Geometric modeling and grid dividing method and device for high slope platform of wind turbine generator

Abstract

The invention discloses a geometric modeling and grid dividing method and device for a high slope platform of a wind turbine generator, and belongs to the technical field of wind power generation. The wind turbine generator high slope platform geometric modeling and mesh generation method comprises the steps that a CAD topographic map containing wind turbine generator platform design information and background DEM data covering a site area are acquired, and fused topographic data are generated; based on the fused topographic data, constructing a high slope platform geometric model comprising an excavation face and a transition area through a cosine control equation and Boolean operation; and based on the high slope platform geometric model, a four-level layered adaptive encryption strategy is adopted to generate a three-dimensional computational volume grid for computational fluid mechanics analysis. According to the technical scheme, the CAD topographic map and the background DEM are fused to eliminate data deviation, the accurate high slope model is built through regular sampling, elevation correction and operation, then the CFD grid is generated through four-stage encryption, the accuracy and efficiency are both considered, and the problems that traditional modeling is rough and grid division is low in efficiency are solved.

Description

Technical Field

[0001] This invention relates to the technical field of wind power generation, and in particular to a method and apparatus for geometric modeling and grid division of a high slope platform for a wind turbine. Background Technology

[0002] In the construction of wind farms in complex terrain, due to restrictions such as ecological red lines, forest land, and administrative boundaries, some wind turbine sites have to be located on high slopes. The formation of high slope platforms is usually accompanied by large-scale excavation and filling operations, which alters the original landform and interferes with key wind resource parameters (such as wind speed, turbulence intensity, and inflow angle), leading to reduced unit operating safety and decreased power generation efficiency. This has become a key issue restricting the effectiveness of wind farms in complex terrain.

[0003] Existing technologies for handling high slope platforms have significant shortcomings and fail to meet engineering requirements. First, the geometric modeling is coarse; traditional methods do not accurately simulate the geometric characteristics of high slopes after platform excavation, leading to significant discrepancies between subsequent computational fluid dynamics (CFD) analysis results and actual wind resource conditions. Second, mesh generation is inefficient; general-purpose meshing tools (such as OpenFOAM's snappyHexMesh) lack sufficient mesh refinement capabilities for key local areas of high slopes (such as platform edges and slope toes), making it difficult to balance computational accuracy and efficiency. Third, there is a lack of targeted workflows; no systematic solutions have been proposed for the special boundaries of high slope platforms (such as steep slopes and platform-slope transition zones). These problems urgently need to be addressed to improve the reliability of high slope wind resource assessment. Summary of the Invention

[0004] This invention provides a method and apparatus for geometric modeling and mesh generation of high slope platforms for wind turbines, which solves the problem of geometric modeling and mesh generation of high slope platforms for wind turbines under complex terrain.

[0005] According to a first aspect of the present invention, a method for geometric modeling and mesh generation of a high slope platform for a wind turbine is provided, the method comprising: Obtain a CAD topographic map containing wind turbine platform design information and background DEM data covering the site area. Perform coordinate mapping and edge smoothing fusion processing on the CAD topographic map and the background DEM data to generate fused topographic data. Based on the fused terrain data, regularized grid sampling and elevation correction are performed in the platform area and the outer buffer zone. A geometric model of the high slope platform, including the excavation face and the transition zone, is constructed by using cosine control equations and Boolean operations. Based on the geometric model of the high slope platform, the boundary conditions of the computational domain are set, and a four-level hierarchical adaptive densification strategy is used to generate a three-dimensional computational volume mesh for computational fluid dynamics analysis.

[0006] In one embodiment, the coordinate mapping and edge smoothing fusion process of the CAD topographic map and the background DEM data includes: Extract elevation point clouds from CAD topographic maps and determine the core area boundary. Extend the core area boundary outward by a preset distance and calculate the coordinate range of the extended boundary to crop the background DEM data. Spatial mapping is performed between the CAD elevation point cloud and the cropped background DEM data. The elevation offset between the two is calculated and the reference plane is aligned to generate a preliminary stitching shape. The edges and seams of the preliminary spliced ​​terrain are smoothed to eliminate splicing marks, thereby obtaining the fused terrain data with continuous elevation transition.

[0007] In one embodiment, constructing the geometric model of the high slope platform based on fused terrain data includes: Within the extended area of ​​the platform that integrates terrain data, a set of regular two-dimensional sampling points with a first resolution is generated, and random perturbations are applied to the sampling point set to simulate natural landform features; Using the bicubic interpolation algorithm, the background elevation value corresponding to the sampling point set is calculated based on the background DEM data to generate an initial point cloud containing background terrain features. Polygon inclusion detection is performed on each point in the initial point cloud to identify points located within the platform design range, and their elevations are forcibly corrected to the design platform elevation.

[0008] In one embodiment, constructing the geometric model of the high slope platform further includes elevation correction of the platform-slope transition zone, including: Construct a line string object representing the platform boundary, and for sampling points located outside the platform design area, calculate the shortest horizontal distance from the sampling point to the platform boundary and the current slope; When the current slope is detected to exceed the preset design value, the elevation of the sampling point is adjusted according to the elevation correction formula to construct a slope shape that gradually changes from the edge of the platform to the background terrain. The corrected elevation data is processed by Gaussian filtering to eliminate local elevation abrupt changes, and the processed point set is converted into an unstructured mesh through triangulation and exported as an STL format file as the geometric model of the high slope platform.

