CN113095012A - Splicing and fusing method for numerical simulation calculation results of wind power plant flow field partitions - Google Patents

Splicing and fusing method for numerical simulation calculation results of wind power plant flow field partitions Download PDF

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CN113095012A
CN113095012A CN202110536805.8A CN202110536805A CN113095012A CN 113095012 A CN113095012 A CN 113095012A CN 202110536805 A CN202110536805 A CN 202110536805A CN 113095012 A CN113095012 A CN 113095012A
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陈新明
吴有兵
叶昭良
王建峰
张波
闫永昌
郭雨桐
闫姝
蔡鹏飞
刘鑫
梁盛
钟迪
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Huaneng Clean Energy Research Institute
Huaneng Longdong Energy Co Ltd
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Abstract

The invention discloses a splicing and fusing method for numerical simulation calculation results of flow field partitions of a wind power plant, which is used for solving the splicing and fusing problem of sub-model flow field simulation results obtained after large terrain is segmented and calculated during CFD (computational fluid dynamics) simulation calculation of the flow field of the large wind power plant. The method comprises the steps of firstly carrying out interpolation calculation on grid points of an overlapping region by using the calculation results of the overlapping region of adjacent submodels, then searching association relations of the calculation values of different submodels through a statistical method based on the same grid coordinate points, determining linear relations by using a least square method, then correcting the grid point values of the submodels needing to be spliced and fused by using the linear relations, and finally combining the corrected submodel results to form the complete CFD simulation model calculation result of the whole wind power plant.

Description

Splicing and fusing method for numerical simulation calculation results of wind power plant flow field partitions
Technical Field
The invention belongs to the technical field of simulation calculation of a wind power plant flow field, and particularly relates to a splicing and fusing method for numerical simulation calculation results of a wind power plant flow field partition.
Background
With the arrival of the era of wind power generation flattening, the reduction of development cost and the increase of generated energy through large-scale development are important ways for improving the wind power development income, for example, in areas with relatively rich wind resources, the development cost can be effectively reduced by carrying out centralized development and developing the construction of large-scale wind power bases. The base-scale wind power plant development is different from the conventional wind power project, the boundary range of the wind power plant needing wind resource evaluation is increased from twenty-three kilometers to hundreds of kilometers, the area is enlarged by tens of times, and the simulation calculation scale is increased by more than two orders of magnitude, so that the conventional wind power plant flow field simulation calculation method is not applicable to the wind resource evaluation of the base-scale wind power plant. In order to solve the problem of solving CFD simulation calculation of complex terrains with boundary ranges of hundreds of kilometers, large terrains are decomposed into small sub-regions during grid division, grid modeling is respectively carried out, and solution calculation is independently carried out on each sub-model. After all the submodels are solved and calculated, the calculation results of all the submodels need to be spliced and fused into a large model again, so that the later-stage anemometer tower data substitution and full-field interpolation calculation are conveniently carried out, and the full-field wind resource evaluation is completed. However, when each submodel performs CFD solution calculation, independent boundary conditions are used, and direct combination of submodel calculation results of the same sector causes confusion of acceleration ratios in the whole field of large terrain, so that an accurate calculation result cannot be obtained.
Disclosure of Invention
The invention provides a splicing and fusing method of a wind power plant flow field partition numerical simulation calculation result, which is used for normalizing the acceleration ratio of a whole field and splicing and fusing simulation calculation results of a plurality of submodels of the same sector into an integral result file of the whole field range so as to obtain a CFD directional calculation simulation result of each sector.
In order to achieve the purpose, the invention adopts the following technical scheme:
a splicing and fusing method for numerical simulation calculation results of wind power plant flow field partitions determines a model splicing and fusing datum and a splicing and fusing sequence according to relative position relations of all submodels in the whole wind power plant, carries out statistical analysis on grid point values of overlapping regions of adjacent submodels to find an association relation, then calibrates all grid point values of the submodels according to the association relation, respectively calibrates all grid point values of the submodels according to a specific sequence, and combines the submodels to obtain a splicing and fusing result of numerical simulation calculation in the whole field range.
