Numerical simulation and quantitative analysis method for plant cooling effect

Abstract

The invention provides a numerical simulation and quantitative analysis method for a plant cooling effect, and the method comprises the following steps: extracting morphological parameters of a plant canopy based on tree three-dimensional laser point cloud data, so as to build a tree crown geometric model; building a plant-air multi-physics field coupling model which comprises a fluid control equation, a turbulence model, a plant action source item and a canopy energy balance model; solving the coupling model through a computational fluid dynamics method, and outputting data of a temperature field, a wind speed field and a humidity field around the plant so as to quantitatively analyze the cooling effect.

Description

Technical Field

[0001] The present invention belongs to the interdisciplinary technical fields of simulation, urban microclimate, etc., and specifically relates to a numerical simulation and quantitative analysis method of the cooling effect of plants. Background Art

[0002] The combined effects of high temperatures, heat waves, and the urban heat island effect worsen the urban living environment, impacting residents' comfort and health. Trees, through shading and transpiration, provide a cooling effect, becoming an important means of alleviating urban heat.

[0003] The ecological functions of urban greening trees include maintaining carbon and oxygen balance, regulating microclimates, conserving water, trapping dust and reducing pollution, and cooling and humidifying. These cooling and humidifying functions primarily occur through shading and transpirational cooling. Shading is related to structural characteristics of trees, such as tree height, leaf cover, and canopy morphology. Transpirational cooling is influenced by physiological parameters such as leaf stomatal conductance and soil water potential, as well as environmental factors such as canopy radiation and saturated vapor pressure difference. Furthermore, trees obstruct air flow, thus affecting urban ventilation and pollutants within neighborhoods. The cooling and humidifying effects of plants are closely related to their complex physiological and biochemical processes and the characteristics of their canopy components.

[0004] Currently, the main research methods for the cooling effect of plants include field measurements, remote sensing and meteorological data analysis, and numerical simulation. Field measurements are susceptible to interference from environmental and human factors, making it difficult to analyze specific factors in isolation. Furthermore, due to time and economic costs, the monitoring equipment, site layout, and personnel required for field measurements are generally limited to small scales and short periods of time, making it difficult to fully capture the spatial variability of the urban thermal environment. Remote sensing and meteorological data analysis offer a rich data product range and a wide range of research areas. However, the quality of satellite remote sensing data depends on favorable weather conditions and satellite motion cycles, resulting in data products representing land surface temperature series with insufficient temporal and spatial resolution to describe the dynamics of urban microclimates. Furthermore, when remote sensing images cover large areas and lack detailed atmospheric parameters during satellite passes, it is often difficult to ensure that the recorded surface temperature values have been atmospherically corrected and the surface emissivity accurately estimated. Consequently, it is difficult to accurately depict the real-time three-dimensional spatial pattern of the urban thermal environment. Numerical simulation methods overcome the spatial limitations of field measurements and remote sensing data.

[0005] Therefore, to accurately assess the impact of complex green vegetation on the urban thermal environment, an urban microclimate model that considers airflow, radiation, and the ecological characteristics of green trees is required to rationally analyze the ecological cooling effects of urban green space landscapes. Applying relevant physical models and methods from computational fluid dynamics (CFD) to study the interaction between green plants and the urban thermal environment can overcome the limitations of traditional observation methods that only measure single-plane parameters. This allows for quantitative, parameterized, and dynamic simulation and prediction of the cooling mechanisms of urban green spaces at different heights, providing a reference for the rational allocation, planning, and scenario verification of urban ecological construction.

[0006] The current numerical simulation research on urban microenvironment has the following characteristics: (1) Limited by the limitations of various commercial software platforms, although the industrial simulation software Ansys Fluent, STAR-CCM+ and the open source CFD calculation library OpenFOAM have powerful performance and high operability, they require high professional quality from users; although the microclimate simulation software ENVI-met is relatively friendly to non-professional users, its mathematical model is simple and cannot be secondary developed, and its grid resolution is controversial in the simulation field; although PHOENICS has relatively complete green infrastructure simulation functions, its version updates and maintenance are slow, and the various functional modules are not well coupled. (2) When describing the leaf area density (LAD) of plant canopies, existing methods cannot well describe the vertical differentiation characteristics of plant canopy LAD under real conditions and rely on existing statistical data. (3) Most existing studies focus on the comprehensive impact of specific cases such as certain greening planning and building layout on the thermal environment of urban blocks, and lack deeper research on the mechanism of physical energy exchange activities between trees themselves and the surrounding environment at the scale of individual trees and the influencing factors of the cooling effect of individual trees. (4) In the numerical simulation of plants, the influence of trunk geometry on simulation results has been discussed from the perspectives of wind-induced tree deformation and pollutant diffusion. However, these tree models are all deciduous trees, and there is currently little analysis of the impact of the resistance effect of the canopy geometry model on airflow.

[0007] The precise and appropriate depiction of plant canopy structure will significantly impact the results of plant cooling effect simulations. The development and application of high-tech multi-platform LiDAR measurement and processing methods will be an effective way to accurately capture and express three-dimensional plant information. Summary of the Invention

[0008] To address the shortcomings and deficiencies of existing technologies (such as the inadequate description of the vertical differentiation characteristics of plant canopy leaf area density (LAD) by traditional methods, low simulation accuracy due to the decoupling of plant physiological processes and flow fields, and simplified calculation of radiation attenuation), the present invention provides a method and system for numerical simulation and quantitative analysis of plant cooling effects based on the coupling of laser point clouds and multi-physics fields. This method achieves accurate quantification of plant cooling effects through the following innovative designs:

[0009] Firstly, based on tree laser point cloud data, the LAD is calculated by voxelizing and hierarchically counting the effective voxel unit ratio, and the drag coefficient is dynamically allocated according to the LAD mean, which solves the problem that traditional methods rely on statistical means and ignore the spatial heterogeneity of LAD. Secondly, a plant-air multi-physics field coupling model is constructed, and physiological processes such as plant transpiration (mass source term), flow resistance (momentum source term), and sensible heat / latent heat exchange (energy / humidity source term) are embedded in the CFD control equation in the form of source terms. Combined with a modified turbulence model (introducing a source term related to LAD and wind speed cube), the dynamic coupling of plants and flow fields is realized. At the same time, a canopy energy balance model is proposed, which abstracts the canopy layer into a single leaf, and iteratively solves the steady-state balance equations of net radiation flux, sensible heat flux, and latent heat flux to obtain the leaf surface temperature (the residual converges to 10 -6 ), and combined with the sun's ray path screening (angle threshold) and the Beer-Lambert radiation attenuation model, the accuracy of radiation calculation is improved; in addition, structured grid division (plant edge encryption) and optimized discretization format (bounded linear upwind format, unit-restricted Gaussian linear format) are used to ensure the accuracy of flow field solution.