[0009] In one embodiment, generating a three-dimensional computational volume mesh using a four-level hierarchical adaptive encryption strategy includes: A basic background mesh of level 1 is constructed, using a Cartesian hexahedron structure, and the global basic resolution is set to the second resolution to form the initial computational domain framework. Level 2 initial densification is applied to the entire computational domain to improve the overall grid resolution and adapt to the flow characteristics; A densification zone was defined for the platform-slope transition area, and the control parameters were further densified at level 3 to achieve local refinement of the steep slope and platform edge. An additional second-level densification of level 4 is carried out in the wind turbine location and core flow field area, so that the cumulative densification level of the core area reaches level 4 and the grid resolution reaches the third resolution. The value of the third resolution is less than the value of the second resolution.

[0010] In one embodiment, it also includes: In the slope area, a multi-layered prismatic mesh is generated as a boundary layer, and the thickness of each layer is set to increase geometrically. The flow field is monitored in real time during the calculation process. When the velocity gradient modulus exceeds the threshold, dynamic mesh refinement is triggered to automatically refine the flow separation or turbulent areas caused by terrain.

[0011] According to a second aspect of the present invention, a device for geometric shaping and grid division of a high slope platform for a wind turbine is provided, comprising: The acquisition module is used to acquire a CAD topographic map containing wind turbine platform design information and background DEM data covering the site area, and to perform coordinate mapping and edge smoothing fusion processing on the CAD topographic map and the background DEM data to generate fused topographic data. The construction module is used to perform regularized grid sampling and elevation correction in the platform area and the outer buffer zone based on the fused terrain data, and to construct a geometric model of the high slope platform including the excavation face and the transition zone through cosine control equations and Boolean operations. The generation module is used to set the boundary conditions of the computational domain based on the geometric model of the high slope platform, and to generate a three-dimensional computational volume mesh for computational fluid dynamics analysis using a four-level hierarchical adaptive encryption strategy.

[0012] According to a third aspect of the present invention, an electronic device is provided, comprising: a communication interface, a processor, and a memory; The memory is used to store program instructions, which, when executed by the processor connected to the memory via the communication interface, implement any of the above-mentioned methods for geometric modeling and mesh generation of high slope platforms for wind turbines.

[0013] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, which, when executed by a computer (e.g., a processor in a computer), implement any of the above-described methods for geometric modeling and mesh generation of a high slope platform for a wind turbine.

[0014] In summary, this invention provides a method and apparatus for geometric modeling and mesh generation of a high slope platform for a wind turbine. The method includes: acquiring a CAD topographic map containing design information of the wind turbine platform and background DEM data covering the site area; performing coordinate mapping and edge smoothing fusion processing on the CAD topographic map and the background DEM data to generate fused topographic data; based on the fused topographic data, performing regularized mesh sampling and elevation correction in the platform area and the outer buffer zone; constructing a geometric model of the high slope platform including the excavation face and the transition zone through cosine control equations and Boolean operations; and based on the geometric model of the high slope platform, setting the boundary conditions of the computational domain and using a four-level hierarchical adaptive densification strategy to generate a three-dimensional computational volume mesh for computational fluid dynamics analysis. The technical solution of this application eliminates data deviation and splicing faults by mapping the coordinates of CAD topographic maps and background DEMs and smoothing the edges, thereby improving the accuracy of topographic data. Based on the fused data, regular grid sampling and elevation correction are performed, and Boolean operations are combined to accurately restore the morphology of the high slope excavation surface and transition zone, avoiding geometric distortion. Then, a CFD analysis grid is generated with four-level layered adaptive densification, which takes into account the accuracy of key areas and computational efficiency, effectively solving the problems of coarse traditional modeling and inefficient grid division.

[0015] Other features and advantages of the invention will be set forth in the description, and some advantages may be apparent directly from the description or learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and drawings.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A flowchart of a method for geometric modeling and mesh generation of a high slope platform for a wind turbine provided as an embodiment of the present invention; Figure 2 A flowchart illustrating another method for geometric modeling and mesh generation of a high slope platform for a wind turbine, provided as an embodiment of the present invention; Figure 3 A flowchart illustrating another method for geometric modeling and mesh generation of a high slope platform for a wind turbine, provided as an embodiment of the present invention; Figure 4A flowchart illustrating another method for geometric modeling and mesh generation of a high slope platform for a wind turbine, provided as an embodiment of the present invention; Figure 5 A flowchart illustrating another method for geometric modeling and mesh generation of a high slope platform for a wind turbine, provided as an embodiment of the present invention; Figure 6 A flowchart illustrating another method for geometric modeling and mesh generation of a high slope platform for a wind turbine, provided as an embodiment of the present invention; Figure 7 A structural diagram of a wind turbine high slope platform geometry shaping and grid division device provided as an embodiment of the present invention; Figure 8 This is a structural diagram of an electronic device provided as an embodiment of the present invention. Detailed Implementation

[0019] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0021] like Figure 1 As shown, this invention provides a method for geometric modeling and mesh generation of a high slope platform for a wind turbine, which includes: In step S11, a CAD topographic map containing wind turbine platform design information and background DEM data covering the site area are obtained. The CAD topographic map and the background DEM data are subjected to coordinate mapping and edge smoothing fusion processing to generate fused topographic data. In step S12, based on the fused terrain data, regularized grid sampling and elevation correction are performed in the platform area and the outer buffer zone. A geometric model of the high slope platform, including the excavation face and the transition zone, is constructed by using cosine control equations and Boolean operations. In step S13, based on the geometric model of the high slope platform, the boundary conditions of the computational domain are set, and a three-dimensional computational volume mesh for computational fluid dynamics analysis is generated using a four-level hierarchical adaptive densification strategy.

