Roof heat transfer coefficient optimization method and device, electronic device and storage medium

Abstract

The invention relates to the field of building energy conservation, in particular to a roof heat transfer coefficient optimization method, which comprises the following steps of: acquiring at least one plant combination and physical parameters thereof as well as a thickness range of a soil matrix, and permutation and combination are performed on the plant combination and different matrix thicknesses to obtain a sample module group; constructing a geometric model of the roof greening module, inputting the detailed parameters of the sample module group into the geometric model, and performing simulation analysis of a heat transfer coefficient; preliminarily determining a relationship between the heat transfer coefficient and each parameter of the sample module group according to a simulation analysis result; building a roof greening heat transfer model, wherein the model is used for describing and calculating the transfer process of heat in a roof structure; acquiring field experiment data, and performing comparative analysis on the field experiment data and simulation data obtained by geometric model analysis; calculating and analyzing specific influences of different sample module groups on a roof heat transfer coefficient through a roof greening heat transfer model to obtain an optimal parameter combination; and outputting a corresponding optimal roof greening module configuration scheme according to the optimal parameter combination.

Description

Technical Field

[0001] This application relates to the technical field of building energy conservation, and specifically relates to an optimization method for the roof heat transfer coefficient, an optimization device, an electronic device, and a storage medium. Background Art

[0002] In recent years, the accelerating advancement of global climate change and urbanization has injected strong impetus into the global economy and urbanization development. However, the ensuing challenges cannot be ignored. The rapid expansion of urban space has led to a sharp reduction in the greening coverage rate, the intensification of the urban "heat island effect", the rise in regional temperature and the decline in humidity. Coupled with the continuous increase in building energy consumption, it has become an important factor restricting the sustainable development of cities. Against this background, it has become an urgent task to explore and implement effective strategies to alleviate the above problems.

[0003] Roof greening, as an emerging green building technology, is widely regarded as one of the effective means to address urban environmental problems. By planting vegetation on the top of buildings, this technology can not only increase the green space in the city, but also significantly improve the quality of the urban ecological environment. Specifically, roof vegetation has the dual effects of absorbing rainwater and purifying the air, which helps to regulate the urban microclimate, increase air humidity, and relieve the pressure on the urban drainage system. In addition, it can effectively reduce the heat absorption of buildings, reduce the temperature difference between indoors and outdoors, and reduce the use of air conditioners in summer, thus achieving the goal of energy conservation and emission reduction.

[0004] Although roof greening shows great potential, its actual application still faces several technical challenges. The existing roof greening modules on the market still have deficiencies in design and implementation, mainly reflected in the lack of scientific plant selection and the diversity of module construction materials without a unified standard. These problems not only limit the full play of the ecological benefits and energy-saving efficiency of roof greening, but also affect its popularity and acceptance in urban buildings.

[0005] Therefore, in view of the limitations of the existing technology, there is an urgent need to develop an optimization scheme for the roof heat transfer coefficient. The system developed by this scheme should comprehensively consider plant adaptability, the standardization and environmental protection of module materials, in order to achieve optimized energy-saving effects and ecological benefits, and at the same time promote the large-scale popularization and application of roof greening. Solving the above problems through in-depth research and technological innovation will be a key step in promoting urban green development and improving the quality of residents' lives. Summary of the Invention

[0006] The purpose of the present invention is to overcome the deficiencies in the background art and provide an optimization scheme for the roof heat transfer coefficient.

[0007] The technical solution adopted by the present invention is as follows:

[0008] An optimization method for the roof heat transfer coefficient, comprising:

[0009] Obtain at least one plant combination and its physical parameters, as well as the thickness range of the soil substrate, and arrange and combine the plant combination with different substrate thicknesses to obtain a sample module group;

[0010] Construct a geometric model of the green roof module, input the detailed parameters of the sample module group into the geometric model, and conduct a simulation analysis of the heat transfer coefficient; according to the results of the simulation analysis, preliminarily determine the relationship between the heat transfer coefficient and the parameters of each sample module group;

[0011] Based on the equivalent thermal resistance of the green roof module, establish a green roof heat transfer model, which is used to describe and calculate the heat transfer process in the roof structure;

[0012] Among them, the green roof heat transfer model is:

[0013]

[0014] Q is the transferred heat, with the unit of W, U is the heat transfer coefficient, with the unit of W / m 2 ·K, A is the heat transfer area, with the unit of m 2 , ΔT is the temperature difference, with the unit of K, R is the total thermal resistance of heat transfer, with the unit of m 2 ·K / W;

[0015] R = the thermal resistance of the plant layer + the thermal resistance of the soil substrate layer + the thermal resistance of the roof structure layer, and the thermal resistance of the roof structure layer is calculated from the structural layer parameters of each layer of the roof enclosure structure component;

[0016] Obtain on-site experimental data, conduct a comparative analysis with the simulation data obtained from the geometric model analysis, calculate the relative error, and determine the accuracy of the simulation. The on-site test is carried out under the same conditions as the simulation analysis;

[0017] Calculate and analyze the specific influence of different sample module groups on the roof heat transfer coefficient through the green roof heat transfer model to obtain the optimal parameter combination;

[0018] According to the optimal parameter combination, output the corresponding optimal green roof module configuration plan, which includes the recommended plant combination, the thickness of the soil substrate, and the expected energy-saving effect.