[0010] Through the above technology, the present invention can output the temperature field, wind speed field and humidity field data around the plants, quantitatively analyze the cooling effect of different tree species on the surrounding thermal environment, and provide a scientific basis for the selection and spatial layout of urban greening tree species.

[0011] The technical solution specifically adopted by the present invention to solve the technical problem is:

[0012] A numerical simulation and quantitative analysis method for plant cooling effects includes the following steps:

[0013] Based on the three-dimensional laser point cloud data of trees, the plant canopy morphological parameters are extracted to establish the crown geometry model;

[0014] Construct a plant-air multi-physics coupling model, including fluid control equations, turbulence models, plant source terms, and canopy energy balance models;

[0015] The coupled model is solved by computational fluid dynamics methods, and the temperature field, wind speed field and humidity field data around the plants are output to quantitatively analyze the cooling effect.

[0016] Furthermore, the plant canopy morphological parameters include leaf area density LAD, resistance coefficient, tree height, crown width and crown height, wherein LAD is obtained by voxelizing laser point cloud data and calculating the ratio of effective voxel units; the resistance coefficient is dynamically allocated according to the canopy LAD mean.

[0017] Furthermore, the plant action source terms include a mass source term, a momentum source term related to the canopy LAD and wind speed, an energy source term calculated based on the sensible heat and latent heat flux of the leaves, the sensible heat flux is determined by the temperature difference between the leaf surface and the air and the boundary layer resistance, and a humidity source term calculated by the transpiration water vapor flux.

[0018] Furthermore, the canopy energy balance model abstracts the crown layer into a single leaf, and solves the leaf surface temperature through the steady-state balance equations of net radiation flux, sensible heat flux and latent heat flux, where the latent heat flux is calculated by the latent heat of water vaporization and transpiration flux; the leaf surface temperature is calculated iteratively until the residual converges.

[0019] Furthermore, the calculation process uses structured grids to divide the computational domain, the grid in the plant canopy area is encrypted, and the divergence term uses a bounded linear upwind format when discretely solving, and the gradient term uses a unit-restricted Gaussian linear format.

[0020] Furthermore, the turbulence model is a Realizable k-ε model, and both the turbulent flow energy term and the dissipation rate source term contain cubic terms of the canopy LAD and the wind speed.

[0021] Furthermore, when calculating the radiation model, the radiation propagation path is screened by the angle threshold between the sun's ray vector and the grid list, and the net radiation flux of each layer in the canopy is dynamically updated; the Beer-Lambert theorem is used to calculate the radiation attenuation of shortwave and longwave in the canopy.

[0022] Furthermore, the calculation of the mass source term includes a pore resistance calculation model: based on the shortwave radiation flux and the saturated vapor pressure difference, it is determined by the minimum pore resistance and the dynamic correction term.

[0023] Furthermore, the plant is considered as a porous medium, satisfying the following assumptions: the three-dimensional porosity is characterized by LAD; the canopy and trunk resistance are isotropic; and the influence of canopy branches on airflow is ignored.

[0024] And, a numerical simulation and quantitative analysis system for plant cooling effects, comprising:

[0025] Data processing module: used to obtain three-dimensional laser point cloud data of trees, extract plant canopy morphological parameters including leaf area density (LAD), drag coefficient, tree height, crown width, and crown height, and establish a crown geometry model. LAD is obtained by voxelizing the laser point cloud data and calculating the ratio of valid voxel units. The drag coefficient is dynamically allocated based on the mean value of the canopy LAD.

[0026] Model construction module: used to construct a plant-air multi-physics field coupling model, including fluid control equations, turbulence model, plant action source terms including mass source terms, momentum source terms, energy source terms, and humidity source terms, and a canopy energy balance model;

[0027] Solution module: used to solve the coupled model through computational fluid dynamics method, using structured grid to divide the computational domain, using bounded linear upwind format for divergence term and unit-restricted Gaussian linear format for gradient term when discrete solution;

[0028] Analysis module: used to output the temperature field, wind speed field and humidity field data around the plant, and quantitatively analyze the cooling effect of the plant.

[0029] And, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0030] A non-transitory computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0031] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects:

[0032] Accurately characterize plant canopy structure and solve the problem of insufficient traditional parameterization: calculate leaf area density (LAD) by voxelizing and layering statistically effective voxel unit ratios of laser point cloud data, and dynamically allocate resistance coefficients according to the LAD mean. This breaks through the limitations of traditional methods that rely on statistical means and ignore the vertical differentiation characteristics of LAD, and realizes the three-dimensional refined characterization of plant canopy structure.

[0033] Dynamic coupling of multiple physical fields improves simulation realism: Physiological processes such as plant transpiration (mass source term), flow resistance (momentum source term), and sensible / latent heat exchange (energy / humidity source term) are embedded in the CFD control equations in the form of source terms. Combined with a modified turbulence model (introducing source terms related to LAD and wind speed cube), dynamic coupling of plants and air flow fields is achieved, avoiding simulation deviations caused by decoupling of plant effects and flow fields in traditional models.