[0022] In one embodiment, the implementation process of this invention is described in detail using the geometric modeling and mesh generation of the high-slope platform of wind turbine No. 2 in a mountainous wind farm as an example. This wind farm is located in the southwestern mountainous region. Due to restrictions imposed by forest land and ecological red lines, turbine No. 2 needs to be built on a high slope area with a gradient of 35°. The platform is designed to have dimensions of 40m × 60m and a design elevation of 1250m. This method is required to construct an accurate geometric model and a mesh for CFD analysis to assess the impact of wind resources on turbine operation.

[0023] Two core types of data were acquired for the wind farm: first, a CAD topographic map containing design information for Unit 2 platform, with a scale of 1:500, covering the platform slope ratio (1:1.2), slope protection structure boundaries, and measured elevation points (200 points in total, with a point accuracy of ±0.05m); second, background DEM data covering the site area, with a resolution of 30m and a data coordinate system of WGS84. Coordinate mapping was performed on both types of data: the planar coordinates (geodetic coordinate system) of the CAD topographic map were aligned with the WGS84 coordinates of the background DEM using a seven-parameter transformation model, and the elevation offset of the DEM relative to the CAD topographic map was calculated to be +0.8m. Based on this, the elevation of the background DEM was corrected. A Gaussian filter (σ=2) was used to smooth the edges of the fusion area, with the filter window size set to 5×5 pixels, eliminating the stitching marks between the CAD elevation point cloud and the background DEM, generating fused topographic data with a resolution of 10m and a coverage area extending 15km outward from the platform.

[0024] Based on the aforementioned fused terrain data, the geometric outline of the platform excavation area was determined (defined by the platform boundary coordinates in the CAD terrain map), and a sampling area was formed by extending it outward by 150m. This buffer distance was determined based on the terrain feature scale and edge smoothing requirements. Within the sampling area, regularized grid points were generated at a resolution of 2m, resulting in a total of 301×301 grid points. Simultaneously, a random perturbation of ±0.1m was added to each grid point (simulating minor undulations in natural terrain to avoid overly regularized grids). The initial elevation of each grid point was extracted from the fused terrain data using bicubic interpolation. The ray casting method (polygon inclusion detection algorithm) was used to identify grid points within the platform area. Grid points with an initial elevation not equal to 1250m (the platform design elevation) were forcibly corrected to 1250m (points higher than the design elevation were treated by height reduction, and points lower were treated by height reduction). For the platform-slope transition zone, a cosine control equation is used to control the gradual change of slope. The width of the transition zone is set to 8m. Starting from the platform boundary, the elevation of each point in the transition zone is calculated using the cosine function y=Acos(πx / L)+B (where A=17.5m, L=8m, B=1250m) to ensure that the slope of the transition zone smoothly transitions from 0° at the platform edge to 35° on the slope. Redundant terrain data outside the platform area is removed by Boolean operations to construct a three-dimensional geometric model of the high slope platform that includes the excavation face, the transition zone, and the background terrain.

[0025] The CFD computational domain is set with the geometric model of the high slope platform as the core. The computational domain is rectangular in shape, with the center of the platform as the origin. The x-axis is along the ridge, the y-axis is perpendicular to the ridge, and the z-axis is perpendicular to the ground. The x-axis range is -8000m to 8000m, the y-axis range is -7000m to 7000m, and the z-axis range is 0 to 1000m (from the lowest elevation of the site to 500m above the platform elevation). This range can cover the main airflow movement area of ​​the high slope area. The boundary conditions are set as follows: the computational domain inlet (x=-8000m) adopts a velocity inlet boundary, with the inlet wind speed set to 8m / s (based on the annual average wind speed measured by the site's anemometer tower), and the turbulence intensity is 12%; the outlet (x=8000m) adopts a pressure outlet boundary, set to atmospheric pressure; the side boundary (y=±7000m) adopts a symmetrical boundary condition; the ground, high slope, and platform surfaces adopt a no-slip wall boundary condition, with the wall roughness set to 0.03m (to match the local rock surface characteristics).

[0026] Based on the above geometric model and boundary conditions, a four-level hierarchical adaptive encryption strategy is adopted to generate a three-dimensional computational volume mesh.