[0019] In a further plan, the plant combination includes one or several of ground cover plants and herbaceous plants;

[0020] The ground cover plants include Sedum lineare, Sedum acre, Thymus serpyllum, and the herbaceous plants include Iris tectorum and Trifolium repens.

[0021] In a further solution, the physical parameters of the plant combination include plant height, leaf area index, leaf reflectivity, leaf transmittance, minimum stomatal resistance, maximum volumetric water content at saturation, minimum residual volumetric water content, and initial volumetric water content.

[0022] In a further solution, the geometric model for constructing the green roof module includes: performing a 1:1 simulation of the green roof module through the EnergyPlus software.

[0023] In a further solution, the roof structural layer includes a cement mortar plaster layer, a polyethylene foam insulation board, and a reinforced concrete slab, and the structural layer parameters of each layer include thickness, thermal conductivity, density, and specific heat capacity.

[0024] In a further solution, the specific analysis of the influence of different sample module groups on the roof heat transfer coefficient also includes:

[0025] Calculating the equivalent insulation board thickness to verify the feasibility of the green roof module as a thermal insulation material.

[0026] In a further solution, when obtaining on-site experimental data, comparing and analyzing it with the simulation data, calculating the relative error, and determining the accuracy of the simulation, a relative error within 10% indicates that the simulation data and the on-site experimental data match well.

[0027] Another technical solution of the present invention is: an optimization device for the roof heat transfer coefficient, including:

[0028] A data input module, used to input the plant combination and its physical parameters, the thickness range of the soil matrix, and the structural parameters of the roof structural layer, and arrange and combine the plant combination with different substrate thicknesses to obtain a sample module group;

[0029] A simulation module, used to construct a geometric model of the green roof module, perform a simulation analysis of the heat transfer coefficient according to the detailed parameters of the sample module group; and preliminarily determine the relationship between the heat transfer coefficient and each parameter according to the simulation results;

[0030] An experimental module, based on the equivalent thermal resistance of the green roof module, establishes a green roof heat transfer model, which is used to describe and calculate the heat transfer process in the roof structure;

[0031] Among them, the green roof heat transfer model is:

[0032]

[0033] Q is the transferred heat, with the unit of W, U is the heat transfer coefficient, with the unit of W / m 2 ·K, A is the heat transfer area, with the unit of m 2 , ΔT is the temperature difference, with the unit of K, R is the total heat transfer resistance, with the unit of m2 ·K / W;

[0034] R = thermal resistance of the plant layer + thermal resistance of the soil matrix layer + thermal resistance of the roof structure layer, and the thermal resistance of the roof structure layer is obtained by calculating the structural layer parameters of each layer of the roof enclosure structure component;

[0035] The comparative analysis module obtains on-site experimental data, conducts comparative analysis with the simulation data, calculates the relative error, determines the accuracy of the simulation, and the on-site test is carried out under the same conditions as the simulation analysis; analyze the specific impacts of different soil matrix thicknesses and plant combinations on the roof heat transfer coefficient, and identify the optimal parameter combination;

[0036] The output module outputs the corresponding optimal roof greening module configuration plan according to the analysis results, and the plan includes the recommended plant combination, soil matrix thickness, and expected energy-saving effect.

[0037] Another technical solution of the present invention is: an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the optimization method.

[0038] Another technical solution of the present invention is: a readable storage medium, wherein a computer program is stored in the readable storage medium, and the computer program includes program codes for controlling a process to execute the process, and the process includes the optimization method.

[0039] The beneficial effects of the present invention are:

[0040] The main contributions and innovations of the present invention are as follows:

[0041] 1. Improvement in systematicness and comprehensiveness: The present invention proposes a complete set of optimization methods for the impact of roof greening modules on the roof heat transfer coefficient, integrating parametric design, simulation analysis, and on-site experimental verification. Compared with the isolated or fragmented research in the prior art, this method is more systematic, covering the whole process from theory to practice, and can more comprehensively and accurately evaluate the impact of roof greening on building energy efficiency.

[0042] 2. Parametric design and optimization: By introducing the parametric design method, the present invention can precisely control and optimize the key parameters of the roof greening module, such as the soil matrix thickness, plant species and their combinations. This design method not only improves the flexibility and pertinence of the experiment, but also enhances the scientificity and adaptability of the roof greening configuration.

[0043] 3. Combination of accurate simulation and on-site verification: Using EnergyPlus software to conduct 1:1 simulation of the roof greening module, combined with on-site experimental data, the present invention ensures the accuracy and reliability of the heat transfer coefficient analysis, effectively reduces the gap between simulation and reality, and improves the practicality of the experimental results.

[0044] 4. Identification of optimal parameter combinations: Through comparative analysis, the optimal rooftop greening configuration is identified, including specific plant combinations and soil matrix thickness. These parameter combinations perform excellently in reducing the roof heat transfer coefficient, providing clear guidance for practical applications.

[0045] 5. Calculation of equivalent insulation board thickness: The concept of an equivalent insulation board is innovatively proposed, and the performance of the optimal rooftop greening module is quantitatively compared with that of traditional thermal insulation materials. It is clarified that a greening module with a certain thickness of soil matrix can equivalently replace a certain thickness of polyethylene foam insulation board, providing a strong basis for the application of rooftop greening as a new type of energy-saving material.

