Speed cloud picture generation method based on relative coordinates
By using a relative coordinate system to generate velocity contour maps in fluid analysis, the problem of flow direction distortion under an absolute coordinate system is solved, enabling more accurate flow characteristic analysis and improving the comparative analysis effect of contour maps.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HARBIN ENG UNIV
- Filing Date
- 2025-12-30
- Publication Date
- 2026-05-01
AI Technical Summary
In existing fluid analysis methods, the use of an absolute coordinate system to generate velocity contour maps leads to distortion of the flow direction, making it difficult to accurately reflect the characteristics of curved surface flow. Especially in the analysis of complex curved surface flow, the contour map analysis results differ significantly from the actual physical phenomena, affecting the accurate judgment of flow characteristics.
A velocity cloud map generation method based on relative coordinates is adopted. By selecting the first layer of grid near the wall in the monitoring interval as the reference point, tangents and angles are generated, and the velocity vectors in absolute coordinates are converted into relative tangential and normal velocities, which are then merged into a two-dimensional rectangular pixel array to generate a velocity cloud map.
While ensuring data accuracy, this approach makes velocity cloud map analysis more intuitive, improves the effectiveness of cloud map comparison and analysis, and accurately captures key processes such as vortex system evolution and boundary layer development.
Smart Images
Figure CN121960261A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a fluid analysis method, specifically a method for generating velocity contour maps. Background Technology
[0002] Current methods for processing velocity contour maps of curved surfaces in fluid analysis suffer from serious technical flaws. Traditional methods directly generate contour maps from mesh data in an absolute coordinate system, fundamentally ignoring the geometric characteristics of curved surface flows. When fluid flows over a curved wall, its true flow direction should follow the tangent to the wall, but the velocity components in an absolute coordinate system cannot accurately reflect this physical reality. This not only leads to distortion of the velocity vector direction in the contour map but also causes systematic deviations in determining the location of key flow features such as separation zones and recirculation zones. In regions with drastic curvature changes, this deviation can severely affect the judgment of flow characteristics, resulting in significant discrepancies between the contour map analysis results and the actual physical phenomena.
[0003] Existing contour plot generation methods suffer from a fundamental flaw in their coordinate system selection. Using absolute coordinates as the display benchmark results in the same physical phenomenon exhibiting completely different visual forms in different curvature regions. This not only increases the difficulty for engineers to interpret the data but also renders comparisons of contour plots under different operating conditions unscientific. Particularly in the analysis of complex surface flows such as those in turbomachinery and vascular blood flow, absolute coordinate contour plots often obscure the true flow structure, making it difficult to accurately capture key processes such as vortex evolution and boundary layer development. This incompatibility of the coordinate system has become a bottleneck problem restricting the refined analysis of surface flows. Summary of the Invention
[0004] The purpose of this invention is to provide a velocity cloud map generation method based on relative coordinates that makes the velocity cloud map more intuitive for comparative analysis while ensuring the accuracy and rationality of data and coordinates, thereby ensuring the effectiveness of velocity cloud map comparative analysis.
[0005] The objective of this invention is achieved as follows: This invention discloses a method for generating velocity contour maps based on relative coordinates, characterized by comprising the following steps: (1) Input the actual physical field and geometric conditions of the calculation model; (2) Select monitoring range based on actual needs: Construct the monitoring range according to monitoring needs and actual characteristic locations; (3) Based on the selected monitoring interval, extract the first layer of grid near the wall as the lowest longitudinal reference point: within the selected monitoring interval, extract all the first layer of grid within the detection interval and use it as the lowest longitudinal reference point of the velocity cloud map; (4) Based on the selected lowest reference point, generate the tangent and angle at the corresponding position and classify the remaining grid: Based on the selected lowest reference point, generate the tangent at the corresponding position to obtain the angle at the position of each reference point. At the same time, classify the remaining grid according to the structured grid distribution law to obtain the grid column with each reference point as the lowest point, and obtain the data from it. (5) Based on the data obtained in step (4), the velocity vectors under the absolute coordinates in each column of the original physical field, including the flow velocity in the absolute direction and the vertical flow velocity in the absolute direction, are converted into relative tangential and normal velocities relative to each column of the grid, i.e., the lowest reference point. (6) Merge the data and arrange the converted relative velocity vectors in a standard two-dimensional rectangular pixel array to output velocity cloud maps based on relative coordinates at different positions.
