Farmland protection forest net windproof effect rapid evaluation method based on simplified three-dimensional modeling and CFD simulation

Abstract

The invention discloses a method for rapidly evaluating the windproof effect of a farmland protection forest net based on simplified three-dimensional modeling and computational fluid mechanics simulation. The method aims at solving the problems that large-range wind field data is difficult to obtain in existing field observation, and traditional computational fluid mechanics uses a refined farmland protection forest three-dimensional model, so that the calculation cost is too high, and the method cannot be applied to regional scales. The method comprises the following steps: firstly, acquiring spatial distribution of a forest network and three-dimensional structure parameters and layered leaf area density of a single forest belt by using an unmanned aerial vehicle oblique photography and rapid canopy measurement technology; then, in a geographic information system and engineering design software, a single forest belt is converted into a polyhedral structure with internal attributes, and a forest network three-dimensional model is constructed; further, efficient numerical simulation of the wind field around the forest network is realized through a computational fluid mechanics method; and finally, a target height wind speed distribution diagram is extracted from a result wind field obtained through simulation, an effective protection area is discriminated through a grid binarization method, and the forest network protection efficiency is rapidly evaluated.

Description

Technical Field

[0001] This invention belongs to the technical field of windbreak effect assessment of farmland shelterbelts, specifically involving a rapid assessment method for the windbreak effect of farmland shelterbelt networks based on simplified three-dimensional modeling and CFD simulation. Background Technology

[0002] Constructing shelterbelts for farmland is a crucial engineering measure for maintaining the stability of farmland ecosystems, preventing wind and sand disasters, improving the microclimate for crop growth, and ensuring stable and increased grain production. Farmland shelterbelts use linearly planted belts as their basic structural unit, forming a network around farmland on a landscape scale. The fence-like structure of the belts creates local airflow obstruction and dissipation; the combined effect of multiple belts creates an effective protective area with significantly reduced wind speed within the network. Conducting scientific, rapid, and comparable windbreak effect assessments of farmland shelterbelts (especially network shelterbelts) is an important prerequisite for carrying out network planning, design, effectiveness diagnosis, and management.

[0003] Currently, the assessment of the windbreak effect of farmland shelterbelts mainly relies on field observations, wind tunnel experiments, and computational fluid dynamics (CFD) numerical simulations. Field observations are limited by environmental controllability and labor costs, making it difficult to obtain complete and comparable wind fields. While wind tunnel experiments offer greater controllability, they are constrained by data acquisition density and experimental space. CFD methods, as a means of numerically simulating fluid motion based on the laws of physics, have advantages such as rapidly obtaining complete wind fields and being unrestricted by scene or manpower, and are therefore widely used in wind field research on shelterbelts.

[0004] However, current CFD-based wind field studies of farmland shelterbelts mainly employ methods such as lidar point cloud reconstruction to create high-fidelity simulation models of the shelterbelt structure (e.g., explicit characterization of branch and leaf structure or high-precision 3D reconstruction) and simulate the wind field to obtain a near-realistic picture of the shelterbelt's wind field. However, this method significantly increases computational scale and solution costs, making it difficult to simulate regional-scale shelterbelt networks. Furthermore, acquiring and processing lidar data is cumbersome, highly specialized, and requires expensive equipment, hindering its widespread adoption at the grassroots level. Considering the relatively uniform age, even spacing, and simple geometric shape of farmland shelterbelts, adopting a simplified 3D modeling strategy combined with internal aerodynamic parameterization is not only feasible but also a necessary approach to achieve rapid and accurate simulation of wind fields at the shelterbelt network scale. At the same time, existing simulation technologies remain at the level of displaying wind field results, lacking a general process for converting simulation results into quantitative indicators such as shelterbelt efficiency that can directly serve shelterbelt planning and management.