[0046] 6. Comprehensive energy conservation and ecological benefits: This invention not only focuses on reducing the roof heat transfer coefficient, but also emphasizes improving building energy efficiency, alleviating the urban heat island effect, enhancing indoor comfort through rooftop greening, and promoting ecological sustainable development, providing a green and economical solution for urban building design.

[0047] 7. Automated and intelligent tools: The proposed optimization device, electronic device, and readable storage medium provide automated and intelligent tools for rooftop greening design and research, improving work efficiency, facilitating the rapid application of research results by scientific researchers and designers, and promoting the popularization and progress of rooftop greening technology.

[0048] Details of one or more embodiments of this application are presented in the following drawings and description to make other features, purposes, and advantages of this application more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the following briefly introduces the drawings required to be used in the embodiments of this application. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 is a schematic flowchart of an optimization method for the influence of a rooftop greening module on the roof heat transfer coefficient according to an embodiment of this application.

[0051] Figure 2 is a schematic diagram of the simulation results of each combination according to an embodiment of this application.

[0052] Figure 3 is a schematic diagram of the experimental results of each combination according to an embodiment of this application.

[0053] Figure 4It is a comparison chart of the simulation results and experimental results according to the embodiments of the present application.

[0054] Figure 5 It is a schematic structural diagram of an electronic device according to the embodiments of the present application. Detailed implementation manners

[0055] To enable those of ordinary skill in the art to more clearly understand the purpose, technical solutions, and advantages of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. However, the present invention is not limited to the following embodiments.

[0056] The present invention will be described in detail with reference to exemplary embodiments, which are illustrated in the accompanying drawings. When the following description refers to the accompanying 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 one or more embodiments of this specification. On the contrary, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0057] It should be noted that: In other embodiments, the steps of the corresponding methods are not necessarily executed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may also be combined into a single step for description in other embodiments.

[0058] Embodiment 1

[0059] The present application aims to propose an optimization method for the roof heat transfer coefficient. Specifically, referring to Figure 1 , the method includes:

[0060] 1. Determine the research object

[0061] Construction method based on modular green roofs, considering different combinations of plants and different thickness characteristics of soil substrates, designed and selected 5 common green roof plants, including 3 ground covers (Sedum lineare Thunb. A, Sedum makinoi Maxim. B, Thymus serpyllum L. C) and 2 herbs (Iris tectorum Maxim. D, Trifolium repens L. E), and arranged and combined them in one, two, three, four, and five ways, with a total of 31 combinations as plant classifications, as shown in Table 1. Soil substrates with different thicknesses have different heat capacities and thermal conductivity coefficients, thus affecting the heat insulation effect of the roof. According to the common soil substrate layer thicknesses, this application divides the substrate layer into thin substrate (100 mm), relatively thin substrate (200 mm), medium substrate (300 mm), and thick substrate (400 mm). After arranging and combining different plants and soil substrate thicknesses, removing the symmetric repetitions of the sample modules, a total of 120 groups of sample modules are obtained.

[0062] Table 1 Combinations of Plants and Their Attributes

[0063]

[0064]

[0065] 2. Research Methods

[0066] 2.1 Simulation of Heat Transfer Performance of Green Roofs

[0067] Use EnergyPlus software to conduct steady-state simulation of green roofs and calculate and output the heat transfer coefficient, and investigate the influence of different greening layout forms on the thermal performance of green roofs. Based on the actual size of the green roof module (500 mm × 500 mm), construct 1:1 geometric models for 120 combinations in EnergyPlus respectively for steady-state simulation. This simplified model ignores the influence of wind speed, internal differences of each material layer, and roof slope direction. Based on the material thickness, density, specific heat, and thermal conductivity physical property parameters of each structure layer of the module, set the unsteady energy equations of each material layer, the boundary conditions of the material layer affected by direct solar radiation, and the inner surface boundary conditions of the material layer close to the indoor environment.

[0068] Among them, EnergyPlus is a professional building energy consumption simulation software developed by the U.S. Department of Energy. Based on the principle of heat balance, it can comprehensively simulate and analyze the energy consumption of buildings, including aspects such as heating, cooling, ventilation, lighting, and equipment load. This software can detail the simulation of the radiation and convective heat transfer processes on the inner and outer surfaces of buildings, with high flexibility and accuracy.

[0069] 2.1.1 Soil Substrate Thickness

[0070] The thickness of the soil substrate is one of the important factors affecting the heat conduction performance of green roofs. Soil substrates of different thicknesses have different heat capacities and thermal conductivities, thus affecting the heat insulation effect of the roof. In this embodiment, the soil substrate layer is divided into a thin substrate (100 mm), a relatively thin substrate (200 mm), a medium substrate (300 mm), and a thick substrate (400 mm).

[0071] 2.1.2 Plant species

[0072] Different plant species have different growth characteristics and ecological benefits. For example, drought-tolerant plants are suitable for green roofs in arid regions, while cold-tolerant plants are suitable for green roofs in cold regions. In modular design, by selecting different plant species, diverse plant combinations are formed to improve the ecological benefits of green roofs. In this embodiment, the physical properties of the arranged plant combinations are shown in Table 2.