Further, determining a model splicing fusion datum and a splicing fusion order according to the relative position relationship of all the sub-models in the whole wind power plant specifically comprises the following steps: firstly, determining the relative position relation of the calculation areas of all the sub-models in the whole wind power plant, and sequentially fusing all the sub-models line by line according to the sequence from west to east and from north to south.
Further, sequentially fusing all the submodels line by line according to the sequence from west to east and from north to south specifically comprises: and taking the first sub-model in the northwest corner as a reference, and sequentially splicing and fusing the calculation results of the other sub-models after correlation calibration.
Further, performing statistical analysis to find the association relationship through the overlapping area grid point values of the adjacent submodels specifically comprises: firstly, interpolation calculation is carried out on grid points of one sub-model in an overlapping area to grid points of the other sub-model, values of two adjacent sub-models in the same grid coordinate are obtained in the overlapping area, the values of the two sub-models in the same grid coordinate are used as a scatter diagram, and the linear relation between the scatter values is obtained by using a least square method.
Further, after the linear relation between the scatter point values is obtained, the obtained linear relation between the scatter point values is utilized, one submodel is used as a reference to calibrate the other submodel, and the calibrated submodel and the previous model are combined to obtain a splicing and fusing result.
Further, when the incidence relation of the calculation results of the adjacent submodels is searched, only the grid point value of the core area of the overlapping area is adopted.
Further, the core area is defined as follows: for two submodels adjacent to each other, the width of the core area is 1/3 of the width of the overlapping area, and the height of the core area is 2/3 of the height of the overlapping area; for the two submodels adjacent north and south, the width of the core region is 2/3 of the width of the overlap region, and the height is 1/3 of the height of the overlap region.
Further, the interpolation calculation of the grid points of one submodel by using the grid points of the overlapping area to the grid points of the other submodel is specifically as follows: the method comprises the steps of firstly searching eight grid points of another submodel nearest to a point to be interpolated, wherein the eight grid points form eight vertexes of a cube, the point to be interpolated is projected to the top surface and the bottom surface of the cube respectively, the projection points of the top surface and the bottom surface are projected to two edges of the top surface and the bottom surface respectively, the projection point values on the edges are obtained through linear interpolation through the vertexes, the projection point values of the top surface and the bottom surface are obtained through linear interpolation of the projection point values on the edges, and the final interpolation point value to be obtained is obtained through linear interpolation of the projection point values of the top surface.
Compared with the prior art, the invention has the following beneficial technical effects:
the method solves the problem of splicing and fusing the flow field partition simulation calculation results of the large-terrain wind power plant, and performs correlation calibration on the sub-model calculation results of the partitions to form a unified overall numerical simulation calculation result in the whole field range. By means of the technology, large terrain in a range of hundreds of kilometers can be divided into a plurality of small terrain models in a range of tens of kilometers to tens of kilometers for numerical calculation, so that the size of a single model is reduced, and the calculation and convergence speed is accelerated.
Furthermore, a unified large model is formed after the plurality of sub-models are fused and spliced, so that the wind resource assessment in the whole field range and the micro site selection optimization in the whole range are facilitated.
Drawings
FIG. 1 is a schematic diagram of sub-model mesh overlay;
wherein, 1-submodel S grid; 2-submodel A grid; 17-grid overlap area middle line;
FIG. 2 is a schematic diagram of an overlap region grid interpolation process;
FIG. 3 is a schematic diagram of a large terrain segmentation submodel;
FIG. 4 is a schematic diagram of a scatter distribution of grid point values in an overlapping area of adjacent submodels.
Detailed Description
The invention is described in further detail below:
a splicing and fusing method for a wind power plant flow field partition numerical simulation calculation result determines a model splicing and fusing datum and a splicing and fusing sequence according to relative position relations of all submodels in the whole wind power plant, carries out statistical analysis on overlapping area grid point values of adjacent submodels to find an association relation, then calibrates all grid point values of the submodels according to the association relation, respectively calibrates all the submodel grid point values according to a specific sequence and merges the submodels, relates to space grid points of overlapping areas, divides the submodels according to an overlapping area middle line rule, and discards space point values outside the overlapping area middle line in a submodel merging process.