[0034] The energy balance model has been optimized to truly reflect canopy heat exchange: the canopy layers are abstracted into single leaves, and the leaf surface temperature is iteratively solved through the steady-state balance equations of net radiation flux, sensible heat flux, and latent heat flux. This more realistically simulates the heat and mass exchange between the canopy at different heights and the air, improving the accuracy of leaf surface temperature calculations.

[0035] The radiation model is refined to reduce attenuation errors: the radiation propagation path is screened by using the angle threshold between the sunlight vector and the grid list, and the Beer-Lambert theorem is used to calculate the attenuation of short-wave and long-wave radiation in the canopy. The net radiation flux of each layer is dynamically updated, avoiding the simplified assumptions of the traditional model on the radiation path and more accurately depicting the distribution of radiation energy in the canopy.

[0036] Grid and discrete format optimization to ensure solution reliability: The computational domain is divided into structured grids (with denser plant canopy areas) and the discrete format is optimized (bounded linear upwind for divergence terms and Gaussian linear for gradient terms). This improves the spatial resolution and numerical stability of the flow field solution and ensures the reliability of the simulation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0038] Figure 1 A schematic diagram of a technical process implemented by a method according to an embodiment of the present invention;

[0039] Figure 2 Schematic diagram of the numerical simulation solution process in an embodiment of the present invention;

[0040] Figure 3 Schematic diagram of the spatial distribution of measuring stations in an embodiment of the present invention;

[0041] Figure 4 Schematic diagram of a canopy region grid division scheme in an embodiment of the present invention;

[0042] Figure 5 Schematic diagram of the calculation domain of the plane tree simulation experiment in an embodiment of the present invention;

[0043] Figure 6 This is a diagram showing the simulation results of the influence of plane trees on the thermal environment according to an embodiment of the present invention;

[0044] Figure 7 This is a schematic diagram of voxelization of plant canopy point cloud data according to an embodiment of the present invention;

[0045] Figure 8 Schematic diagram of calculating the layered convex hull of leaf area density according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the features and advantages of the present invention more clearly understood, the following embodiments are given for detailed description:

[0047] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meanings as those commonly understood by those skilled in the art to which this application belongs.

[0048] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0049] The present invention provides a method for numerical simulation and quantitative analysis of the cooling effect of plants. Based on tree laser point cloud data, the leaf area density, drag coefficient, tree height, crown height, and crown width of the plant canopy are calculated, and a rectangular canopy expression model is established. A plant-air multi-physics field coupling model is established, including a governing component equation, a turbulence model, the introduction of a plant source term, a canopy energy balance model, and a radiation model. Using the open-source computational fluid dynamics simulation resource OpenFOAM, a finite volume method is used to discretize differential equations and a semi-implicit algorithm for the pressure-velocity coupling equations is used to solve the steady-state velocity field, achieving numerical simulation of the cooling effect of plants. The computational domain and boundary conditions are set to simulate the temperature, wind speed, and relative humidity field data surrounding the plants in specific scenarios, thereby quantitatively analyzing the cooling effect of plants on the surrounding thermal environment. This method can quantitatively assess the extent to which different tree species affect the surrounding thermal environment, providing a reference for the selection of tree species for urban greening and the layout of green space.

[0050] In view of the shortcomings of existing methods, this example uses CFD numerical simulation methods to design a simulation scheme for the cooling effect of trees on the OpenFOAM platform. High-precision laser point cloud data is used to extract the canopy geometry and leaf area density of trees, and a numerical model for simulating the cooling effect of trees is established. By simulating and quantitatively analyzing the degree of cooling effect of trees on their surrounding thermal environment, a reference is provided for the selection of tree species and the layout of green space in urban greening.

[0051] like Figure 1 As shown, the implementation of this embodiment specifically includes the following steps:

[0052] Step S0: Calculate the plant canopy leaf area density, resistance coefficient, tree height, crown height, and crown width based on the tree laser point cloud data, and then establish a rectangular canopy expression model based on the tree's morphological parameters;

[0053] Step S1: Establish a plant-air multi-physics field coupling model, including the control component equations, turbulence model, plant source term introduction, canopy energy balance model and radiation model;

[0054] Step S2: Taking canopy flow as the main analysis object, determine the computational domain, set boundary conditions, create a dictionary file, mesh the computational domain, and set initial conditions;

[0055] Step S3: Using the open source computational fluid dynamics simulation resource OpenFOAM, the finite volume method (FVM) is used to discretize differential equations and the semi-implicit method for pressure-velocity coupled equations (SIMPLE) is used to solve the steady-state velocity field. A plant-air multi-physics field coupling model program is developed to simulate the cooling effect of plants. The temperature field, wind speed field, and humidity field data around the plants are obtained, thereby quantitatively analyzing the degree of influence of plants on the surrounding thermal environment.

[0056] To simplify the modeling and calculation process, in this example, the plant canopy is considered as a porous medium and the following assumptions are made:

[0057] (1) Treating trees as porous media, that is, the space occupied by trees in the computational domain is regarded as porous media, and using leaf area density (LAD) as a parameter to characterize the three-dimensional porosity;

[0058] (2) The resistance of the tree canopy and trunk to air flow is considered isotropic, that is, when air flows through the two parts, the resistance effect in different directions is the same;

[0059] (3) The thinner trunk branches in the tree canopy are considered as leaves, that is, the influence of the thin branches in the tree canopy on the airflow is not considered;

[0060] (4) The trunk of the tree is regarded as a lower density crown, that is, the low LAD value is used in the calculation of the trunk's influence on wind speed;

[0061] (5) The volume force in the convective motion of air fluids considers the effect of temperature on buoyancy;

[0062] (6) The crown does not take into account the secondary radiation and reflection generated by the leaves inside it;

[0063] (7) The fluid in the study area is considered to be an incompressible Newtonian fluid.