[0005] Therefore, in order to overcome the problems of complex implementation process, high cost and low efficiency of existing technologies, there is an urgent need for a rapid assessment method of forest network wind field that combines simplified 3D modeling and CFD simulation, and a standardized and procedural technical system to achieve rapid assessment of the windbreak effect of farmland shelterbelts at the regional scale, and to provide efficient technical tools and scientific decision support for the planning, rating and optimization management of forest networks. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a rapid evaluation method for the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and computational fluid dynamics (CFD) simulation.

[0007] The specific details of the invention are as follows:

[0008] A rapid evaluation method for the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and CFD simulation includes the following steps:

[0009] (1) Acquisition of forest belt structure parameters: Measure the spatial location, orientation, length, width, and height of each forest belt within the target area; and obtain the layered leaf area density (LAD) of the forest belt, which characterizes the canopy structure. Preferably, point cloud data of the target area is acquired using UAV oblique photography, and the forest belt point cloud is separated by a point cloud classification algorithm to extract the geometric parameters of the forest belt; at the same time, the layered leaf area density (LAD) of the forest belt is acquired using a UAV equipped with a fisheye lens.

[0010] (2) Construction of a simplified 3D model of the forest network: Based on the forest belt structure parameters obtained in step (1), each forest belt is simplified into a 3D geometric body containing external geometric and internal structural attributes, and combined to form a 3D model of the forest network, thereby obtaining a closed entity or equivalent geometric model suitable for numerical simulation. Preferably, in a geographic information system (GIS) or engineering design (CAD) software, geometric polyhedra or cuboid equivalent bodies representing each forest belt are constructed according to the length, width and height parameters of the forest belt, and combined to form a 3D model of the forest network; the model is exported to a general 3D exchange format for solidification and optimization, the model is converted into a closed entity object, and finally exported as a 3D model file of the forest network that can be recognized by CFD software;

[0011] (3) CFD numerical simulation calculation: The three-dimensional model of the forest network obtained in step (2) is imported into the fluid dynamics numerical simulation environment, the boundary conditions of the computational domain are set and the computational grid is generated, and the turbulence model is selected to solve the steady-state or quasi-steady-state wind field; wherein, the corresponding area of ​​the forest belt is set as the porous medium area or the volume force source area to characterize the canopy aerodynamic resistance; the single forest belt is layered according to the height along the vertical direction to obtain at least three layers of canopy layer structure; for each layer, the dynamic coefficient of aerodynamic resistance of the layer is determined according to the leaf area density (LAD) of the layer. Momentum source terms: The momentum source terms of each layer are assigned to the computational units in the CFD model corresponding to the spatial range of that layer, so that the forest belt area introduces a resistance term related to local velocity during the solution process to characterize the dissipation effect of the canopy on airflow; wherein, the resistance of each layer increases with the increase of the leaf area density (LAD) of the layer, and the resistance of different layers characterizes the influence of the vertical non-uniform structure of the canopy on airflow; when the residuals of each control equation are lower than the preset threshold, and the wind speed change at the key monitoring point is less than the preset tolerance and tends to stabilize, the calculation is considered to be converged;

[0012] (4) Visualization of windbreak effect and evaluation of protection efficiency: Extract the horizontal wind speed distribution map at the target height from the simulation results, divide the wind speed distribution map into zones according to the preset protection effectiveness criteria to obtain the effective protection zone and the unprotected zone, and calculate the percentage of the effective protection zone to obtain the overall protection efficiency of the forest network. Preferably, the raster data of the wind speed distribution map at the target height is binarized, and the percentage of the effective protection zone is calculated by counting the number of pixels in the effective protection zone and the unprotected zone.

[0013] Preferably, in step (1), the point cloud data is in a standard point cloud format, including but not limited to LAS, LAZ, PLY or equivalent formats, and the forest belt point cloud is separated by a point cloud classification algorithm, thereby extracting the forest belt geometric parameters.

[0014] Preferably, the extraction of forest belt geometric parameters specifically includes: calculating the spatial center coordinates, orientation angle, length, width, and average height of the forest belt based on the forest belt point cloud.