[0006] The present invention may also include: 1. Step (1) includes the structured mesh after division, the velocity vector within the mesh obtained by CFD calculation, and the geometric model used for calculation.
[0007] 2. The specific content of step (3) is as follows: Within the selected monitoring interval, select all grids in the first layer near the wall of the structured grid within the monitoring interval as the lowest relative longitudinal reference points of all grids, namely N1, N2, N3...N n There are a total of n reference points. These n reference points are used as reference points for all grids (i.e., n columns of grids) within the detection interval.
[0008] 3. In step (4), based on the selected n reference points, generate the tangents and angles at the corresponding positions, and classify the remaining mesh: Based on the selected n reference points on the curve, generate n tangents between the corresponding point and the wall, thereby obtaining the angles at the positions of each reference point. , , ... The remaining grids are matched with the reference points according to the structured grid position distribution, thus obtaining a grid sequence of n vertically distributed columns, and thus obtaining n sets of data, including the angle of each column of grids, i.e. the tangent angle of the reference point in each column, the coordinates of each grid position, and the velocity vector within each grid.
[0009] 4. In step (5), the absolute flow velocity and the absolute vertical flow velocity are determined based on the measured angle. Converted into relative tangential and normal velocities relative to the monitoring point: Among them , This represents the flow direction and velocity perpendicular to the flow in the original grid data, i.e., in absolute coordinates. , These are the tangential and normal velocities at the corresponding positions after coordinate transformation.
[0010] 5. Step (6) specifically involves: creating the target cloud. Figure 2 A rectangular pixel array, where the horizontal axis represents the curved distance of each grid reference point on the wall surface, i.e., the curved distance between the reference point and the lower left starting point of the monitoring area along the wall surface; the vertical axis represents... : in For the new cloud map, i.e., the new two-dimensional pixel region, the coordinates of the i-th grid in the n-th column from bottom to top. This is the relative normal distance perpendicular to the wall from the center of the first grid cell in the nth column (i.e., the reference grid cell). Let be the straight-line distance from the i-th grid cell (from bottom to top) in the n-th column to the reference point of that column. Then: in These are the coordinates of the first and nth grids from bottom to top in the nth column of the original coordinate system. Then, by arranging the coordinates and velocity vectors of the transformed grid into a pixel array, a structured grid velocity cloud map with relative coordinates can be generated.
[0011] The advantages of this invention are: 1. This invention proposes a piecewise coordinate transformation method by selecting reasonable segmentation points within a specified interval, which standardizes the coordinate transformation process and ensures the rationality of the coordinate transformation. 2. This invention completes the overall transformation of the velocity cloud map within the region by segmenting the data and grid coordinates in the original grid data, thus ensuring the effectiveness of data comparison and cloud map analysis. Attached Figure Description
[0012] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram illustrating the working principle of the present invention. Detailed Implementation
[0013] The invention will now be described in more detail with reference to the accompanying drawings: Combination Figure 1-2 A method for generating structured mesh velocity contour maps based on relative coordinates includes the following steps: (1) Input the actual physical field and geometric conditions of the calculation model; (2) Select monitoring range based on actual needs: Construct the monitoring range according to monitoring needs and actual characteristic locations; (3) Based on the selected monitoring interval, extract the first layer of grid near the wall as the lowest longitudinal reference point: within the selected monitoring interval, extract all the first layer of grid within the detection interval and use it as the lowest longitudinal reference point of the velocity cloud map; (4) Based on the selected lowest reference point, generate the tangent and angle at the corresponding position and classify the remaining grid: Based on the selected lowest reference point, generate the tangent at the corresponding position to obtain the angle at the position of each reference point. At the same time, classify the remaining grid according to the structured grid distribution law to obtain the grid column with each reference point as the lowest point, and obtain the data from it. (5) Based on the data obtained in step (4), the velocity vectors under the absolute coordinates in each column of the original physical field, including the flow velocity in the absolute direction and the vertical flow velocity in the absolute direction, are converted into relative tangential and normal velocities relative to each column of the grid, i.e., the lowest reference point. (6) Merge the data and arrange the transformed relative velocity vectors in a standard two-dimensional rectangular pixel array to output velocity cloud maps based on relative coordinates at different positions; In step (1), the raw data is obtained, including the divided structured mesh, the velocity vectors within the mesh obtained by CFD calculation, and the geometric model used for calculation.