[0027] Level 1 (Initial Background Mesh): Using a Cartesian hexahedron as the structure, the background mesh resolution is set to 15m. The initial computational domain mesh is generated using the OpenFOAM blockMesh tool, with a total of approximately 1.2 million meshes, thus establishing the basic framework of the computational domain. Level 2 (Preliminary Global Encryption): Performs Level 1 encryption on the entire computational domain, increasing the grid resolution to 7.5m and the total number of grids to 9.6 million, thereby improving the overall grid's adaptability to terrain. Level 3 (Targeted Refinement of Transition Zone): An additional level of refinement is added to the platform-slope transition zone (20m wide), reducing the resolution to 3.75m. The total number of grids in this area is approximately 2.8 million, ensuring accurate capture of the geometric features of the transition zone. Level 4 (Deep Core Area Densification): Two additional densification levels are applied within a 50m radius around the center of the wind turbine hub (elevation 1270m), reducing the resolution to 0.9375m. The total number of grids in the core area is approximately 4.2 million, meeting the requirements for fine simulation of the flow field around the unit.

[0028] Simultaneously, five layers of prism mesh are generated in the slope area, with the first boundary layer thickness δ1=0.3m, and the thickness of each layer according to the formula δ n =δ1×1.2^(n-1) (geometric series with common ratio r=1.2) increases, ensuring the wall y+ value remains between 28 and 32; enable the dynamic adaptive module when the velocity gradient modulus in the flow field is detected to be >0.1s. -1 At that time, the grid in the region is automatically supplemented and densified at level 1, and the total amount of the generated three-dimensional computational volume grid is about 21 million.

[0029] Comparing the geometric model of the high slope platform generated in this embodiment with the field layout data, the elevation deviation of the platform-slope transition zone is ≤ ±0.15m, and the slope toe position deviation is ≤ 0.3m, which is far superior to the traditional method (elevation deviation ±0.8m, slope toe deviation ±1.2m). Compared with the traditional mesh generation method that only uses the default settings of snappyHexMesh, under the premise of ensuring the same CFD calculation accuracy (wind speed simulation deviation ≤ 5%), the total number of meshes in this embodiment is reduced by 35%, the CFD calculation time is shortened by 40%, and no numerical divergence problem caused by mesh skew occurs. This fully verifies the high accuracy and high efficiency of this method. The technical solution in this embodiment eliminates data deviations and splicing faults by mapping the coordinates of the CAD topographic map and the background DEM and smoothing the edges, thereby improving the accuracy of topographic data. Based on the fused data, regular grid sampling and elevation correction are performed, and Boolean operations are combined to accurately restore the morphology of the high slope excavation surface and transition zone, avoiding geometric distortion. Then, a CFD analysis grid is generated with four-level layered adaptive densification, which takes into account the accuracy of key areas and the computational efficiency, effectively solving the problems of rough modeling and inefficient grid division in traditional methods.

[0030] In one embodiment, such as Figure 2 As shown, it also includes the following steps S21-S23: In step S21, the elevation point cloud is extracted from the CAD topographic map and the core area boundary is determined. The core area boundary is extended outward by a preset distance, and the coordinate range of the extended boundary is calculated to crop the background DEM data. In step S22, the CAD elevation point cloud and the clipped background DEM data are spatially mapped, the elevation offset between the two is calculated and the reference plane is aligned to generate a preliminary splicing shape. In step S23, the edge seams of the preliminary spliced ​​terrain are smoothed to eliminate splicing marks, thereby obtaining the fused terrain data with continuous elevation transition.

[0031] In one embodiment, taking the terrain processing of the high slope platform of wind turbine No. 2 in a mountainous wind farm in Southwest China as a scenario, the coordinate mapping and edge smoothing fusion process of the CAD topographic map and the background DEM data are described in detail. The CAD topographic map of the site (scale 1:500, including platform slope ratio 1:1.2, slope protection boundary and 200 measured elevation points, with point accuracy ±0.05m) and the background DEM data covering the site (resolution 30m, coordinate system WGS84) are obtained. Elevation point clouds were extracted from the CAD topographic map. Combined with the location of the wind tower (coordinates X=3256872.5m, Y=568921.3m), the coordinates of the wind tower, and the platform boundary, the core area boundary was determined to be a rectangle with sides of 100m. According to engineering requirements, the core area boundary was extended outwards by 15km (the extension distance was determined based on the Gaussian smoothing kernel size and terrain data density). The extreme values ​​in the x-direction (Xmin=3256822.5m, Xmax=3256922.5m) and y-direction (…) of the core area were calculated. Ymin=568871.3m, Ymax=568971.3m. Substitute these values ​​into the boundary expansion formula (Xmin_new=Xmin-15000m, Xmax_new=Xmax+15000m, the same applies to the Y direction) to obtain the expanded boundary range. Then, use ArcGIS's "ExtractbyMask" tool to crop the background DEM, retain the DEM data within the expanded boundary, and resample it to a 10m resolution to ensure compatibility with the CAD elevation point cloud data resolution.

[0032] Spatial mapping and elevation alignment were performed: A seven-parameter coordinate transformation model (translation parameters ΔX=2.1m, ΔY=-1.8m, ΔZ=3.2m, rotation parameters εx=0.002°, εy=-0.001°, εz=0.003°, scale parameter μ=1.000002) was used to convert the geodetic coordinates of the CAD elevation point cloud to WGS84 coordinates consistent with the background DEM. Ten evenly distributed common points (covering the core area and the edge transition zone) were selected in the transformed CAD elevation point cloud. The elevation difference between each common point and the corresponding position in the cropped DEM was calculated, and the average value was taken to obtain an elevation offset of +0.8m. Based on this, the elevation of the cropped DEM data was corrected to align the reference surfaces of the two, generating preliminary stitched geodetic data.