[0073] Table 2 Physical properties of plant media

[0074]

[0075]

[0076] 2.1.3 Roof enclosure structure

[0077] The structural construction of the roof is cement mortar plaster, polyethylene foam insulation board, and reinforced concrete slab. Its physical properties are shown in Table 3. Table 3 gives the parameter characteristics of the bare roof. From the thermal resistance calculation formula the thermal resistance R of the bare roof can be obtained roof to be 0.75 m 2 ·K / W. Where d represents the material thickness and K is the thermal conductivity of the material. Among them, the parameters of the roof enclosure structure are used for subsequent software simulations.

[0078] Table 3 Parameters of roof enclosure structure layer

[0079]

[0080] 2.2 Experimental verification of green roof

[0081] For the software simulation results, this application conducts a comparative experiment to verify the results. If the error is within an acceptable range, the experiment is considered valid. The concept of equivalent thermal resistance is adopted, that is, the thermal resistance of the insulation layer of a hypothetical insulated roof with the same average inner surface temperature as the green roof within one climate cycle. Thus, the complex heat transfer, moisture transfer, and phase change coupling process is simplified into a relatively simple heat transfer model similar to that of a general roof to describe and calculate the heat transfer process in the roof structure. Since the heat transfer model of green roofs considering the thickness of plants and soil is a complex process involving multiple layers (such as the plant layer, soil layer, roof structure layer, etc.) and their interactions. The basic heat transfer equation is expressed as:

[0082]

[0083] Q is the transferred heat, with the unit of W, U is the heat transfer coefficient, with the unit of W / m 2 ·K, A is the heat transfer area with the unit of m 2 , ΔT is the temperature difference, with the unit of K, R is the total thermal resistance of heat transfer, with the unit of m 2 ·K / W; R = the thermal resistance of the plant layer + the thermal resistance of the soil matrix layer + the thermal resistance of the roof structure layer, and the thermal resistance of the roof structure layer is calculated from the structural layer parameters of each layer of the roof enclosure component.

[0084] That is, based on the above model, a heat transfer model of green roofs is established. When considering the green roof layer, the total thermal resistance R can be divided into the thermal resistance of the plant layer (R plant ), the thermal resistance of the soil matrix layer (R soil ), and the thermal resistance of the roof structure layer (R roof ). Therefore, the total thermal resistance R total can be expressed as:

[0085] R total = R roof + R soil + R plant

[0086] Among them, the thermal resistance R soil of the soil matrix layer depends on the thermal conductivity k soil of the soil and the thickness d soil :

[0087]

[0088] The thermal conductivity k soil of the soil matrix layer = 0.4 w / (m·K), and the thickness classification of the soil matrix layer is: thin matrix 100 mm (0.1 m), relatively thin matrix 200 mm (0.2 m), medium matrix 300 mm (0.3 m), thick matrix 400 mm (0.4 m).

[0089] Therefore, the calculation formulas for the thermal resistances of four different soil layers are as follows

[0090] (1) For the thin substrate layer R soil,thin :

[0091] R soil,thin = 0.05 / 0.4 = 0.125 m 2 ·K / W

[0092] (2) For the relatively thin substrate layer R soil,medicum-thin :

[0093] R soil,medicum-thin = 0.15 / 0.4 = 0.375 m 2 ·K / W

[0094] (3) For the medium substrate layer R soil,medicum :

[0095] R soil,medicum = 0.25 / 0.4 = 0.625 m 2 ·K / W

[0096] (4) For the thick substrate layer R soil,thick :

[0097] R soil,thick = 0.35 / 0.4 = 0.875 m 2 ·K / W

[0098] In this way, the thermal resistances of 31 groups of plant combinations are calculated based on the thickness and thermal conductivity of each plant, as shown in Table 4 (unit: m 2 ·K / W).

[0099] Table 4 Thermal resistance data of roof greening plant combinations

[0100] Plant combination Thermal resistance Plant combination Thermal resistance Plant combination Thermal resistance A 0.40 BE 0.43 BCE 0.45 B 0.40 CD 0.50 BDE 0.45 C 0.50 CE 0.48 CDE 0.47 D 0.50 DE 0.48 ABCD 0.45 E 0.45 ABC 0.43 ABCE 0.44 AB 0.40 ABD 0.43 ABDE 0.44 AC 0.45 ABE 0.42 ACDE 0.46 AD 0.45 ACD 0.47 BCDE 0.46 AE 0.43 ACE 0.45 ABCDE 0.45 BC 0.45 ADE 0.45 BD 0.45 BCD 0.48

[0101] Therefore, the total thermal resistance R total and the heat transfer coefficient U are as follows:

[0102] R total = R roof + R soil + R plant

[0103]

[0104] 3. Results and Discussion

[0105] 3.1 Verification of simulation and experimental results

[0106] The results after simulation by EnergyPlus are as Figure 2From the simulation results of plant species, the heat transfer coefficients of single plant species (A, B, C, D, E) and combined plant species (such as AB, AC, AD, etc.) are the same under different soil thicknesses, which are: 100mm (0.76), 200mm (0.624), 300mm (0.53), 400mm (0.46). From the simulation results of soil matrix thickness, the heat transfer coefficient decreases with the increase of soil thickness, indicating that a thicker soil layer has better heat insulation performance. The comprehensive results show that soil thickness is the key factor determining the heat transfer performance of the green roof module, and no significant difference is shown among different plant combinations in this simulation.