[0015] Preferably, in step (2), the step of constructing simplified polyhedral or cuboid equivalent models of each forest belt in the Geographic Information System (GIS), combining them into a three-dimensional model of the forest network, and then exporting them into a general three-dimensional exchange format, specifically involves: using the 3D modeling tools of the GIS platform, constructing geometric polyhedra representing each forest belt based on the length, width, and height parameters of the forest belt, combining them into a three-dimensional model of the forest network, and then exporting them into a general three-dimensional exchange format.

[0016] Preferably, in step (2), the solidification and optimization are performed in the engineering design software (CAD), specifically including: importing the forest network model in the general three-dimensional exchange format, converting it into a closed solid object, and finally exporting it as a forest network three-dimensional model file in a standard format that can be recognized by CFD software.

[0017] Preferably, in step (3), when the forest belt area is set as a porous medium area or a volumetric force source term area, a layered parameterization method based on layered leaf area density (LAD) is used to characterize the canopy resistance: a single forest belt is divided into at least three layers along the vertical direction, and the aerodynamic resistance of each layer is quantified respectively; the momentum source term of each layer is positively correlated with the leaf area density (LAD) of the corresponding layer, and is assigned to the calculation unit corresponding to the spatial range of the layer, so that the forest belt area introduces a resistance term related to local flow velocity in the solution process, thereby reflecting the influence of the vertical non-uniform structure of the canopy on airflow attenuation.

[0018] Preferably, in step (3), the size of the calculation domain is set to satisfy the following: the length along the main direction is at least 20 times the average height of the forest belt to fully cover the target forest network and leave a buffer zone; the horizontal width is not less than 11.5 times the average height of the forest belt; and the vertical height is not less than 5 times the average height of the forest belt.

[0019] Preferably, in step (3), the boundary condition setting includes: the top surface and outlet of the computational domain adopt open boundary conditions, the side surface adopts non-penetrating free slip (non-shear) boundary conditions, and the bottom surface is set as a wall with a specific roughness. The wind speed profile used at the inlet is a logarithmic wind speed profile; when dividing the computational grid, the forest belt area and the near-surface layer are densified to improve the computational accuracy.

[0020] Preferably, in step (4), the overall protection efficiency is calculated as follows: based on the number of pixels in the effective protection area and the unprotected area, the percentage of the effective protection area is calculated.

[0021] The benefits of this invention are as follows: by representing farmland shelterbelts with simplified three-dimensional geometry and converting layered leaf area density (LAD) into layered momentum source terms that can be directly solved by CFD models through parametric algorithms, a standardized process of "multi-source data acquisition - simplified modeling - numerical simulation - rasterization partitioning and pixel statistical quantization" is formed. While ensuring reasonable accuracy of regional scale wind field simulation, the modeling complexity and computational cost are significantly reduced. This enables rapid and quantitative assessment and visualization of the windbreak effect of farmland shelterbelt networks at the landscape scale, providing efficient and practical technical means for forest network planning and design, effectiveness diagnosis and structural optimization. Attached Figure Description

[0022] Figure 1 Flowchart of the invention method;

[0023] Figure 2 : Point cloud data of farmland shelterbelts and simplified modeling based on geometric parameters;

[0024] Figure 3 : Schematic diagram of the construction of a 3D model of the forest network within the CFD computational domain;

[0025] Figure 4 : Schematic diagram of CFD numerical simulation conditions and computational grid settings;

[0026] Figure 5 : Schematic diagram of wind field simulation results and protection efficiency calculation of farmland shelterbelts. Detailed Implementation

[0027] The present application will be further described in detail below with reference to embodiments, comparative examples and performance test results. These embodiments should not be construed as limiting the scope of protection claimed in this application.

[0028] In the following description of the embodiments, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0029] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0030] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0031] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0032] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0033] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0034] Example 1

[0035] Reference Appendix Figure 1 The implementation of the method described in this invention mainly includes four steps.