[0033] Finally, the edge seams of the preliminary spliced ​​terrain (200m wide, covering the transition range between the CAD core area and the DEM background) were smoothed: a Gaussian filtering algorithm was used, with a filter standard deviation σ=2 and a filter window of 5×5 pixels. Convolution operations were performed on the elevation raster data at the seams. Verification through elevation profile analysis showed that the elevation difference at the seams after smoothing was ≤0.1m, the slope change was ≤2°, and the splicing traces were completely eliminated. The resulting fused terrain data with continuous elevation transition, a coverage range extending 15km outward from the platform, and a resolution of 10m was obtained, which can be directly used for subsequent geometric modeling of high slopes.

[0034] In one embodiment, such as Figure 3 As shown, it also includes the following steps S31-S33: In step S31, within the platform extension area of ​​the fused terrain data, a regular two-dimensional sampling point set with a first resolution is generated, and random perturbation is applied to the sampling point set to simulate natural landform features; In step S32, the background elevation value corresponding to the sampling point set is calculated based on the background DEM data using the bicubic interpolation algorithm formula, thereby generating an initial point cloud containing background terrain features. In step S33, polygon inclusion detection is performed on each point in the initial point cloud to identify points located within the platform design range, and their elevations are forcibly corrected to the design platform elevation.

[0035] In one embodiment, taking the modeling of the high slope platform of the No. 2 wind turbine unit in a mountainous wind farm in Southwest China as the scenario, the geometric model of the high slope platform is constructed based on the aforementioned fused terrain data (resolution 10m, WGS84 coordinate system, platform design range 40m×60m, design elevation 1250m). The platform extension area was determined. Based on the platform boundary marked on the CAD topographic map, a sampling area was formed by extending outward by 150m. The x-coordinate range of this area was 3256852.5m to 3256952.5m, and the y-coordinate range was 568891.3m to 568991.3m. Within this area, a set of regular two-dimensional sampling points was generated at a first resolution of 2m. The number of x-coordinate sampling points Nx = (3256952.5 - 3256852.5) / 2 + 1 = 51, and the number of y-coordinate sampling points Ny = (568991.3 - 568891.3) / 2 + 1 = 51, generating a total of 51 × 51 = 2601 sampling points. To simulate the slight undulations of the natural terrain, a random perturbation of ±0.1m was applied to the x and y coordinates of each sampling point. The perturbation value was generated using a normal distribution random function to ensure that the perturbation amplitude did not affect the overall terrain trend.

[0036] Based on the background DEM (10m resolution) in the fused terrain data, a bicubic interpolation algorithm was used to calculate the background elevation values ​​of the above sampling point set. The bicubic interpolation algorithm, based on the elevation information of 16 adjacent DEM grid points around each sampling point, uses polynomial coefficients to fit and obtain continuous elevation values ​​for each sampling point, generating an initial point cloud containing background terrain undulation features. Polygon inclusion detection was performed on the initial point cloud using the ray casting method (emitting rays from the sampling point in the positive x-axis direction and counting the number of intersections between the ray and the platform design boundary; an odd number of intersections indicates the point is within the platform). This identified 300 sampling points within the 40m × 60m platform design area. The elevations of these points were forcibly corrected to 1250m (points with initial elevations higher than 1250m were lowered, and points lower than 1250m were lowered). After correction, the elevation deviation of the sampling points within the platform area was ≤ ±0.05m, meeting the accuracy requirements for subsequent geometric modeling.

[0037] In one embodiment, such as Figure 4 As shown, it also includes the following steps S41-S42: In step S41, a line string object of the platform boundary is constructed, and for sampling points located outside the platform design range, the shortest horizontal distance from the sampling point to the platform boundary and the current slope are calculated. In step S42, when the current slope is detected to exceed the preset design value, the elevation of the sampling point is adjusted according to the elevation correction formula to construct a slope shape that gradually changes from the edge of the platform to the background terrain. In step S43, the corrected elevation data is subjected to Gaussian filtering to eliminate local elevation abrupt changes, and the processed point set is converted into an unstructured mesh through triangulation and exported as an STL format file as the geometric model of the high slope platform.

[0038] In one embodiment, continuing with the scenario of wind turbine unit No. 2 in a mountainous wind farm in Southwest China, based on the aforementioned platform-outside sampling point set (a total of 2301 points, 2m resolution), elevation correction of the platform-slope transition zone is performed to improve the geometric model of the high slope platform. A platform boundary line string object is constructed: the coordinates of the four vertices of the platform boundary marked on the CAD topographic map are extracted (X1 = 3256872.5m, Y1 = 568921.3m; X2 = 3256912.5m, Y2 = 568921.3m; X3 = 3256912.5m, Y3 = 568981.3m; X4 = 3256872.5m, Y4 = 568981.3m), and a closed line string object is constructed using GeoPandas for geometric distance calculation. For each sampling point outside the platform design range, the Euclidean distance algorithm is used to calculate the shortest horizontal distance (accuracy ±0.01m) to the line string object. At the same time, based on the elevation difference and horizontal spacing of adjacent sampling points, the current slope is calculated according to the formula "slope = elevation difference / horizontal spacing". The preset design slope of the platform-slope transition zone is 1:1.2 (approximately 40°).