[0107] And through the calculation of the roof surface experiment, the results are as Figure 3 。From the classification results of the soil matrix layer, the heat transfer coefficient of the thin matrix layer is between 0.727 and 0.784; that of the relatively thin matrix layer is between 0.615 and 0.656; that of the medium matrix layer is between 0.533 and 0.563, and that of the thick matrix layer is between 0.471 and 0.494. Generally speaking, the lowest heat transfer coefficients are C, D, CD (0.471), and the highest are A, B, AB (0.784). Whether it is a single plant or a combined plant green roof, the heat transfer coefficient decreases with the increase of soil thickness. This conforms to the physical principle that the increase of thermal resistance leads to the decrease of the heat transfer coefficient.

[0108] Compare the on-site measured results of the roof surface with the simulation results, and the errors between them are as Figure 4 shown. The simulation results are in good agreement with the experimental results, and the error is controlled within 10%. The relative errors between the simulation data and the experimental data are smaller for the soil matrix with thicknesses of 200mm and 300mm. In addition, due to the existence of the vegetation shadow area, it is beneficial to reduce the temperature of the outer surface of the roof in summer and decrease the roof cooling load. Therefore, the calculation results of the existing model are more conservative and average compared with the actual process.

[0109] 3.2 Influence of Different Soil Matrix Thicknesses on the Roof Heat Transfer Coefficient

[0110] This application studies the influence of different soil matrices on the heat transfer of green roofs by comparing simulation and experimental results. The results show that the average value of the simulation data for the soil matrix with a thickness of 100 mm is 0.76, and the average value of the experimental data is 0.756. The simulation and experimental data are relatively close, indicating that at a thickness of 100 mm, the heat transfer coefficient of the soil changes little, and the simulation model reflects the actual situation well. The average value of the simulation data for the 200-mm thickness is 0.624, and the average value of the experimental data is 0.631. The experimental data is slightly higher than the simulation data, indicating that as the thickness increases, the influence of the soil on heat transfer begins to appear. The average value of the simulation data for the 300-mm thickness is 0.53, and the average value of the experimental data is 0.546. The experimental data is significantly higher than the simulation data, indicating that after the thickness is further increased, the hindering effect of the soil layer on heat transfer is more obvious. Under the experimental conditions, the thick soil layer may retain more moisture, resulting in an enhanced humidity effect, thus affecting the heat transfer performance. The possible reason is that the thin soil layer has a limited hindering effect on heat transfer, so the difference between the simulation and experimental results is not significant. The average value of the simulation data for the 400-mm thickness is 0.46, and the average value of the experimental data is 0.479. When the thickness increases to 400 mm, the experimental data is still higher than the simulation data, and the difference is still significant. The possible reasons include the heat preservation effect of the thick soil layer, the heat convection and heat radiation effects in the actual environment, and these factors have a greater impact under the experimental conditions.

[0111] In summary, as the thickness of the soil matrix increases, the experimental results gradually become higher than the simulation results, and both results show that the roof heat transfer coefficient decreases significantly. The thicker soil layer can provide better heat insulation and reduce the heat conduction through the roof. This is because the increase in thickness leads to an increase in thermal resistance, thereby reducing heat transfer, and this trend is reflected in all materials and material combinations.

[0112] 3.3 Influence of Different Plant Species on Roof Heat Transfer Coefficient

[0113] By comparing the simulation and experimental results, the influence of different plant species on the heat transfer of green roofs was studied. The results showed that the experimental values of the green roof modules with two plant species were higher than the simulation values, indicating that the actual heat insulation performance was worse than the simulation results. Among the three-plant combinations, the experimental values of ACD, BCD, BCE, BDE, and CDE were close to or slightly lower than the simulation values, showing good actual effects. For a few combinations (such as CD, CE, and DE), the experimental values were close to or slightly lower than the simulation values, indicating that the actual heat insulation performance was similar to or better than the simulation results. The experimental values of the green roof modules with four materials and five-plant combinations were generally higher than the simulation values, and the actual heat insulation performance was slightly worse than the simulation results. As the number of plant species increased, the difference between the experimental and simulation results also increased, which might be due to the influence of interface thermal resistance and environmental factors in the actual application of complex plant combinations. In addition, the variation trends of the heat transfer coefficients of the green roofs with different plant combinations showed that since the soil thickness played a dominant role in the heat transfer process, the difference in the heat transfer coefficients of the green roofs with single plants and combined plants was small.

[0114] In summary, different plant combinations had little influence on the roof heat transfer coefficient. From the experimental results, single plant species could provide better heat insulation effects (such as C and D). According to the analysis of the physical properties of plants, single plants would form a more uniform and dense covering layer during growth, and such a covering layer could effectively prevent solar radiation and heat from being conducted to the roof, thus reducing the heat transfer coefficient. In contrast, combined plants might have uneven covering layers due to limited module space and differences in the growth habits and densities of different plants, reducing the heat insulation effect. In addition, the plant coverage rate and transpiration also had important effects on the heat conduction performance.