[0036] Step 1: Acquisition of Forest Belt Structure Parameters. Aerial photography of the target farmland forest network area is conducted using a drone equipped with an oblique camera, generating standard format point cloud data (such as LAS, LAZ, PLY, etc.). Point cloud clusters representing the forest belts are separated using a point cloud classification algorithm, and key external geometric parameters of each forest belt are extracted, including its spatial center coordinates, orientation, length, width, and average height. Simultaneously, to obtain parameters representing the internal structure of the forest belts, the overall leaf area index (LAI) is measured using a ground-based canopy analyzer. A drone equipped with a fisheye lens is used to hover and photograph the forest belts at different heights within the belts, and the vertical leaf area density (LAD) variation data of the forest belts is obtained through professional software analysis. The average height H of each forest belt is divided into N layers vertically, with a layer thickness Δz = H / N. The leaf area density of the nth layer is then denoted as LAD. n ,but:

[0037]

[0038] Among them, LAD n Unit m 2 / m 3 The unit of LAI is m. 2 / m 2 .

[0039] Step Two: Constructing a Simplified 3D Model of the Forest Network. In Geographic Information System (GIS) software (such as ArcGIS Pro), based on the length, width, and height parameters of each forest belt obtained in Step One, use 3D modeling tools to create a simplified geometric multipatch or cuboid equivalent for each forest belt. Combine these to form a simplified 3D model of the forest network and export it to a common 3D exchange format (such as OBJ). Subsequently, import this model into engineering design software (CAD, such as AutoCAD) for model repair, ensuring it is a closed solid object. Finally, export the computational domain and target model to an STL format file, which is commonly supported by CFD software.

[0040] Step 3: CFD numerical simulation calculation. Use professional computational grid generation software (such as Fluent Meshing) to read the 3D model of the forest network, draw the computational domain boundary according to the forest network morphology, generate the computational grid, and automatically diagnose the grid quality. The dimensions of the computational domain must meet the following requirements: the length along the main direction must be more than 20 times the average height of the forest belt, the lateral width must be no less than 11.5 times the average height of the forest belt, and the vertical height must be no less than 5 times the average height of the forest belt.

[0041] Import the generated computational domain, computational mesh, and STL-formatted 3D forest network model into computational fluid dynamics software (such as OpenFOAM, Fluent, or PHOENICS). Set the top and outlet surfaces of the computational domain as open boundaries, the sides as free-slip (no shear, impenetrable) boundaries, and the bottom surface as a wall with a certain degree of roughness. Set the inlet boundary as a logarithmic wind speed profile conforming to the characteristics of the atmospheric boundary layer, with the following expression:

[0042]

[0043] Among them, u * Let z be the frictional velocity, K be the Karman constant (taken as 0.4), and z0 be the surface roughness length.

[0044] The 3D model of the forest belt is set as a plant entity (Foliage Object) with aerodynamic drag characteristics. The leaf area density (LAD) of each layer is set as an internal attribute of the forest belt, and the drag coefficient C is uniformly set. d Value. Specifically: The leaf area density of the nth layer in the vertical direction of a simplified model of a forest belt is LAD. n The drag effect on the airflow is transmitted through the momentum source term S. Ui This can be represented as follows:

[0045]

[0046] Where ρ is the air density, C dU is the drag coefficient, |U| is the magnitude of the wind speed vector, and U i It is the velocity component in direction i. This source term is applied to the canopy volume region (which can be equivalently realized in Foliage / porous media / volume force region) to characterize the canopy's resistance to airflow dissipation.

[0047] The standard k-ε turbulence model was selected for steady-state calculation. The solution parameters included the number of iterations, convergence criterion, and relaxation index. After the simulation converged, the three-dimensional wind field distribution in the entire computational domain was obtained.