[0064] As a preferred solution of this embodiment, the specific steps of estimating the leaf area density (LAD) of the plant canopy layer using the tree laser point cloud data based on the voxel unit tree canopy analysis method in step S0 and determining the drag coefficient are as follows:

[0065] Step S01: extracting leaf point clouds by separating branches and leaves from the tree laser point cloud;

[0066] Step S02: voxelize the leaf point cloud in the three-dimensional space by the maximum interval and voxel unit size in the X, Y and Z directions, divide the canopy into a number of voxel units, and use the center coordinate of the grid unit to represent the spatial position of each voxel unit;

[0067] Step S03: traverse the voxel units layer by layer, mark the valid voxel units with leaf point clouds in the grid as 1, otherwise mark the invalid voxel units as 0, then calculate the minimum valid plane convex hull of the voxel units marked as 1 according to their position coordinates, count all the voxel unit data in the convex hull, and calculate the LAD value of this layer according to formula (1);

[0068]

[0069] Where ΔH is the thickness of the k-th voxel unit; m h and m h +ΔH represents the index range of the voxel unit of the current altitude layer in the entire canopy voxel unit data; n v (k) and n t (k) are the number of voxels marked as 1 and the number of all voxels in the minimum valid convex hull of the kth layer, respectively; θ is the zenith angle of the laser beam incident direction, and G(θ) is the average projection of the unit leaf area on the plane perpendicular to the laser beam. Based on existing research results, the correction coefficient cos(θ) / G(θ) is preferably fixed to 1.1.

[0070] Step S04: resistance coefficient c of a certain tree species d According to the size of the canopy LAD mean, when the canopy average LAD is less than 1m 2 / m 3 When c d Take the value as 0.3; when the average LAD of the canopy is 1 to 3m 2 / m 3 When c d Take the value as 0.4; when the average LAD of the canopy is greater than 3m 2 / m 3 When c d The value is 0.5.

[0071] As a preferred solution of this embodiment, step S1 includes establishing a plant-air multi-physics field coupling model, including the control component equations, integrating the Reynolds time-averaged method and the Boussinesq hypothesis, and the complete control equations of the numerical model for evaluating the cooling effect of trees are composed of mass, momentum, temperature, and humidity equations, and the nabla operator is introduced. The simplified equation expression is as follows:

[0072] The mass conservation equation:

[0073]

[0074] Momentum conservation equation:

[0075]

[0076] v eff =v+v t Formula (5)

[0077]

[0078] Energy (temperature) equation:

[0079]

[0080] Moisture content (humidity) equation:

[0081]

[0082] The specific physical parameters in the formula are as follows:

[0083] is the average static pressure considering the influence of gravity, in Pa;

[0084] p0 is the fluid pressure in the initial state, in Pa;

[0085] ρ0 is the fluid density in the initial state, in kg / m 3 According to the Boussinesq hypothesis, the ambient atmospheric density is considered to be constant. The present invention takes the air density value of 1.225 kg / m under standard conditions (288.15 K, 101325 Pa) as 1.225 kg / m 3 ;

[0086] g is the gravity acceleration vector, which is (0, 0, -9.81) in this invention. T , the negative sign indicates the direction is vertically downward;

[0087] v eff Represents the effective kinematic viscosity of the fluid, in m 2 / s, which is the fluid kinematic viscosity v and turbulent kinematic viscosity v t The sum of the kinematic viscosity v of air under the above standard state is taken as 1.45×10 -5 m 2 / s;

[0088] F is the part of the volume force change caused by the buoyancy generated by temperature change;

[0089] β is the thermal expansion coefficient of the fluid. In the present invention, the thermal expansion coefficient of air under the above standard state is 3×10 -3 K -1 ;

[0090] is the average ambient temperature in the initial state, in K;

[0091] S is the average strain rate of the velocity tensor;

[0092] is the average velocity vector in m / s;

[0093] is the average air temperature in K;

[0094] Pr, σ v 、Pr t 、 are Prandtl number, Schmidt number, turbulent Prandtl number, and turbulent Schmidt number, which are 0.9, 0.9, 0.7, and 0.7 respectively;

[0095] is the average humidity ratio, that is, the ratio of the mass of water vapor to the mass of dry air;

[0096] S ρ 、S u 、S T 、S w They represent the mass source term, momentum source term, energy source term and moisture content source term in the flow field affected by trees.

[0097] As a preferred embodiment of this embodiment, according to the relevant regulations of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), the moisture content w is defined as the mass of water per kilogram of dry air, in kg / kg. The specific calculation formula is as follows:

[0098]

[0099] The specific physical parameters in the formula are as follows:

[0100] p and p w They are respectively the atmospheric pressure in the environment and the water vapor partial pressure in the air, in Pa;

[0101] R a and R w They are the mass constants of the relative molecules of dry air and water vapor, respectively. According to ASHRAE regulations, their values are 287.055 J / kg / K and 461.524 J / kg / K, respectively.

[0102] According to ASHRAE regulations, the saturated water vapor partial pressure p in the environment wsat The relationship with the relative humidity RH calculation formula is as follows:

[0103]

[0104] The specific physical parameters in the formula are as follows:

[0105] T is the ambient air temperature in K;

[0106] C1~C6 are reference constants. According to the relevant regulations of ASHRAE, they are respectively -5.8002206×10 3 , 1.3914993, -4.8640239×10 -2 , 4.1764765×10 -5 , -1.4452093×10 -8 , 6.5459673.

[0107] As a preferred solution of this embodiment, step S1 includes establishing a plant-air multi-physics field coupling model, including a set of turbulence equations. The turbulence equations use the Realizable k-ε model as the turbulence model for evaluating the cooling effect of trees. The specific equations are as follows:

[0108] k transport equation:

[0109]

[0110] ε transport equation:

[0111]

[0112] The specific physical parameters in the formula are as follows:

[0113] k is turbulent kinetic energy (TKE), unit is m 2 / s 2 ;

[0114] ε is the turbulent dissipation rate (TDR), in m 2 / s 3 ;

[0115] C 2ε , σ k , σ ε are equation constants, and the present invention takes values of 1.92, 1.0, and 1.2 respectively;

[0116] C 1ε 、C μIt will be determined by the velocity gradient during the simulation and the ratio of turbulent kinetic energy to turbulent dissipation rate;

[0117] S k 、S ε They represent the turbulent kinetic energy term and dissipation rate source term in the flow field affected by trees.