[0115] 3.4 Equivalent Insulation Board Thickness

[0116] Studies have shown that using a combination of soil and plants to replace traditional insulation boards can effectively reduce the heat transfer performance of buildings. This technology is sustainable, uses renewable resources as raw materials, helps reduce dependence on non-renewable resources, and meets the modern society's pursuit of environmental protection, energy conservation, and sustainable development. In this application, through the optimal combination of plants and soil U green roof , the relationship between the heat transfer coefficient U and the thermal resistance R is: U = 1 / R;

[0117] Therefore, the thermal resistance R of the optimal group green roof is: U green roof = 1 / R green roof . The thermal conductivity k of the polyethylene foam insulation board used in this article insulation is 0.03 W·m -1 ·K -1 , and the thickness d insulationis 0.02 m. According to the thermal resistance formula R = d / k, the thermal resistance R of the polyethylene foam insulation board is calculated insulation is 0.667 m 2 ·K / W.

[0118] In order to make the thermal resistance of the optimal combination equivalent to that of the insulation board, the equivalent thickness d is required equivalent such that: R green roof = R insulation . Substitute into the relational expression:

[0119]

[0120] Solve for d equivalent :

[0121]

[0122] Overall, the heat transfer coefficients of C, D, and CD are the smallest compared to those of other green roofs. By comparing the simulation values and experimental results of different soils, it is found that the optimal groups with relatively small relative errors and low heat transfer coefficients are C-200 (0.624 W / m 2 ·K), C-300 (0.53 W / m 2 ·K), D-200 (0.624 W / m 2 ·K), D-300 (0.53 W / m 2 ·K), CD-200 (0.624 W / m 2 ·K), CD-300 (0.53 W / m 2 ·K). After substituting into the formula, it is obtained that when U green roof is 0.624, the equivalent insulation board thickness is 0.048 m, and when U green roof is 0.624, the equivalent insulation board thickness is 0.057 m. Therefore, the green roof modules with the above parameter combinations can effectively replace the traditional polyethylene foam insulation board and be effectively applied in practice. In other words, C-200, D-200, and CD-200 can replace the traditional roof insulation material with a thickness of 0.048 m in actual applications, and C-300, D-300, and CD-300 can effectively replace the traditional insulation material with a thickness of 0.057 m. Through the above method, the optimal group is equivalent to the insulation board thickness, so as to use modular green roofs to replace traditional insulation materials in actual projects, maximizing economic and ecological benefits.

[0123] Among them, C-200 represents the combination of creeping thyme + a relatively thin layer of substrate, C-300 represents the combination of creeping thyme + a middle layer of substrate, and the rest will not be elaborated.

[0124] 3.5 Parameter Optimization and Design Guidelines

[0125] Based on the research results, this application proposes a key parameter combination for optimizing the roof greening design and summarizes specific design guidelines. Single species plants suitable for roof greening modules include creeping thyme (ground cover) and iris (herbaceous), and the combined plants are creeping thyme (ground cover) + iris (herbaceous). When the soil substrate thickness of these plants is 200 mm, it can equivalently replace the thickness of 0.048 m of the traditional insulation board polyethylene foam insulation board; when the soil substrate thickness is 300 mm, it can effectively replace 0.057 m of insulation materials. In the actual process, the traditional insulation materials can be selected and replaced according to specific circumstances. Through these optimized designs, the high-efficiency energy conservation and maximization of ecological benefits of roof greening can be achieved.

[0126] 4. Conclusion

[0127] Through the parametric design method, precise control and optimization of design parameters are achieved, systematically analyzing and optimizing each key parameter of roof greening, and improving the scientificity and operability of the design. This application systematically analyzes the influence of key parameters such as soil substrate thickness and plant species on the roof heat transfer coefficient through comparative simulation and experimental data. The research results show that:

[0128] (1) The simulation results are all in good agreement with the experimental results, and the error is within the range of 10%. The relative errors between the simulation data and the experimental data are relatively small for the soil substrates with thicknesses of 200 mm and 300 mm.

[0129] (2) As the soil substrate thickness increases, the experimental results gradually become higher than the simulation results, and both results show that the roof heat transfer coefficient decreases significantly. The data indicates that the thicker the soil substrate thickness, the smaller the heat transfer coefficient, and the better the heat insulation performance.

[0130] (3) The influence of plants on the heat transfer performance of roof greening modules is not significant. Due to the influence of the interface thermal resistance and environmental factors in the actual application of complex plant combinations, as the number of plant species increases, the difference between the experimental and simulation results also increases. In addition, for combined plants, due to limited module space and differences in the growth habits and densities of different plants, the coverage layer may be uneven. Therefore, single plant species can relatively provide better heat insulation effects.

[0131] (4) The optimal parameter groups are C-200 (0.624 W / m 2 ·K), C-300 (0.53 W / m 2 ·K), D-200 (0.624 W / m 2 ·K), D-300 (0.53 W / m 2 ·K), CD-200 (0.624 W / m 2·K), CD - 300(0.53 W / m 2 ·K). When the thickness of the soil matrix is 200 mm, it can equivalently replace the thickness of a 0.048 m traditional insulation board, polyethylene foam insulation board; when the thickness of the soil matrix is 300 mm, it can effectively replace 0.057 m of insulation material.

[0132] This application has screened out parametric green roof modules with good energy conservation and emission reduction effects, which can significantly improve the building energy conservation effect and ecological benefits. The suggestions put forward provide a scientific basis and practical reference for green roofs.