[0039] When the current slope (e.g., 45°) of a sampling point is detected to exceed the design value of 40°, the elevation is adjusted according to the elevation correction formula Z_new=Z_platform+slope×dist: where Z_platform=1250m (platform design elevation), slope=1 / 1.2≈0.833 (design slope ratio), and dist is the shortest horizontal distance from the point to the platform boundary (e.g., if dist=6m, then Z_new=1250+0.833×6≈1255m), constructing a slope shape that gradually changes from the platform edge to the background terrain. Gaussian filtering was applied to all the corrected elevation data of the sampling points: the standard deviation of the filter was set to σ=2 and the filter window was 5×5 pixels. Local elevation abrupt changes were eliminated by convolution operation (elevation difference between adjacent points after filtering ≤0.3m). The processed point set was converted into an unstructured mesh using the Delaunay triangulation algorithm. Invalid triangular faces with an area of ​​less than 0.5m² were removed. The mesh was then exported as an STL file using MeshLab (file accuracy ±0.1m), thus completing the geometric model construction of the high slope platform.

[0040] In one embodiment, such as Figure 5 As shown, it also includes the following steps S51-S54: In step S51, a basic background mesh of level 1 is constructed, using a Cartesian hexahedron structure, and the global basic resolution is set to the second resolution to form the initial computational domain framework. In step S52, a first-level preliminary densification of the entire computational domain at level 2 is performed to improve the overall grid resolution to adapt to the flow characteristics; In step S53, a densification zone is defined for the platform-slope transition zone, and the control parameters are subjected to an additional level of densification at level 3 to achieve local refinement of the steep slope and platform edge. In step S54, an additional second-level encryption of level 4 is performed in the wind turbine location and core flow field area, so that the cumulative encryption level of the core area reaches level 4 and the grid resolution reaches the third resolution, wherein the value of the third resolution is less than the value of the second resolution.

[0041] In one embodiment, continuing with the scenario of wind turbine No. 2 in a mountainous wind farm in Southwest China, a three-dimensional computational mesh is generated based on the aforementioned constructed high-slope platform geometric model (STL format, platform design elevation 1250m). A four-level hierarchical adaptive densification strategy is used to generate the mesh. The basic background mesh of level 1 is constructed: a Cartesian hexahedral structure is adopted, with a global second resolution set to 15m. The initial computational domain is defined with the platform center (coordinates X = 3256872.5m, Y = 568921.3m) as the origin. The x-axis range is -8000m to 8000m (along the ridgeline), the y-axis range is -7000m to 7000m (perpendicular to the ridgeline), and the z-axis range is 0 to 1000m (covering the lowest site elevation to 500m above the platform). The initial computational domain framework is generated using OpenFOAM's blockMesh tool, with a total mesh size of approximately 1.2 million.

[0042] Level 2 preliminary global densification was performed. By modifying the encryption control dictionary of snappyHexMesh, Level 1 densification was applied to the entire computational domain, increasing the grid resolution from 15m to 7.5m and the total number of grid cells to approximately 9.6 million. This improved the overall grid's adaptability to terrain undulations and airflow. Next, Level 3 local densification was performed in the transition zone: the platform-slope transition zone (extending 20m outward from the transition zone, with ranges X = 3256852.5m to 3256892.5m and Y = 568901.3m to 568941.3m) was designated as the densification zone. The refinementRegions parameter was set to "additional Level 1 densification" in the control dictionary, reducing the grid resolution in this area to 3.75m and the total number of grid cells to approximately 2.8 million. This achieved precise refinement of the steep slope and platform edges. Finally, a core area deep densification of level 4 was implemented: the core flow field area was delineated with the center of the wind turbine hub (elevation 1270m) as the center and a radius of 50m, and "additional level 2 densification" was set to make the cumulative densification level of this area reach level 4, and the grid resolution was reduced to the third resolution of 0.9375m (less than the second resolution of 15m). The total number of grids in the core area was about 4.2 million, which met the requirements for fine simulation of the flow field around the unit.

[0043] In one embodiment, such as Figure 6 As shown, it also includes the following steps S61-S62: In step S61, a multi-layer prism mesh is generated in the slope area as a boundary layer, and the thickness of each layer is set to increase geometrically. In step S62, the flow field is monitored in real time during the calculation process. When the velocity gradient modulus exceeds the threshold, dynamic mesh refinement is triggered to automatically refine the flow separation or turbulent areas caused by the terrain.

[0044] In one embodiment, continuing with the scenario of wind turbine unit No. 2 in a mountainous wind farm in Southwest China, a boundary layer is set for the high slope surface (slope of 35°) and dynamic mesh refinement is implemented. Five layers of prismatic mesh are generated in the slope area as the boundary layer, and the thickness of each layer is set according to the geometric progression growth law: the thickness of the first boundary layer δ1 = 0.3m (matching the y+ value control requirements), the common ratio of the geometric progression r = 1.2, and according to the formula δ n = δ1×r^(n-1) calculates the thicknesses of each layer as 0.3m, 0.36m, 0.432m, 0.5184m, and 0.62208m respectively, with a total boundary layer thickness of approximately 2.23m. This ensures that the wall y+ value is maintained between 28 and 32, meeting the accuracy requirements for near-wall flow field calculation in turbulence simulation.