[0118] As a preferred solution of this embodiment, the mass source term calculation formula is as follows:

[0119] S ρ =LAD·g w,leaf Formula (17)

[0120] In the formula, the mass source term is required to calculate the water vapor flux caused by transpiration activity of the leaves g w,leaf Parameters, the calculation formula is as follows:

[0121]

[0122] The specific physical parameters in the formula are as follows:

[0123] h c,m is the convective mass transfer coefficient, in s / m;

[0124] o w,leaf and p w are the water vapor pressure above the blade and the blade boundary layer, respectively, in Pa;

[0125] r s It is the resistance of pores to water vapor diffusion, and its unit is s / m.

[0126] Stomatal resistance r s can be modeled as the shortwave radiation flux q rad,sw And the function of the difference between the saturated vapor pressure and the actual vapor pressure in the air D:

[0127]

[0128] Where r s,min is the minimum stomatal resistance. For trees, the minimum value is generally 150s / m. When D is less than 1.2kPa, the third term in formula (19) can be ignored.

[0129] As a preferred solution of this embodiment, the momentum source term calculation formula is as follows:

[0130] S u =-LAD·ρ a c d |u|u formula (20)

[0131] Where c d is the drag coefficient of the tree.

[0132] As a preferred solution of this embodiment, the energy source term calculation formula is as follows:

[0133]

[0134] The specific physical parameters in the formula are as follows:

[0135] q sen,leaf is the sensible heat flux of heat exchange between leaves and air, in W / m 2 ;

[0136] c p is the specific heat capacity of air under constant pressure, which is taken as 1003.5 J / kg / K in the present invention.

[0137] As a preferred solution of this embodiment, the humidity source term calculation formula is as follows:

[0138]

[0139] The calculation formula for the sensible heat flux in the energy source term is as follows:

[0140]

[0141] The specific physical parameters in the formula are as follows:

[0142] 2 means that sensible heat exchange occurs on both surfaces of the blade;

[0143] h c,h is the convection heat transfer coefficient, in W / m 2 / K;

[0144] T leaf and T are leaf surface temperature and air temperature, respectively, in K;

[0145] r a is the dynamic resistance to transpiration in the boundary layer around the leaf, in s / m, and is calculated as follows:

[0146]

[0147] Where D is an empirical constant, and in this invention, 200s is taken. 0.5 / m, l is the characteristic diameter of the leaf, and the leaf morphological parameters of the actual research object, the length L and width W of the leaf, are calculated as follows:

[0148] l=(L 2 +W 2 ) 0.5 Formula (25)

[0149] Leaf surface temperature T leaf The calculation formula is as follows:

[0150]

[0151] As a preferred solution of this embodiment, the turbulence model source term includes a turbulent kinetic energy term and a turbulent dissipation rate source term, and the calculation formula is as follows:

[0152] S k =LAD·ρc d (β p |u| 3 -β d |u|k) Formula (27)

[0153] S ε =LAD·ρc d (β p4 |u| 3 -β d C5|u|ε) formula (28)

[0154] In the formula, coefficients C4 and C5 are empirical constants of the model, β p is the proportional coefficient representing the conversion of average kinetic energy into turbulent kinetic energy, β d To represent the proportional coefficients of the reduction of k and ε caused by plant action, the above model coefficients in the present invention are taken as 0.9, 0.9, 1.0 and 5.1 respectively.

[0155] As a preferred solution of this embodiment, step S1 includes establishing a plant-air multi-physics field coupling model, including a canopy energy balance model characterized by considering a leaf energy steady-state model and constructing heat and mass exchange between the canopy and the air around the energy balance. The present invention abstracts different height layers of the canopy as a whole large leaf. The differences in leaf properties at different height layers are represented by LADs that are non-uniformly distributed along the height. The material energy exchange between the leaf surface and the surrounding air is calculated for the abstract large leaves at different heights. The leaf surface energy steady-state equation is described as:

[0156] q rad,leaf =q lat,leaf +q sen,leaf Formula (29)

[0157] Where q rad,leaf is the net radiation flux absorbed by leaf cells, q lat,leaf ,q sen,leaf are the net latent heat flux and net sensible heat flux, respectively, both in W / m2. The calculation of sensible heat flux is shown in formula (23).

[0158] The latent heat flux calculation formula is as follows:

[0159] q lat,leaf =L v g w,leaf Formula (30)

[0160] The energy coefficient of latent heat of water vaporization L v The value is 2.5×10 6 J / kg, water vapor mass flux g w,leaf The calculation is done using formula (18).

[0161] As a preferred solution of this embodiment, step S1 includes establishing a plant-air multi-physics field coupling model. The net radiation flux of the plant canopy includes shortwave radiation and longwave radiation. The calculation model is as follows:

[0162]

[0163] Where q rad,sw and q rad,lw are the shortwave and longwave radiation fluxes respectively, and l is the distance the radiation passes through the plant canopy.

[0164] The attenuation effect of the plant canopy on shortwave and longwave radiation is modeled using the Beer-Lambert theorem to model the changes in radiation energy within the plant canopy. The calculation formula is as follows:

[0165] q=q0·exp(-β∫LAD dl) Formula (32)

[0166] Where q0 represents the initial long-wave (short-wave) radiation energy reaching the top of the canopy along the propagation path l, β is the attenuation coefficient of the plant canopy to the long-wave (short-wave) radiation, and 0.829 and 0.613 are selected as the attenuation coefficients of plant leaves to the long-wave and short-wave radiation.

