation rate constant k=0.001 (1 / day).

[0085] The performance degradation factor is: ,Will Multiplying the heat loss Q in step S2 by the correction factor yields the dynamic heat loss rate of the pipeline on day t of service.

[0086] S4. Calculate the overall thermal protection effectiveness index:

[0087] Define the comprehensive thermal protection effectiveness index Set weighting coefficients. =0.7 (focusing on energy saving), =0.3 (Emphasis on lifespan). Original design lifespan. The lifespan is 10 years (3650 days). The service life of the optimized coating is calculated. :

[0088] Set a critical threshold for performance degradation =0.92, by solving the equation 0.92=exp(-0.001* )+0.9, to get It is estimated to be 693 days, but considering its still relatively high residual performance, its effective lifespan can be set. It is 15 years (5475 days). Substitute into the formula to calculate. The results showed A value significantly greater than 1 indicates that the optimized solution is superior to the original design.

[0089] S5, with Parameter optimization for the target:

[0090] by With maximization as the objective, a genetic algorithm was used for iterative optimization. Optimization variables included coating thickness (80mm to 120mm) and the particle size distribution of functional fillers (such as hollow glass microspheres) (D50: 10μm to 50μm). Through iterative calculations, when the coating thickness was optimized to 95mm and a filler with D50 = 25μm was used, When the index reaches a preset threshold (e.g., 25% improvement over the original design), the algorithm terminates and outputs the optimal process parameters.

[0091] Optional, S6, closed-loop control construction:

[0092] The optimal parameters (thickness 95mm, filler D50=25μm) are input into the automated spraying control system. The system controls the robotic arm to spray according to the preset thickness trajectory and monitors the coating thickness in real time. Simultaneously, the material system mixes materials according to the optimized filler ratio. Actual parameters during the construction process (such as coating density and thickness uniformity) are fed back to the model database for subsequent model calibration and updates, achieving closed-loop control.

[0093] Electronic device 200 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 200 may include, but is not limited to, processor 201 and memory 202. Those skilled in the art will understand that... Figure 2 This is merely an example of electronic device 200 and does not constitute a limitation on electronic device 200. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0094] The processor 201 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0095] The memory 202 can be an internal storage unit of the electronic device 200, such as a hard disk or RAM of the electronic device 200. The memory 202 can also be an external storage device of the electronic device 200, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 200. Furthermore, the memory 202 can include both internal and external storage units of the electronic device 200. The memory 202 is used to store the computer program 203 and other programs and data required by the electronic device. The memory 202 can also be used to temporarily store data that has been output or will be output.

[0096] The above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. A method for evaluating the heat transfer protection effect of a pipe insulation coating, characterized in that, Includes the following steps: S1. Obtain pipeline operating parameters, environmental parameters, and basic physical properties of the thermal insulation coating material; S2. Based on the aforementioned basic physical property parameters, establish a pipeline heat loss calculation model that considers the equivalent thermal conductivity of the coating microstructure. The pipeline heat loss calculation model is as follows: ， in, This represents the total heat loss of the pipeline per unit time. For the length of the pipe, and These are the fluid temperature inside the pipe and the ambient temperature, respectively. and These are the inner radius of the pipe and the outermost radius of the outermost insulation layer, respectively. For the first The outer radius of the thermal insulation coating layer and These are the convective heat transfer coefficients inside and outside the pipe, respectively. For the first The equivalent thermal conductivity of the thermal insulation coating; S3. Introduce a coating performance degradation factor based on time-varying conditions to modify the pipeline heat loss calculation model and obtain the pipeline dynamic heat loss rate. S4. Define and calculate the comprehensive thermal protection performance index of the thermal insulation coating to quantitatively evaluate its thermal protection effect. S5. Taking the comprehensive thermal protection performance index as the optimization target, iterative calculations are performed by adjusting the construction process parameters or structural parameters of the thermal insulation coating. When the index reaches a preset threshold, the optimal coating performance parameters and corresponding process parameters are output.

2. The method according to claim 1, characterized in that, In step S2, the equivalent thermal conductivity of the insulation coating , in, The inherent thermal conductivity of the coating solid substrate, The volume fraction of porosity in the coating. and The structural correction coefficients, which are related to pore shape and distribution, are obtained by fitting experimental data.

3. The method according to claim 1, characterized in that, The performance degradation factor η(t) in step S3 is calculated by the following formula: ， in, For coating service life; This is the initial performance factor of the coating. This refers to the residual performance factor after the coating performance has stabilized due to degradation. The dynamic heat loss rate is a degradation rate constant related to the weather resistance of the coating material, expressed as a property degradation factor. This is obtained by multiplying the correction factor into the heat loss calculation model.

4. The method according to claim 1, characterized in that, In step S4, the formula for calculating the comprehensive thermal protection effectiveness index Ψ is: ， in, and The heat loss rates are respectively those of the standard coating and the optimized coating. and The expected service life of the standard coating and the optimized coating are respectively. and Let be the weight coefficient, and satisfy... Its specific value is determined by the economic and safety requirements of the medium being transported in the pipeline.

5. The method according to claim 4, characterized in that, The expected service life By degrading the coating performance to a critical threshold The corresponding time The coating lifetime is determined by taking the inverse function of the performance degradation factor equation.

6. The method according to claim 1, characterized in that, In step S5, the construction process parameters to be optimized include the coating thickness, the drying and curing temperature profile, and the particle size distribution of functional fillers in the coating system. The iterative calculation employs a genetic algorithm or a particle swarm optimization algorithm to perform global optimization within a set parameter space.

7. The method according to claim 1, characterized in that, The method further includes step S6: The optimized coating performance parameters and process parameters are used as inputs to drive the automated spraying equipment to perform the construction of pipeline insulation coating, and the key parameters during the construction process are fed back to the calculation model to achieve closed-loop control.

8. An electronic device, characterized in that, include: One or more processors; A storage unit is used to store one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the performance evaluation method for the heat transfer protection effect of the pipe insulation coating according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it can implement the performance evaluation method for the heat transfer protection effect of the pipeline thermal insulation coating according to any one of claims 1 to 7.

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