[0045] During CFD calculations, the flow field velocity gradient was monitored in real time using OpenFOAM's dynamicRefineFvMesh module: the velocity gradient modulus threshold was set to 0.1 s².-1 When the velocity gradient modulus in areas such as the toe of the slope and the edge of the platform exceeds this threshold (e.g., the gradient in the flow separation zone at the toe of the slope reaches 0.15s), the threshold is reached. -1 When the grid resolution is reduced from 3.75m to 1.875m, dynamic mesh refinement is automatically triggered, accurately capturing the significant characteristics of flow separation and turbulence caused by the terrain, avoiding distortion of flow field calculation due to insufficient mesh resolution, and ensuring the accuracy of wind resource parameter simulation.

[0046] In one embodiment, Figure 7 This is a block diagram illustrating the geometric design and grid division device for a high slope platform of a wind turbine, according to an exemplary embodiment. Figure 7 As shown, the geometric modeling and mesh generation device for the high slope platform of the wind turbine includes an acquisition module 71, a construction module 72, and a generation module 73.

[0047] The acquisition module 71 is used to acquire a CAD topographic map containing wind turbine platform design information and background DEM data covering the site area, and to perform coordinate mapping and edge smoothing fusion processing on the CAD topographic map and the background DEM data to generate fused topographic data. The construction module 72 is used to perform regularized grid sampling and elevation correction in the platform area and the outer buffer zone based on the fused terrain data, and to construct a geometric model of the high slope platform including the excavation face and the transition zone through cosine control equations and Boolean operations. The generation module 73 is used to set the boundary conditions of the computational domain based on the geometric model of the high slope platform, and to generate a three-dimensional computational volume mesh for computational fluid dynamics analysis using a four-level hierarchical adaptive densification strategy.

[0048] The acquisition module 71, construction module 72, and generation module 73 included in the block diagram of the wind turbine high slope platform geometry modeling and mesh generation device are controlled to execute the wind turbine high slope platform geometry modeling and mesh generation method described in any of the above embodiments.

[0049] like Figure 8 As shown, the present invention provides an electronic device 800, which includes: a communication interface, a processor 801, and a memory 802; The memory 802 stores program instructions. When executed by the processor 801, which is connected to the memory 802 via the communication interface, the program instructions acquire a CAD topographic map containing wind turbine platform design information and background DEM data covering the site area. The CAD topographic map and the background DEM data are then subjected to coordinate mapping and edge smoothing fusion processing to generate fused topographic data. Based on the fused topographic data, regularized grid sampling and elevation correction are performed in the platform area and the outer buffer zone. A high slope platform geometric model containing the excavation face and transition zone is constructed using cosine control equations and Boolean operations. Based on the high slope platform geometric model, the computational domain boundary conditions are set, and a four-level hierarchical adaptive densification strategy is used to generate a three-dimensional computational volume mesh for computational fluid dynamics analysis.

[0050] This invention provides a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed by a processor, they acquire a CAD topographic map containing wind turbine platform design information and background DEM data covering the site area. The CAD topographic map and the background DEM data are then subjected to coordinate mapping and edge smoothing fusion processing to generate fused topographic data. Based on the fused topographic data, regularized grid sampling and elevation correction are performed in the platform area and the outer buffer zone. A high-slope platform geometric model containing the excavation face and transition zone is constructed using cosine control equations and Boolean operations. Based on the high-slope platform geometric model, computational domain boundary conditions are set, and a four-level hierarchical adaptive densification strategy is used to generate a three-dimensional computational volume mesh for computational fluid dynamics analysis.

[0051] It should be understood that the specific features, operations, and details described above regarding the method of the present invention can also be similarly applied to the apparatus and system of the present invention, or vice versa. Furthermore, each step of the method of the present invention described above can be performed by a corresponding component or unit of the apparatus or system of the present invention.

[0052] It should be understood that the various modules / units of the device of the present invention can be implemented wholly or partially through software, hardware, firmware, or a combination thereof. Each module / unit can be embedded in the processor of a computer device in hardware or firmware form or independent of the processor, or it can be stored in the memory of a computer device in software form for the processor to call to execute the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0053] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores computer instructions executable by the processor, which, when executed by the processor, instruct the processor to perform steps of the methods of embodiments of the present invention. The computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the methods of the present invention.

[0054] This invention can be implemented as a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0055] It will be understood by those skilled in the art that the method steps of the present invention can be performed by a computer program instructing related hardware, such as a computer device or processor. The computer program may be stored in a non-transitory computer-readable storage medium, and its execution causes the steps of the present invention to be performed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0056] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for geometric modeling and mesh generation of a high-slope platform for a wind turbine generator, characterized in that, include: Obtain a CAD topographic map containing wind turbine platform design information and background DEM data covering the site area. Perform coordinate mapping and edge smoothing fusion processing on the CAD topographic map and the background DEM data to generate fused topographic data. Based on the fused terrain data, regularized grid sampling and elevation correction are performed in the platform area and the outer buffer zone. A geometric model of the high slope platform, including the excavation face and the transition zone, is constructed by using cosine control equations and Boolean operations. Based on the geometric model of the high slope platform, the boundary conditions of the computational domain are set, and a four-level hierarchical adaptive densification strategy is used to generate a three-dimensional computational volume mesh for computational fluid dynamics analysis.