[0014] In step (3), based on the monitoring range in step (2), all grids in the first layer near the wall of the structured grid within the monitoring interval are selected as the lowest relative longitudinal reference points for all grids: within the selected monitoring interval, all grids in the first layer near the wall of the structured grid within the monitoring interval are selected as the lowest relative longitudinal reference points for all grids, i.e., N1, N2, N3...N n There are a total of n reference points. These n reference points are used as reference points for all grids (i.e., n columns of grids) within the detection interval.
[0015] In step (4), based on the selected n reference points, the tangents and angles at the corresponding positions are generated, and the remaining meshes are categorized: based on the n reference points on the selected curve, n tangents between the corresponding points and the wall are generated, thereby obtaining the angles at the positions of each reference point. , , ... The remaining grids are matched with the reference points according to the structured grid position distribution, thus obtaining a grid sequence of n vertically distributed columns, and thus obtaining n sets of data, including: the angle of each column of grids, that is, the tangent angle of the reference point in each column; the coordinates of the position of each grid; and the velocity vector within each grid.
[0016] In step (5), the absolute flow velocity and the absolute vertical flow velocity are determined based on the angle measured in step (4). This is converted into relative velocities in the tangential and normal directions relative to the monitoring point, where: Among them , This represents the flow direction and velocity perpendicular to the flow in the original grid data, i.e., in absolute coordinates. , These represent the tangential and normal velocities at the corresponding positions after coordinate transformation. The tangent angle of the reference point measured in step (4); Step (6) specifically involves: summarizing and statistically analyzing the data from step (5), and then creating the target cloud. Figure 2 A rectangular pixel array, where the horizontal axis represents the curved distance of each grid reference point on the wall surface, i.e., the curved distance between the reference point and the lower left starting point of the monitoring area along the wall surface; the vertical axis represents... Among them are: in For the new cloud map, i.e., the new two-dimensional pixel region, the coordinates of the i-th grid in the n-th column from bottom to top. This is the relative normal distance perpendicular to the wall from the center of the first grid cell in the nth column (i.e., the reference grid cell). Let be the straight-line distance from the i-th grid cell (from bottom to top) in the n-th column to the reference point of that column. Therefore: in These are the coordinates of the first and nth grids from bottom to top in the nth column of the original coordinate system.
[0017] Then, by arranging the coordinates and velocity vectors of the transformed grid into a pixel array, a structured grid velocity cloud map with relative coordinates can be generated.
[0018] The overall process is as follows: First, input the mesh data, including the mesh's velocity and position data. Then, select and identify the transformation interval according to user needs. Based on the interval and curvature change rules, generate segmented points within the interval and complete the segmented coordinate transformation, which is divided into data transformation and coordinate transformation. Tangential velocity and normal velocity are selected as the velocity of the transformed cloud map. The curve distance S between the original mesh and the curved wall in the model is selected as the abscissa of the transformed cloud map. The normal height H perpendicular to S is selected as the ordinate of the transformed cloud map. Then, based on the data and coordinates obtained after transformation, a comparative analysis cloud map is generated. Finally, the velocity and cloud map of relative coordinates are generated and analyzed.