[0133] Embodiment 2

[0134] Based on the same concept, this application also proposes an optimization method and device for the roof heat transfer coefficient, including:

[0135] A data input module, according to the type of plants and the thickness of the soil matrix, selects several common plants used for green roofs, arranges and combines them in one - plant or multiple - plant ways to obtain a group of plant combinations, and then arranges and combines the plant combinations in the group of plant combinations with soil matrices of different thicknesses to obtain a group of sample modules; prepares roof enclosure structure components, including a cement mortar plaster layer, a polyethylene foam insulation board, and a reinforced concrete slab, and records the structural layer parameters of each layer, including thickness, thermal conductivity, density, and specific heat capacity;

[0136] A simulation module, constructs a 1:1 geometric model of the green roof module through EnergyPlus software, inputs the detailed parameters of the green roof module applying the group of sample modules, and conducts a simulation analysis of the heat transfer coefficient; among them, the detailed parameters include the thickness of the soil matrix and the physical parameters of the plant combination; according to the simulation results, initially determine the relationship between the heat transfer coefficient and each parameter;

[0137] An experimental module, establishes a green roof heat transfer model using the concept of equivalent thermal resistance, and conducts on - site experiments under the same conditions as the simulation analysis. This model is used to describe and calculate the heat transfer process in the roof structure;

[0138] Among them, the green roof heat transfer model is:

[0139]

[0140] Q is the transferred heat with the unit of W, U is the heat transfer coefficient with the unit of W / m 2 ·K, A is the heat transfer area with the unit of m 2 , ΔT is the temperature difference with the unit of K, R is the total heat transfer resistance with the unit of m 2 ·K / W; R = the thermal resistance of the plant layer + the thermal resistance of the soil matrix layer + the thermal resistance of the roof structure layer, and the thermal resistance of the roof structure layer is calculated from the structural layer parameters of each layer of the roof enclosure structure component;

[0141] A comparative analysis module compares and analyzes the simulation data with the on-site experimental data, calculates the relative error, and determines the accuracy of the simulation; analyzes the specific effects of different soil matrix thicknesses and plant combinations on the roof heat transfer coefficient, and identifies the optimal parameter combination;

[0142] An output module proposes an optimal roof greening module configuration plan based on the analysis results to guide the design and implementation of actual roof greening projects, and provides specific design guidelines, including recommended plant combinations, soil matrix thicknesses, and expected energy-saving effects, for practical applications.

[0143] Embodiment III

[0144] This embodiment also provides an electronic device, referring to Figure 5 , including a memory 404 and a processor 402. The memory 404 stores a computer program, and the processor 402 is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0145] Specifically, the above-mentioned processor 402 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured as one or more integrated circuits implementing the embodiments of the present application.

[0146] Among them, the memory 404 may include a mass storage 404 for data or instructions. By way of example and not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In suitable cases, the memory 404 may include removable or non-removable (or fixed) media. In suitable cases, the memory 404 may be internal or external to the data processing device. In a particular embodiment, the memory 404 is a non-volatile memory. In a particular embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). In suitable cases, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these. In suitable cases, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0147] The memory 404 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402.

[0148] By reading and executing the computer program instructions stored in the memory 404, the processor 402 implements the optimization method for any roof heat transfer coefficient in the above embodiments.

[0149] Optionally, the above electronic device may further include a transmission device 406 and an input / output device 408. Among them, the transmission device 406 is connected to the above processor 402, and the input / output device 408 is connected to the above processor 402.

[0150] The transmission device 406 can be used to receive or send data via a network. Specific examples of the above network may include wired or wireless networks provided by the communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0151] The input / output device 408 is used to input or output information.

[0152] Embodiment 4

[0153] This embodiment also provides a readable storage medium. The readable storage medium stores a computer program, and the computer program includes program code for controlling a process to execute the process. The process includes the optimization method for the influence of the roof greening module on the roof heat transfer coefficient according to Embodiment 1.

[0154] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and will not be elaborated here.

[0155] Generally, various embodiments can be implemented in hardware or special circuits, software, logic, or any combination thereof. Some aspects of the present invention can be implemented in hardware, while other aspects can be implemented by firmware or software executed by a controller, a microprocessor, or other computing devices, but the present invention is not limited thereto. Although various aspects of the present invention can be shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, as a non-limiting example, the blocks, devices, systems, technologies, or methods described in this application can be implemented in hardware, software, firmware, special circuits or logic, general hardware or a controller or other computing devices, or some combination thereof.

[0156] Embodiments of the present invention can be implemented by computer software, which is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. A computer software or program (also referred to as a program product), including software routines, applets, and / or macros, can be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. The computer program product can include one or more computer-executable components configured to perform the embodiments when the program runs. One or more computer-executable components can be at least one software code or a part thereof. Additionally, at this point, it should be noted that any box in the logical flow in the figure can represent a program step, or interconnected logical circuits, boxes, and functions, or a combination of program steps and logical circuits, boxes, and functions. The software can be stored on physical media such as memory chips or storage blocks implemented within the processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs. The physical media is a non-transitory medium.

[0157] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope described in this specification.