[0167] As a preferred solution of this embodiment, step S1 includes establishing a plant-air multi-physics field coupling model, including radiation calculation,

[0168] Calculate the change of net radiation flux in the calculation domain according to the initial condition parameters. The specific steps are as follows:

[0169] Step S1401: Calculate the initial value Rg0 of the shortwave radiation energy flux from the sun's position to the canopy top at different times using the geographic astronomical parameter method, and select the grid cells below the plant height;

[0170] Step S1402: Create a list of sunlight paths for the current grid cells, and search for candidate grid cells from the grid group on the upper layer starting from the current layer, so that the vectors of the two<cells,celle> Vector with sun rays<O,sun> If the angle is within a preset threshold (5° in this invention), the index of the current celle is pushed into the path list of cells.

[0171] Step S1403: Repeat step S1402. If the angle between the vector formed by the new candidate grid and the sun's ray vector is smaller, replace the candidate grid that has been added to the path list until the optimization of the current layer is completed.

[0172] Step S1404: Repeat steps S1402 to S1403 until all grid Lists have been recorded, update the shortwave radiation flux Rgi for all grid cells according to formula (32), and calculate the net shortwave radiation flux Rni absorbed by each plant grid cell except the bottom layer according to formula (31) and the path list Lists;

[0173] Step S1405: Specifically, for the grid cells where Lists is empty in the bottom edge grid of the plant, the attenuation degree of the shortwave radiation flux Rgi at that location over the length of the z side of the grid is directly calculated as the net shortwave radiation flux Rni of this bottom edge grid.

[0174] As a preferred solution of this embodiment, the grid division in step S2 uses a structured hexahedral grid, and the grid is encrypted at the edge of the plant.

[0175] As a preferred solution of this embodiment, when solving the plant-air multi-physics field coupling model in step S3, the finite volume method is used to discretize the differential equations and the semi-implicit algorithm of the pressure-velocity coupling equation group to solve the steady-state velocity field. The difference scheme used in OpenFOAM, that is, the discrete format for solving each physical quantity is set as: t is steadyState, the gradient term is cellLimited Gauss linear, the divergence term is bounded Gauss linearUpwind, and the Laplace term is Gausslinear corrected.

[0176] As a preferred solution of this embodiment, the model of step S3 is solved by using the finite volume method and the semi-implicit algorithm of the pressure-velocity coupling equations to solve the steady-state velocity field. The solution process is as follows: Figure 2 As shown in the figure, the interpolation scheme used in OpenFOAM, that is, the discrete format for solving each physical quantity is set as follows: t is steadyState, the gradient term is cellLimited Gausslinear, the divergence term is boundedGauss linearUpwind, and the Laplace term is Gauss linear corrected. The program implementation process is as follows:

[0177] Step S31: Calculate the shortwave radiation and longwave radiation fluxes under initial conditions and set initial physical parameters;

[0178] Step S32: Calculating the plant canopy energy balance model;

[0179] Step S33: Calculate the mass, momentum, energy, humidity, and plant source terms in the turbulence model;

[0180] Step S34: solving the steady-state flow field by calculating the control equations and the turbulence model;

[0181] Step S35: Repeat steps S32 to S34 until the number of physical quantities solved in the control equations and turbulence model reaches 10 -6 Convergence criteria.

[0182] As a preferred solution of this embodiment, the specific implementation steps of step S32 are as follows:

[0183] Step S321: Calculate the radiation energy absorbed by the plant canopy leaves according to formula (31);

[0184] Step S322: determining the boundary layer dynamic resistance of the plant leaf according to formula (24), and determining its stomatal resistance according to formula (19);

[0185] Step S323: Use the air temperature to make a preliminary estimate of the leaf surface temperature T leaf =T;

[0186] Step S324: calculating the saturated vapor pressure on the leaf surface;

[0187] Step S325: Calculate the latent heat flux according to formula (30) and update the blade temperature according to formula (26);

[0188] Step S326: Iterate step S325 until the blade temperature calculation residual converges to 10 -6 .

[0189] The above embodiment scheme is based on the basic physical quantity parameters of a given plant. By creating a corresponding computational domain geometric model, the plant-air multi-physics field coupling model equation and the temperature and humidity transport equation are numerically solved. The canopy energy balance model is used as the core to simulate the changes in physical quantities in the computational domain. Finally, the temperature field, wind speed field, and relative humidity field data around the plant are obtained, thereby quantitatively analyzing the degree of influence of the plant on the surrounding thermal environment.

[0190] This solution can quantify the impact of plants on the surrounding microclimate: first, based on the laser point cloud data collected in the field, the leaf area density, drag coefficient, tree height, crown height and crown width of the plant canopy are calculated, and a rectangular tree crown expression model is established; by combining the plant transpiration model and the CFD turbulence model, the source term method is used to construct a plant-air multi-physics field coupling model that characterizes the aerodynamic and thermodynamic effects of trees. Using the open source computational fluid dynamics simulation resource OpenFOAM, the finite volume method and the semi-implicit algorithm of the pressure-velocity coupling equation group are used to solve the steady-state velocity field to achieve numerical simulation of the cooling effect of plants; the calculation domain and boundary conditions are set to simulate the temperature field, wind speed field and humidity field data around the plants in specific scenarios, so as to quantitatively analyze the degree of influence of plants on the surrounding thermal environment.

[0191] Existing research on the cooling effects of urban trees is mostly based on measured data, with some employing fluid dynamics models. However, these models are unable to accurately describe the vertical differentiation of plant canopy LAD in real conditions and rely on existing statistical data. This invention utilizes high-precision laser point cloud data, which can represent three-dimensional plant information, to estimate LAD and canopy drag coefficients, improving the reliability of simulation results. This technology can provide a reference for the selection of urban tree species and the layout of green spaces.

[0192] The following is a specific test example to verify, demonstrate and introduce the solution of the present invention:

[0193] Taking urban greening tree species as an example, the process of numerical simulation and quantitative analysis of the cooling effect of plants using the method of the present invention is as follows:

[0194] 1. Determination of tree morphological and structural parameters

[0195] Taking a sycamore tree on a certain campus as an example, a terrestrial laser scanner, Riegl VZ-400, was used to obtain tree point cloud data. The tree height, crown width, canopy height, and height below the branches were estimated based on the point cloud data. Leaf point clouds were then extracted by separating branches and leaves. The point clouds were voxelized and the leaf area density of the canopy was calculated layer by layer. Figure 7-Figure 8 ) to determine the canopy resistance coefficient. The relevant parameters of the sycamore are shown in Table 1.