[0048] Step 4: Visualization of wind protection effect and evaluation of protection efficiency. After the simulation is completed, extract the height H above the ground target. target A wind speed distribution map is plotted on a horizontal cross-section at a location such as the standard wind measurement height or the height of the target crop, and then rasterized. A wind speed threshold V is set to determine the effectiveness of the protection. threshold (For example, the critical wind speed at which a preset wind reduction ratio is achieved, or the critical wind speed at which the target crop is damaged.) Threshold segmentation is performed on the wind speed values ​​V(x,y) at each grid point on the wind speed distribution map, and image binarization is completed: if V(x,y) ≤ V threshold Then assign a value of 1, which represents the effective protection zone; if V(x,y) > V threshold The value is assigned to 0, representing the unprotected area. Based on the number of pixels assigned a value of 1 (N1) and the total number of pixels assigned a value of 0 (N0) within the cultivated land area mask, the overall protection efficiency E of the entire forest network under this scenario is calculated, which is the proportion of the effective protected area to the total cultivated land area.

[0049]

[0050] The efficiency value and the visualized protected area distribution map constitute the final evaluation result.

[0051] Example 2

[0052] This paper takes a farmland shelterbelt area in a certain area of ​​Horqin Right Middle Banner as an example to explain in detail the implementation process of this method.

[0053] The first step is to obtain forest belt structure data. (See attached document) Figure 2 In sections a and b, oblique photogrammetry was performed on area 2 of Example 2 using a DJI Mavic 3M drone. Aerial triangulation modeling was then performed, and LAS point cloud data was exported. The point clouds were then classified in ArcGIS Pro software, and the forest belt point clouds were separated as shown in the attached figure. Figure 2 As shown in c, individual forest belts were separated from the classified point cloud data, and their spatial location, length, width, and average height were extracted. Simultaneously with the drone operation, the average leaf area index of the forest belts was measured using a canopy analyzer, and the layered leaf area density (LAD) data of the forest belts was acquired using a fisheye lens drone.

[0054] The second step is to construct a three-dimensional model of the forest network. (See attached reference.) Figure 2 d. In ArcGIS Pro, use the "Multipatch" tool to create simplified geometric models of each forest belt based on the extracted geometric parameters. Combine these models to obtain a 3D model of the forest network, and export it as an OBJ file. Import the OBJ file into AutoCAD software, use the "CONVTOSOLID" command to convert the model into a closed solid, check and optimize it, and then export it as an STL file.

[0055] The third step is CFD numerical simulation calculation. (See attached reference.) Figure 3 and 4 Import the STL model into the PHOENICS software.

[0056] 1. Parametric settings: Based on the measured leaf area density (LAD) data of each forest belt, the momentum source term for each forest belt is calculated and assigned using the aforementioned formula, and the drag coefficient C is set. d =0.5.

[0057] 2. Computational Domain Setup: Based on the size constraints described in the claims, the computational domain size is set. Considering the average height of the forest belt is approximately 20m, the vertical height of the computational domain is set to Z=120m; the length along the main flow direction is set to X=1000m; and the lateral width is set to Y=1800m. A high-quality computational mesh is generated and refined around the forest belt using a Log function, with the minimum mesh size controlled at 0.1m. 3. Boundary Conditions: A logarithmic wind speed profile is used at the inlet, with the reference wind speed at Z=1.5m set to 7.0m / s; the bottom surface roughness z0 is set to 0.1m; the top and outlet are set as open boundaries, and the sides are set as free-slip (no shear, impenetrable) boundaries.

[0058] 4. Solution: The standard k-ε turbulence model is selected, the relaxation factor is set to 0.3, the maximum number of iterations is set to 200, and the convergence condition is set to calculate the iteration until the residual of each governing equation is lower than the preset threshold of 0.01%, and steady-state simulation calculation is performed until convergence.

[0059] Step 4: Spatial visualization of the protective effect of the forest network and assessment of its protective efficiency. (See attached reference) Figure 5 After solving the problem, the wind speed distribution map at a height of 1.5m was extracted and then rasterized (see attached image). Figure 5 a). Using 5.6 m / s (i.e., a 20% reduction in inflow wind speed) as the critical wind speed value, areas with wind speeds below 5.6 m / s were designated as effective protection zones, and areas with wind speeds above 5.6 m / s were designated as unprotected zones. The adjusted wind speed distribution map is shown in the attached map. Figure 5 (b) The colored areas represent the protected areas, and the blank areas represent the unprotected areas. The image is then binarized (see attached diagram). Figure 5c), where the effective protection area is black with a pixel value of 1, and the unprotected area is white with a pixel value of 0. The number of pixels with values ​​of 1 and 0 is counted in units of forest network grid, and the protection efficiency is calculated according to the formula in the technical specification. The final overall protection efficiency of the forest network is obtained, and the results are shown in Table 1.