[0158] The above embodiments only represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. Optimization method for roof heat transfer coefficient, characterized in that, Including: Obtain at least one plant combination and its physical parameters, as well as the thickness range of the soil substrate, and arrange and combine the plant combination with different substrate thicknesses to obtain a sample module group; Construct a geometric model of the green roof module, input the detailed parameters of the sample module group into the geometric model, and conduct a simulation analysis of the heat transfer coefficient; according to the results of the simulation analysis, preliminarily determine the relationship between the heat transfer coefficient and the parameters of each sample module group; Based on the equivalent thermal resistance of the green roof module, establish a green roof heat transfer model, which is used to describe and calculate the heat transfer process in the roof structure; Among them, the green roof heat transfer model is: Q is the heat transferred, in watts (W), U is the heat transfer coefficient, in watts per square meter kelvin (W / m 2 ·K), A is the heat transfer area, in square meters (m 2 ), ΔT is the temperature difference, in kelvin (K), and R is the total thermal resistance to heat transfer, in square meter kelvin per watt (m 2 ·K / W). R = thermal resistance of the plant layer + thermal resistance of the soil substrate layer + thermal resistance of the roof structure layer, and the thermal resistance of the roof structure layer is calculated from the structural layer parameters of each layer of the roof enclosure structure components; Obtain on-site experimental data, conduct a comparative analysis with the simulation data obtained from the geometric model analysis, calculate the relative error, and determine the accuracy of the simulation. The on-site test is carried out under the same conditions as the simulation analysis; Calculate and analyze the specific impact of different sample module groups on the roof heat transfer coefficient through the green roof heat transfer model to obtain the optimal parameter combination; According to the optimal parameter combination, output the corresponding optimal green roof module configuration plan, which includes the recommended plant combination, the thickness of the soil substrate, and the expected energy-saving effect.

2. The optimization method of the roof heat transfer coefficient according to claim 1, characterized in that The plant combination includes one or several of ground cover plants and herbaceous plants; The ground cover plants include Sedum lineare, Sedum makinoi, Thymus serpyllum, and the herbaceous plants include Iris tectorum and Trifolium repens.

3. The optimization method of the roofing heat transfer coefficient according to claim 1, characterized in that The physical parameters of the plant combination include plant height, leaf area index, leaf reflectance, leaf transmittance, minimum stomatal resistance, maximum volumetric water content at saturation, minimum residual volumetric water content, and initial volumetric water content.

4. The optimization method of the roof heat transfer coefficient according to claim 1, characterized in that, The construction of the geometric model of the green roof module includes: conducting a 1:1 simulation of the green roof module through EnergyPlus software.

5. The optimization method of the roof heat transfer coefficient according to claim 1, characterized in that, The roof structure layer includes a cement mortar plaster layer, a polyethylene foam insulation board, and a reinforced concrete slab, and the structural layer parameters of each layer include thickness, thermal conductivity, density, and specific heat capacity.

6. The optimization method of the roof heat transfer coefficient according to claim 1, characterized in that The analysis of the specific impact of different sample module groups on the roof heat transfer coefficient also includes: Calculate the equivalent insulation board thickness and verify the feasibility of the green roof module as a thermal insulation material.

7. The experimental method for the roof heat transfer coefficient according to claim 1, characterized in that When obtaining on-site experimental data, conducting a comparative analysis with the simulation data, calculating the relative error, and determining the accuracy of the simulation, a relative error within 10% is expressed as a good match between the simulation data and the on-site experimental data.

8. Optimization device for roof heat transfer coefficient, characterized in that, Including: A data input module, used to input the plant combination and its physical parameters, the thickness range of the soil substrate, and the structural parameters of the roof structure layer, and arrange and combine the plant combination with different substrate thicknesses to obtain a sample module group; A simulation module, used to construct a geometric model of the green roof module, conduct a simulation analysis of the heat transfer coefficient according to the detailed parameters of the sample module group; according to the simulation results, preliminarily determine the relationship between the heat transfer coefficient and each parameter; An experimental module, based on the equivalent thermal resistance of the green roof module, establish a green roof heat transfer model, which is used to describe and calculate the heat transfer process in the roof structure; Among them, the green roof heat transfer model is: Q is the heat transferred, in watts (W), U is the heat transfer coefficient, in watts per square meter kelvin (W / m 2 ·K), A is the heat transfer area, in square meters (m 2 ), ΔT is the temperature difference, in kelvin (K), and R is the total thermal resistance to heat transfer, in square meter kelvin per watt (m 2 ·K / W). R = plant layer thermal resistance + soil matrix layer thermal resistance + roof structure layer thermal resistance, and the roof structure layer thermal resistance is calculated from the structural layer parameters of each layer of the roof enclosure structure component; A comparative analysis module, which obtains on-site experimental data, conducts a comparative analysis with the simulation data obtained from the geometric model analysis, calculates the relative error, determines the accuracy of the simulation, and the on-site test is carried out under the same conditions as the simulation analysis; calculates and analyzes the specific influence of different soil matrix thicknesses and plant combinations on the roof heat transfer coefficient through the roof greening heat transfer model, and identifies the optimal parameter combination; An output module, which outputs the corresponding optimal roof greening module configuration plan according to the analysis results, and the plan includes the recommended plant combination, soil matrix thickness, and expected energy-saving effect.

9. An electronic device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is set to run the computer program to execute the optimization method according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that, The readable storage medium stores a computer program, and the computer program includes program code for controlling a process to execute the process, and the process includes the optimization method according to any one of claims 1 to 7.

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