[0196] Table 1 Related parameters of Platanus

[0197]

[0198] 2. Weather conditions

[0199] The study area is an open area of ​​Planta chinensis. On August 6, 2024, the weather parameters were measured from 8:25 to 18:15. The whole day was sunny and there were few clouds within the measurement range. Stations A, B and C were set up in the study area. Figure 3Station A is open and free of trees and buildings, representing the background outdoor thermal environment. Station B is located directly below the canopy of a sycamore tree, representing the impact of trees on the thermal environment in outdoor settings. Station C is unobstructed. Wind speed, direction, temperature, and humidity were measured using a Kestrel 5500 weather meter at Stations A and B. Solar shortwave radiation energy was measured at Station C using an LI-200R global radiation sensor and LI1500 light data logger. The Kestrel 5500 was mounted on a tripod 1.5 meters above the ground and set to automatically record relevant data every minute. The LI-200R sensor was manually measured and recorded every hour.

[0200] 3. Grid division

[0201] According to the crown shape characteristics of the sycamore, a rectangular canopy model is used to express the canopy. The computational domain grid uses a hexahedron grid. The number of grids in the entire computational domain is 249307. The plant part grid is divided into 41×41×52. The canopy edge is encrypted to ensure that the grid resolution at the edge is about 0.15m ( Figure 4 ).

[0202] 4. Computational domain and boundary conditions

[0203] The computational domain and boundary conditions are as follows Figure 5 The distances from the edge to the inlet, outlet, front and back surfaces, and top boundary surfaces are 10m, 20m, 10m, and 10m, respectively, to ensure sufficient space for airflow development.

[0204] In OpenFOAM, for this working condition, the boundary surface types and boundary conditions of the computational domain are shown in Table 2: the front and rear surfaces are set to symmetric boundary conditions; the outlet surface is applied with a zero gradient boundary condition; the ground surface uses the standard wall function to model the turbulent kinetic energy k and the dissipation rate ε, and the other physical quantities are assigned a fixedValue of 0; the top surface uses a slip boundary condition (silp) for the velocity, and a zero gradient boundary condition for the other physical quantities; according to relevant research on CFD simulation of the atmospheric boundary layer (Richards et al., 1993; Tominaga et al., 2008; Toparlar et al., 2019), the wind speed u, turbulent kinetic energy k, and dissipation rate ε at different heights at the inlet are also calculated by formulas (33) to (35), where the atmospheric friction u * The calculation of formula (36) is as follows:

[0205]

[0206] Where u r is the reference height zr The wind speed at the location.

[0207] Table 2 Boundary type and boundary condition setting information of the sycamore simulation experiment

[0208] Boundary surface Boundary Type Boundary conditions Entrance General boundary patch Numeric type conditions (need to be specified additionally) exit General boundary patch Zero gradient boundary zeroGradient ground rigid wall Wall function conditions top SymmetryPlane Numeric type conditions (need to be specified additionally) Front and back SymmetryPlane Symmetry Plane

[0209] The initial temperature and humidity data at the inlet boundary surface and the initial long- and short-wave radiation flux data within the computational domain were based on observations from August 6, 2024. The data from station A served as the initial boundary conditions, and the data from station B served as the validation data. Because station B, located under the sycamore tree, is located approximately 10° east-southeast of station A in an open area, this experiment selected meteorological data recorded at station A with an initial wind direction of approximately 270° as the initial boundary conditions, as shown in Table 3. Information on the sycamore tree's related parameters is provided in Table 1.

[0210] Table 3 Initial meteorological data for the sycamore simulation case (station A, August 6, 2024)

[0211]

[0212] 5. Implementation of simulation and quantitative simulation of the impact of trees on the thermal environment

[0213] The above model was designed on a Linux system platform using the open source CFD calculation library OpenFOAM and the C++ development language. The average flow rate of moist air was modeled using the RANS equation. The steady-state velocity field was solved using a semi-implicit algorithm for the pressure-velocity coupling equations. The gradient operator used the second-order central difference format, and the convection term used the second-order linear upwind difference format. The convergence criterion for the residual was set to 10 -6 , a computer program implements thermal environment simulation of environmental factors and the cooling effect of trees.

[0214] In terms of initializing the computational domain, this embodiment uses OpenFOAM's third-party grid division open source library blockMeshDG (with dual grid hierarchical encryption function) to implement mesh encryption operations for different physical domain spaces in the computational domain; OpenFOAM's own setFields and the third-party open source library swak4FOAM's funkySetFields tool (which can set multiple expression constraints) are used to implement the setting of the plant domain LAD in the computational domain. The specific operation process is: first, determine the plant domain and air domain of the simulation working condition; then calculate its geometric space coordinate range through Python and create a blockMeshDict file; at the same time, use blockMeshDG to perform grid segmentation according to the blockMeshDict dictionary file and determine the distribution characteristics of the plant canopy LAD; then use Python+setFields+.stl model or python+funkySetFields to set the initial value of the tree canopy LAD in the computational domain.

[0215] For solving linear equations related to various physical quantities obtained by discretizing the governing equations, OpenFOAM provides users with four main matrix solvers: preconditioned conjugate gradient methods (such as PCG, PBiCG, and PBiCGStab), which require preconditioning; the geometric algebraic multigrid method (GAMG); the smoothing solver (smoothSolver), which requires a smoothing method; and the diagonal solver (diagonalSolver). The settings for the matrix solvers for physical quantities in OpenFOAM for this embodiment are shown in Table 4.