The working principle of the invention is as follows: and calculating the numerical calculation result of the domain overlapping region by using the adjacent submodels, obtaining the association relation of variable values in different submodels by using a statistical analysis method, calibrating the variable values of different grid points in each submodel by using the association relation, calibrating the variable values of each grid point in each submodel in the full wind field range according to the same method, and generating the spliced and fused full field range directional calculation result.
Specifically, the method comprises the following steps: searching the incidence relation by using the numerical calculation result of the overlapped region of the subarea submodel, calibrating the numerical calculation result of the subarea submodel by using the incidence relation, combining the calibrated submodel results and obtaining the splicing fusion result of the numerical simulation calculation of the whole field range, when searching the correlation of the calculation results of the adjacent submodels, firstly, the grids of the overlapped area are used for interpolation calculation, in the range of the overlapping area, the values of the two sub-models under the same grid coordinate are obtained, the values of the two sub-models under the same grid coordinate are used as a scatter diagram, the linear relation between scatter values is obtained by using the least square method, the linear relation between the obtained scatter values is used, and calibrating the other submodel by taking one of the submodels as a reference, and combining the calibrated submodel with the previous model to obtain a splicing and fusing result.
When the incidence relation of the calculation results of the adjacent submodels is searched, only the grid point values of the core area of the overlapping area are adopted, for the two submodels adjacent to the east and the west, the width of the core area is 1/3 of the width of the overlapping area, and the height of the core area is 2/3 of the height of the overlapping area; for two submodels adjacent to the north and south, the width of the core area is 2/3 of the width of the overlapping area, and the height of the core area is 1/3 of the height of the overlapping area; when the grid points of one submodel in the overlapping area are used for carrying out interpolation calculation on the grid points of the other submodel, firstly, eight grid points of the other submodel which are most adjacent to the points to be interpolated are searched, the eight grid points form eight vertexes of a cube, the points to be interpolated are respectively projected to the top surface and the ground of the cube, the projection points of the top surface and the bottom surface are respectively projected to two edges of the top surface and the ground, the projection point values on the edges are obtained by carrying out linear interpolation through the vertexes, the projection point values of the top surface and the bottom surface are obtained by carrying out linear interpolation through the projection point values on the edges, and the final interpolation point value to be obtained is; when the calibrated submodel is combined with the previous model, the two submodels take the middle line of the overlapping area as a boundary, each submodel only keeps the result of the overlapping area within the middle line, the result of the overlapping area outside the middle line is discarded, and after the two submodels are combined into one model, the overlapping area does not exist any more.
The invention is described in further detail in the following examples, which are intended to be illustrative of the invention, but these should not be construed as limiting the scope of the invention, which is defined by the appended claims, any modification which comes within the scope of the invention being covered thereby.
A fusion calculation method for numerical simulation calculation results of flow field partitions of a wind power plant is used for solving the problem of flow field CFD simulation calculation of an 80km x 80km large wind power base, a partition numerical simulation calculation method is adopted for directional calculation, a submodel division method is shown in figure 3, a wind power plant area is divided into 16 submodel areas in total of 4 x 4, the overlapping width of calculation areas between adjacent submodels is 10km, and after directional calculation of all the submodels is completed, splicing and fusion of calculation results are performed according to the method.
And determining model fusion benchmark and fusion sequence according to the relative position relation of the 16 sub-models in the whole wind power plant. The submodel 1 is the most northwest submodel and is used as a reference model for splicing and fusing the large model, and then splicing and fusing are sequentially carried out on the reference model according to the numbering sequence in the schematic diagram.
Taking the splicing and fusion of the sub-model 1 and the sub-model 2 as an example, the sub-model 2 is spliced to the sub-model 1. And searching an incidence relation through a statistical method by using the grid point values of the overlapping area of the submodel, calibrating all grid point values of the submodel 2 according to the incidence relation, and combining all the grid point values of the calibrated middle line east submodel 2 with the submodel 1 with the middle line west to obtain a fusion model. According to the same process, the models 3, 4 and 5 … … 16 are sequentially merged into the fusion model, and finally the complete splicing fusion model in the full field range is obtained.
The sub-model splicing and fusing steps are as follows:
1. firstly, determining the relative position relation of the calculation areas of all the sub-models in the whole wind power plant, and sequentially fusing all the sub-models line by line according to the sequence from west to east and from north to south. And taking the first sub-model in the northwest corner as a reference, and sequentially splicing and fusing the calculation results of the other sub-models after correlation calibration.