Claims
1. A method for generating velocity contour maps based on relative coordinates, characterized by: Includes the following steps: (1) Input the actual physical field and geometric conditions of the calculation model; (2) Select monitoring range based on actual needs: Construct the monitoring range according to monitoring needs and actual characteristic locations; (3) Based on the selected monitoring interval, extract the first layer of grid near the wall as the lowest longitudinal reference point: within the selected monitoring interval, extract all the first layer of grid within the detection interval and use it as the lowest longitudinal reference point of the velocity cloud map; (4) Based on the selected lowest reference point, generate the tangent and angle at the corresponding position and classify the remaining grid: Based on the selected lowest reference point, generate the tangent at the corresponding position to obtain the angle at the position of each reference point. At the same time, classify the remaining grid according to the structured grid distribution law to obtain the grid column with each reference point as the lowest point, and obtain the data from it. (5) Based on the data obtained in step (4), the velocity vectors under the absolute coordinates in each column of the original physical field, including the flow velocity in the absolute direction and the vertical flow velocity in the absolute direction, are converted into relative tangential and normal velocities relative to each column of the grid, i.e., the lowest reference point. (6) Merge the data and arrange the converted relative velocity vectors in a standard two-dimensional rectangular pixel array to output velocity cloud maps based on relative coordinates at different positions.
2. The velocity contour map generation method based on relative coordinates according to claim 1, characterized in that: Step (1) includes the divided structured mesh, the velocity vectors within the mesh obtained by CFD calculation, and the geometric model used for the calculation.
3. The velocity contour map generation method based on relative coordinates according to claim 1, characterized in that: The specific content of step (3) is as follows: Within the selected monitoring interval, select all grids in the first layer near the wall of the structured grid within the monitoring interval as the lowest relative longitudinal reference points of all grids, namely N1, N2, N3...N n There are a total of n reference points. These n reference points are used as reference points for all grids (i.e., n columns of grids) within the detection interval.
4. The velocity contour map generation method based on relative coordinates according to claim 1, characterized in that: In step (4), based on the selected n reference points, the tangents and angles at the corresponding positions are generated, and the remaining meshes are categorized: based on the n reference points on the selected curve, n tangents between the corresponding points and the wall are generated, thereby obtaining the angles at the positions of each reference point. , , ... The remaining grids are matched with the reference points according to the structured grid position distribution, thus obtaining a grid sequence of n vertically distributed columns, and thus obtaining n sets of data, including the angle of each column of grids, i.e. the tangent angle of the reference point in each column, the coordinates of each grid position, and the velocity vector within each grid.
5. The velocity contour map generation method based on relative coordinates according to claim 1, characterized in that: In step (5), the flow velocity in the absolute direction and the vertical flow velocity in the absolute direction are determined based on the measured angle. Converted into relative tangential and normal velocities relative to the monitoring point: Among them , This represents the flow direction and velocity perpendicular to the flow in the original grid data, i.e., in absolute coordinates. , These are the tangential and normal velocities at the corresponding positions after coordinate transformation.
6. The velocity contour map generation method based on relative coordinates according to claim 1, characterized in that: Step (6) specifically involves: creating a two-dimensional rectangular pixel array for the target cloud map, where the horizontal axis represents the curve distance of each grid reference point on the wall surface, i.e., the curve distance between the reference point and the lower left starting point of the monitoring area along the wall surface; the vertical axis represents... : in For the new cloud map, i.e., the new two-dimensional pixel region, the coordinates of the i-th grid in the n-th column from bottom to top. This is the relative normal distance perpendicular to the wall from the center of the first grid cell in the nth column (i.e., the reference grid cell). Let be the straight-line distance from the i-th grid cell (from bottom to top) in the n-th column to the reference point of that column. Then: in These are the coordinates of the first and nth grids from bottom to top in the nth column of the original coordinate system. Then, by arranging the coordinates and velocity vectors of the transformed grid into a pixel array, a structured grid velocity cloud map with relative coordinates can be generated.