2. The method for geometric modeling and mesh generation of a high slope platform for a wind turbine as described in claim 1, characterized in that, The coordinate mapping and edge smoothing fusion process of the CAD topographic map and the background DEM data includes: Extract elevation point clouds from CAD topographic maps and determine the core area boundary. Extend the core area boundary outward by a preset distance and calculate the coordinate range of the extended boundary to crop the background DEM data. Spatial mapping is performed between the CAD elevation point cloud and the cropped background DEM data. The elevation offset between the two is calculated and the reference plane is aligned to generate a preliminary stitching shape. The edges and seams of the preliminary spliced ​​terrain are smoothed to eliminate splicing marks, thereby obtaining the fused terrain data with continuous elevation transition.

3. The method for geometric modeling and mesh generation of a high slope platform for a wind turbine as described in claim 1, characterized in that, The construction of the high slope platform geometric model based on fused terrain data includes: Within the extended area of ​​the platform that integrates terrain data, a set of regular two-dimensional sampling points with a first resolution is generated, and random perturbations are applied to the sampling point set to simulate natural landform features; Using the bicubic interpolation algorithm, the background elevation value corresponding to the sampling point set is calculated based on the background DEM data to generate an initial point cloud containing background terrain features. Polygon inclusion detection is performed on each point in the initial point cloud to identify points located within the platform design range, and their elevations are forcibly corrected to the design platform elevation.

4. The method for geometric modeling and mesh generation of a high slope platform for a wind turbine as described in claim 3, characterized in that, The construction of the geometric model of the high slope platform also includes elevation correction of the platform-slope transition zone, including: Construct a line string object representing the platform boundary, and for sampling points located outside the platform design area, calculate the shortest horizontal distance from the sampling point to the platform boundary and the current slope; When the current slope is detected to exceed the preset design value, the elevation of the sampling point is adjusted according to the elevation correction formula to construct a slope shape that gradually changes from the edge of the platform to the background terrain. The corrected elevation data is processed by Gaussian filtering to eliminate local elevation abrupt changes, and the processed point set is converted into an unstructured mesh through triangulation and exported as an STL format file as the geometric model of the high slope platform.

5. The method for geometric modeling and mesh generation of a high slope platform for a wind turbine as described in claim 1, characterized in that, The method of generating a three-dimensional computational volume mesh using a four-level hierarchical adaptive encryption strategy includes: A basic background mesh of level 1 is constructed, using a Cartesian hexahedron structure, and the global basic resolution is set to the second resolution to form the initial computational domain framework. Level 2 initial densification is applied to the entire computational domain to improve the overall grid resolution and adapt to the flow characteristics; A densification zone was defined for the platform-slope transition area, and the control parameters were further densified at level 3 to achieve local refinement of the steep slope and platform edge. An additional second-level densification of level 4 is carried out in the wind turbine location and core flow field area, so that the cumulative densification level of the core area reaches level 4 and the grid resolution reaches the third resolution. The value of the third resolution is less than the value of the second resolution.

6. The method for geometric modeling and mesh generation of a high slope platform for a wind turbine as described in claim 5, characterized in that, Also includes: In the slope area, a multi-layered prismatic mesh is generated as a boundary layer, and the thickness of each layer is set to increase geometrically. The flow field is monitored in real time during the calculation process. When the velocity gradient modulus exceeds the threshold, dynamic mesh refinement is triggered to automatically refine the flow separation or turbulent areas caused by terrain.

7. A device for geometric shaping and grid division of a high slope platform for a wind turbine, characterized in that, include: The acquisition module is used to acquire a CAD topographic map containing wind turbine platform design information and background DEM data covering the site area, and to perform coordinate mapping and edge smoothing fusion processing on the CAD topographic map and the background DEM data to generate fused topographic data. The construction module is used to perform regularized grid sampling and elevation correction in the platform area and the outer buffer zone based on the fused terrain data, and to construct a geometric model of the high slope platform including the excavation face and the transition zone through cosine control equations and Boolean operations. The generation module is used to set the boundary conditions of the computational domain based on the geometric model of the high slope platform, and to generate a three-dimensional computational volume mesh for computational fluid dynamics analysis using a four-level hierarchical adaptive encryption strategy.

8. The device for geometric shaping and grid division of a high slope platform for a wind turbine as described in claim 7, characterized in that: The acquisition module, the construction module, and the generation module are controlled to execute the geometric modeling and mesh generation method for high slope platforms of wind turbine units as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, include: Communication interface, processor, memory; The memory is used to store program instructions, which, when executed by the processor connected to the memory via the communication interface, enable the electronic device to implement the geometric modeling and mesh generation method for the high slope platform of the wind turbine as described in any one of claims 1 to 6.

10. A computer-readable storage medium having program instructions stored thereon, characterized in that, When the program instructions are executed by the computer, the computer implements the method for geometric modeling and mesh generation of the high slope platform of the wind turbine as described in any one of claims 1 to 6.

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