2. The fusion method of the two adjacent submodels comprises the following steps: taking two east-west adjacent submodels as an example, taking a west-side submodel as a reference model and marking as S, and taking an east-side submodel as a model to be fused and marking as A. Firstly, determining the overlapping area range of two submodels, wherein the east-west coordinate is X, the south-north coordinate is Y, and the east boundary of the west submodel is X1The west boundary of the east submodel is x2The length of the coincidence range of the two sub-models is x2-x1The coincident centerline coordinates of the two submodels are x0Wherein, in the step (A),
Figure BDA0003069986310000071
3. and taking the calculation result in the core area of the overlapping area as a data source for determining the incidence relation. The core region width is 1/3 the overlap region width and the height is 2/3 the overlap region height. West boundary x of core region0LWherein, in the step (A),
Figure BDA0003069986310000072
east boundary x of core region0RWherein, in the step (A),
Figure BDA0003069986310000073
Figure BDA0003069986310000074
the north boundary of the overlap region is y1South boundary is y2The north-south central axis is y0Wherein
Figure BDA0003069986310000075
Upper boundary y of the coincident core region0UWherein, in the step (A),
Figure BDA0003069986310000076
the lower boundary of the overlap region is y0DWherein, in the step (A),
Figure BDA0003069986310000077
4. taking the coordinates of all grid points in the S model in the core area, and calculating as CoordS，{CoordS＝(x，y，z)|CoordSE.g. S, all grid point calculation values in the S model in the core area are calculated as ValueS，{ValueS＝(x，y，z，Value)|(x，y，z)∈Coords}. Carrying out interpolation calculation on coordinates of all grid points in the S model in the core area by using grid point values of the A model in the overlapping area, and carrying out Coord calculation in the core areaSGenerating an interpolation result based on the grid point Value of the A model on the coordinate grid point, and counting the interpolation result as ValueA，{ValueA＝(x，y，z，Value)|(x，y，z)∈CoordS}。
5. As shown in FIG. 4, all the core regions CoordSValue of coordinate pointAAnd ValueSValues are used as scatter plotsAAnd ValueSThe value distribution of (a) and (b) is linear, and k and b are solved by using a least square method.
ValueS＝k·ValueA+b
6. Substituting the linear relation into the A model grid point (calculation range: x > x)0) Obtaining the calibrated ValueA→S，{ValueA→S(x, y, z, Value) | (x, y, z) ∈ A, and x > x0}。
7. Taking ValueS0＝{ValueS0(x, y, z, Value) | (x, y, z) ∈ S, and x ≦ x0Will ValueS0And ValueA→SAnd combining to obtain a calculation result fusion file of the sub-model S and the sub-model A.
8. The method for performing interpolation calculation on the coordinates of all grid points in the S model in the core region by using the grid point values of the A model in the overlap region is as follows: recording the waiting point as SiThe coordinate is (S)x，Sy，Sz) As shown in fig. 2, the point values and coordinates of 8 grid points in the a model whose space is adjacent are found to be a1(xA1，yA1，zA1)，A2(xA2，yA2，zA2)，……，A8(xA8，yA8，zA8) Remember SiIn plane A1A2A3A4Projection on is A1，2，3，4In the plane A5A6A7A8Projection on is A5，6，7，8Sequentially centering the intermediate quantity A by adopting a linear interpolation method1，2，A3，4，A5，6，A7，8，A1，2，3，4，A5，6，7，8And (6) carrying out interpolation.
The interpolation formula is as follows:
Figure BDA0003069986310000081
Figure BDA0003069986310000082
Figure BDA0003069986310000083
Figure BDA0003069986310000084
Figure BDA0003069986310000085
Figure BDA0003069986310000086
Figure BDA0003069986310000087
wherein the content of the first and second substances,
xA1，2，yA1，2by passing
Figure BDA0003069986310000088
The determination is carried out by the following steps,
xA3，4，yA3，4by passing
Figure BDA0003069986310000091
The determination is carried out by the following steps,
xA5，6，yA5，6by passing
Figure BDA0003069986310000092
The determination is carried out by the following steps,
xA7，8，yA7，8by passing
Figure BDA0003069986310000093
And (4) obtaining.