[0060] Table 1. Calculation results of forest network structure information and protection efficiency in Example 2

[0061]

[0062] Example 3

[0063] This embodiment uses a planned new shelterbelt network in an agricultural area of ​​the Huang-Huai-Hai Plain as an example to demonstrate the application of the present invention in the pre-assessment stage of the planning phase.

[0064] The first step is parameter setting and simplified model construction. The planned forest network is a rectangular grid, consisting of a north-south oriented main forest belt (spaced 400m apart) and an east-west oriented secondary forest belt (spaced 300m apart). The designed height of the main forest belt is H1=18m, and the width is W1=10m; the designed height of the secondary forest belt is H2=12m, and the width is W2=6m. The selected tree species is fast-growing poplar. Based on the typical structural parameters of this tree species after it has matured in the local area, the leaf area index (LAI1) of the main forest belt is set to 3.2, and the LAI2 of the secondary forest belt is set to 2.5. Its stratified leaf area density (LAD) is approximated by a normal distribution, with the maximum value located at Z=0.6H. Based on the above geometric parameters, a group of cuboids is directly created in CAD software to construct a simplified 3D model of the planned forest network covering an area of ​​1200m×900m, and exported as an STL file.

[0065] The second step is CFD simulation setup and calculation. The model is imported into ANSYS Fluent software. The computational domain size is set to X=3000m (downwind), Y=1800m, Z=250m. The prevailing wind is set to east. A logarithmic law wind speed profile is used at the inlet. A Realizable k-ε turbulence model is employed. Aerodynamic properties are assigned to the forest belt model according to the technical specifications. Taking the middle of the main forest belt canopy (Z=0.6H1=10.8m) as an example, the LAD of this layer is set to 1.5m² / m³, and ρ=1.3kg / m³. 3 C d =0.5, set the initial wind speed U at this location. ≈6m / s. After meshing and local refinement, the total number of meshes is approximately 3.5 million. The relaxation factor is set to 0.3, the maximum number of iterations is 200, and the convergence condition is calculated until the residuals of each governing equation are lower than a preset threshold of 0.01%. Steady-state simulation calculations are then performed until convergence.

[0066] The third step is result analysis and protection efficiency assessment. After calculation convergence, the wind speed distribution map at the crop canopy height Z=2m is extracted. A wind speed below 7m / s (approximately 70% of the inlet reference wind speed) is used as the effective protection threshold. After binarization and statistical analysis, the overall protection efficiency of the planned forest network under easterly wind conditions is 84.5%. Further analysis shows that the protection efficiency behind the first row of main forest belts is 76%, and from the second row onwards, the protection efficiency within the grid stabilizes between 86% and 88%. This simulation result quantitatively reveals the distribution pattern of windbreak effectiveness under this planning scheme, indicating that the protection capacity of the first row of forest belts is relatively weak, providing accurate data for optimizing the forest belt structure or adjusting the layout.

[0067] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A rapid evaluation method for the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and computational fluid dynamics (CFD) simulation, characterized in that, Includes the following steps: (1) Obtaining forest belt structure parameters: measuring the spatial location, orientation, length, width and height of each forest belt in the target area; And obtain the leaf area density (LAD) of forest belt layers that characterize the canopy structure; (2) Construction of simplified three-dimensional model of forest network: Based on the geometric parameters, each forest belt is simplified into a three-dimensional geometric body and combined to form a three-dimensional model of forest network, so as to obtain a closed entity or equivalent geometric model suitable for numerical simulation; (3) CFD numerical simulation calculation: The three-dimensional model of the forest network is imported into the fluid dynamics numerical simulation environment. The corresponding area of ​​the forest belt is set as a porous medium area or a volume force source term area to characterize the canopy aerodynamic resistance. Based on the layered leaf area density (LAD), the forest belt is divided into multiple layers along the vertical direction through a parameterized algorithm, and each layer is assigned a corresponding layered momentum source term. The boundary conditions of the computational domain are set and a high-quality computational grid is generated. A turbulence model is selected and the steady-state or quasi-steady-state wind field is solved. (4) Visualization of windbreak effect and evaluation of protection efficiency: Extract the wind speed distribution map at the target height from the simulation results, and divide the wind speed distribution map into zones according to the preset protection effectiveness criteria to obtain the effective protection zone and the unprotected zone. Calculate the proportion of the effective protection zone to obtain the overall protection efficiency of the forest network.