[0216] Table 4 Physical quantities to be solved and corresponding matrix solvers

[0217] physical quantity Matrix Solver Smoothing method or preconditioning p_rgh GAMG DIC U smoothSolver Gauss Seidel k / epsilon smoothSolver Gauss Seidel T PBiCG DILU w PBiCG DILU

[0218] 6. Simulation Results

[0219] Table 5 shows the wind speed, air temperature, and relative humidity at station B simulated using the above scheme, along with the measured values. As can be seen from Table 4, the calculated results of the numerical model in this embodiment are all higher than the actual values for wind speed, air temperature, and relative humidity. The relative errors between the simulated results and the measured data for wind speed, air temperature, and relative humidity are approximately 9.17%, 2.73%, and 1.58%, respectively, indicating a high degree of consistency between the simulated results and the field measurements.

[0220] Table 5 Simulation results and measured values (station B)

[0221]

[0222] Figure 6The following is a contour plot of the simulated wind speed, air temperature, and relative humidity at pedestrian height (z = 1.5 m). The results show that trees at this height have a decelerating, cooling, and humidifying effect. As air flows through the trees, wind speed decreases by approximately 0.2 m / s. The lowest air temperature and highest relative humidity at this height occur downstream, with the lowest temperature approximately 0.2°C lower than the initial inflow temperature, and the highest relative humidity approximately 0.9 higher than the initial value.

[0223] Taking into account the errors caused by the measurement equipment and measurement methods, as well as the deviation between the tree model and the actual situation, the simulation results of this embodiment show that the numerical model proposed in the present invention can be considered to have high accuracy, with its error within an acceptable range, and can be used to study the impact of trees on the surrounding thermal environment under different conditions.

[0224] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0225] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which executes the above method when executed by a processor. The storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0226] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0227] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

[0228] The present invention is not limited to the above-mentioned optimal implementation mode. Anyone can derive other forms of numerical simulation and quantitative analysis methods of plant cooling effects under the guidance of the present invention. All equivalent changes and modifications made within the scope of the patent application of the present invention should fall within the scope of the present invention.

Claims

1. A numerical simulation and quantitative analysis method for plant cooling effect, characterized in that: The following steps are involved: Based on the three-dimensional laser point cloud data of trees, the plant canopy morphological parameters are extracted to establish the crown geometry model; Construct a plant-air multi-physics coupling model, including fluid control equations, turbulence models, plant source terms, and canopy energy balance models; The coupled model is solved by computational fluid dynamics methods, and the temperature field, wind speed field and humidity field data around the plants are output to quantitatively analyze the cooling effect.

2. The method for numerical simulation and quantitative analysis of plant cooling effects according to claim 1, characterized in that: The plant canopy morphological parameters include leaf area density (LAD), resistance coefficient, tree height, crown width and crown height, wherein LAD is obtained by voxelizing laser point cloud data and calculating the effective voxel unit ratio; the resistance coefficient is dynamically allocated according to the canopy LAD mean value.

3. The method for numerical simulation and quantitative analysis of plant cooling effects according to claim 1, characterized in that: The plant action source terms include mass source terms, momentum source terms related to canopy LAD and wind speed, energy source terms calculated based on leaf sensible heat and latent heat flux, sensible heat flux determined by the temperature difference between the leaf surface and the air and the boundary layer resistance, and humidity source terms calculated by transpiration water vapor flux.

4. The method for numerical simulation and quantitative analysis of plant cooling effects according to claim 1, characterized in that: The canopy energy balance model abstracts the canopy layer into a single leaf, and solves the leaf surface temperature through the steady-state balance equations of net radiation flux, sensible heat flux and latent heat flux, where the latent heat flux is calculated by the latent heat of water vaporization and transpiration flux; the leaf surface temperature is calculated iteratively until the residual converges.

5. The method for numerical simulation and quantitative analysis of plant cooling effect according to claim 1, characterized in that: The calculation process uses structured grids to divide the computational domain, the grid in the plant canopy area is encrypted, and the divergence term uses a bounded linear upwind format when discretely solving, and the gradient term uses a unit-restricted Gaussian linear format.

6. The method for numerical simulation and quantitative analysis of plant cooling effects according to claim 1, characterized in that: The turbulence model is a Realizable k-ε model, and both the turbulent flow energy term and the dissipation rate source term contain the cubic terms of the canopy LAD and the wind speed.

7. The method for numerical simulation and quantitative analysis of plant cooling effects according to claim 1, characterized in that: When calculating the radiation model, the radiation propagation path is screened by the angle threshold between the sun's ray vector and the grid list, and the net radiation flux of each layer in the canopy is dynamically updated; the Beer-Lambert theorem is used to calculate the radiation attenuation of shortwave and longwave in the canopy.

8. The method for numerical simulation and quantitative analysis of plant cooling effects according to claim 3, characterized in that: The calculation of the mass source term includes a pore resistance calculation model: based on shortwave radiation flux and saturated vapor pressure difference, it is determined by minimum pore resistance and dynamic correction terms.

9. The method for numerical simulation and quantitative analysis of plant cooling effects according to claim 1, characterized in that: The plants are considered as porous media, satisfying the following assumptions: LAD is used to characterize the three-dimensional porosity; the resistance of the canopy and trunk are isotropic; and the influence of canopy branches on airflow is ignored.

10. A numerical simulation and quantitative analysis system for plant cooling effects, characterized in that: include: Data processing module: used to obtain three-dimensional laser point cloud data of trees, extract plant canopy morphological parameters including leaf area density (LAD), drag coefficient, tree height, crown width, and crown height, and establish a crown geometry model. LAD is obtained by voxelizing the laser point cloud data and calculating the ratio of valid voxel units. The drag coefficient is dynamically allocated based on the mean value of the canopy LAD. Model construction module: used to construct a plant-air multi-physics field coupling model, including fluid control equations, turbulence model, plant action source terms including mass source terms, momentum source terms, energy source terms, and humidity source terms, and a canopy energy balance model; Solution module: used to solve the coupled model through computational fluid dynamics method, using structured grid to divide the computational domain, using bounded linear upwind format for divergence term and unit-restricted Gaussian linear format for gradient term when discrete solution; Analysis module: used to output the temperature field, wind speed field and humidity field data around the plant, and quantitatively analyze the cooling effect of the plant.

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