Figure BDA0003069986310000094
Figure BDA0003069986310000095
Figure BDA0003069986310000096
Figure BDA0003069986310000097
Figure BDA0003069986310000098
Figure BDA0003069986310000099
The present invention has been described in further detail with reference to specific examples thereof, which are given by way of illustration and are not to be construed as limiting the scope of the invention, which is defined by the appended claims, as any variation which comes within the scope of the claims is intended to be covered thereby.

Claims (8)

1. The splicing and fusing method for the numerical simulation calculation results of the wind power plant flow field partitions is characterized in that model splicing and fusing datum and splicing and fusing sequence are determined according to the relative position relations of all sub-models in the whole wind power plant, statistical analysis is carried out on grid point values of overlapping regions of adjacent sub-models to find association relations, then all grid point values of the sub-models are calibrated according to the association relations, all sub-model grid point values are respectively calibrated according to a specific sequence, sub-models are combined, and the splicing and fusing results of the numerical simulation calculation of the whole field range are obtained.
2. The splicing and fusing method for the numerical simulation calculation results of the wind farm flow field partitions according to claim 1, characterized in that the determination of the model splicing and fusing reference and the splicing and fusing order according to the relative position relationship of all the sub-models in the whole wind farm specifically comprises the following steps: firstly, determining the relative position relation of the calculation areas of all the sub-models in the whole wind power plant, and sequentially fusing all the sub-models line by line according to the sequence from west to east and from north to south.
3. The splicing and fusing method for the numerical simulation calculation results of the wind farm flow field partitions according to claim 2, characterized in that sequentially fusing all the submodels line by line in the sequence from west to east and from north to south specifically comprises: and taking the first sub-model in the northwest corner as a reference, and sequentially splicing and fusing the calculation results of the other sub-models after correlation calibration.
4. The splicing and fusing method for the numerical simulation calculation results of the wind farm flow field partitions according to claim 1, characterized in that the statistical analysis for finding the association relationship by the overlapping area grid point values of the adjacent submodels is specifically as follows: firstly, interpolation calculation is carried out on grid points of one sub-model in an overlapping area to grid points of the other sub-model, values of two adjacent sub-models in the same grid coordinate are obtained in the overlapping area, the values of the two sub-models in the same grid coordinate are used as a scatter diagram, and the linear relation between the scatter values is obtained by using a least square method.
5. The splicing and fusing method for the numerical simulation calculation results of the wind farm flow field partitions according to claim 4, characterized in that after linear relations among the scatter point values are obtained, the obtained linear relations among the scatter point values are utilized, one submodel is used as a reference to calibrate the other submodel, and the calibrated submodel and the previous model are merged to obtain a splicing and fusing result.
6. The splicing and fusing method for the numerical simulation calculation results of the wind farm flow field partitions according to claim 4, characterized in that only grid point values of a core area of an overlapping area are adopted when the incidence relation of the calculation results of adjacent submodels is searched.
7. The splicing and fusing method for the numerical simulation calculation results of the wind farm flow field partitions according to claim 6, characterized in that the core area is defined as follows: for two submodels adjacent to each other, the width of the core area is 1/3 of the width of the overlapping area, and the height of the core area is 2/3 of the height of the overlapping area; for the two submodels adjacent north and south, the width of the core region is 2/3 of the width of the overlap region, and the height is 1/3 of the height of the overlap region.
8. The splicing and fusing method for the numerical simulation calculation results of the wind farm flow field partitions according to claim 4, wherein the interpolation calculation of the grid points of one submodel to the grid points of another submodel by using the grid points of one submodel in the overlapping area specifically comprises the following steps: the method comprises the steps of firstly searching eight grid points of another submodel nearest to a point to be interpolated, wherein the eight grid points form eight vertexes of a cube, the point to be interpolated is projected to the top surface and the bottom surface of the cube respectively, the projection points of the top surface and the bottom surface are projected to two edges of the top surface and the bottom surface respectively, the projection point values on the edges are obtained through linear interpolation through the vertexes, the projection point values of the top surface and the bottom surface are obtained through linear interpolation of the projection point values on the edges, and the final interpolation point value to be obtained is obtained through linear interpolation of the projection point values of the top surface.
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