2. The method for rapid evaluation of the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and CFD simulation according to claim 1, characterized in that, In step (1), the point cloud data is obtained based on oblique photography. The point cloud data is in a standard point cloud format, including but not limited to LAS, LAZ, PLY or their equivalent formats. The point cloud of the forest belt is separated by a point cloud classification algorithm, and then the geometric parameters of the forest belt are extracted.

3. The method for rapid evaluation of the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and CFD simulation according to claim 2, characterized in that, The extraction of forest belt geometric parameters specifically includes: calculating the spatial center coordinates, orientation angle, length, width, and average height of the forest belt based on the forest belt point cloud.

4. The method for rapid evaluation of the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and CFD simulation according to claim 1, characterized in that, In step (2), the construction of the forest network three-dimensional model specifically involves: in geographic information system (GIS) and engineering design (CAD) software, based on the length, width and height parameters of the forest belt, constructing geometric polyhedra or cuboid equivalents representing each forest belt, combining them into a forest network three-dimensional model and then exporting it into a general three-dimensional exchange format.

5. A rapid evaluation method for the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and CFD simulation, as described in claim 4, is characterized in that... In step (2), the three-dimensional model of the forest network is solidified and optimized, specifically including: importing the model in the general three-dimensional exchange format and converting it into a closed entity object.

6. The method for rapid evaluation of the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and CFD simulation according to claim 1, characterized in that, The parameterization algorithm in step (3) is: to divide a single forest belt into vertical layers according to height, so as to obtain at least three layers of canopy; For each layer, the momentum source term of the aerodynamic drag of that layer is determined based on the leaf area density (LAD) of that layer; the drag of each layer is parameterized into the calculation unit corresponding to the spatial range of that layer in the CFD model, so that the forest belt area introduces a drag term related to the local flow velocity in the solution process, so as to characterize the dissipation effect of the canopy on the airflow.

7. A rapid evaluation method for the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and CFD simulation, as described in claim 1 or 6, characterized in that... In step (3), the size of the calculation domain is set to meet the following requirements: the length along the main direction is at least 20 times the average height of the forest belt to fully cover the target forest network and leave a buffer zone; the horizontal width is not less than 11.5 times the average height of the forest belt; and the vertical height is not less than 5 times the average height of the forest belt.

8. A rapid evaluation method for the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and CFD simulation, as described in claim 1, is characterized in that... In step (3), the boundary conditions are set as follows: the inlet adopts a wind speed profile based on atmospheric boundary layer theory, the top surface of the computational domain and the outlet adopt open boundary conditions, the side adopts free sliding, that is, a boundary without shear and impenetrable, and the bottom surface is set as a wall with roughness.

9. A rapid evaluation method for the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and CFD simulation, as described in claim 8, is characterized in that... The wind speed profile used at the entrance is a logarithmic wind speed profile.

10. A rapid evaluation method for the windbreak effect of farmland shelterbelt networks based on simplified 3D modeling and CFD simulation, as described in claim 1, is characterized in that... In step (4), the overall protection efficiency is calculated as follows: based on the protection effectiveness criterion, the target height wind speed distribution grid data is binarized and divided into an effective protection area and an unprotected area. The percentage of the effective protection area is calculated by counting the number of pixels in the effective protection area and the unprotected area.

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