CN117993328B - Method for characterizing appearance of two-dimensional flow field barrier - Google Patents
Method for characterizing appearance of two-dimensional flow field barrier Download PDFInfo
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Abstract
The invention belongs to intelligent fluid mechanics, and particularly relates to a method for characterizing and representing the appearance of a two-dimensional flow field barrier. The method for characterizing the appearance of the two-dimensional flow field barrier comprises the steps of obtaining Mask images for characterizing the appearance of the barrier; creating a zero level set; defining a symbol area function associated with the level set function; the signed area field SAF is calculated. The method for representing the appearance of the two-dimensional flow field obstacle is based on the appearance characterization representation method of the signed area field, can describe the appearance information of the obstacle, and meets the requirement of searching flow field solution rules.
Description
Technical Field
The invention belongs to intelligent fluid mechanics, and particularly relates to a method for characterizing and representing the appearance of a two-dimensional flow field barrier.
Background
At present, the rise of artificial intelligence technology has prompted the rapid development of the "fourth paradigm" in the modern aerodynamic field. The application of the artificial intelligence technology to the fluid mechanics is not only beneficial to scientific discovery and further improvement of aerodynamic theory, but also can realize rapid prediction of flow field data, thereby reducing the calculation cost. Artificial intelligence techniques rely on large amounts of data to fit the true physical laws. The data features extracted by the neural network have a significant impact on the prediction results. The obstruction exhibits a variety of physical characteristics in the flow field due to its different topography. Therefore, it is necessary to propose a new method for representing the flow field obstacle, which is not only in accordance with the physical rule of the flow field, but also easy to express the unique obstacle shape in the neural network.
Existing appearance feature representation methods mainly include binary coding and Signed Distance Field (SDF) representation. Studies （Guo X, Li W, Iorio F. Convolutional neural networks for steady flow approximation[C]//Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016: 481-490.）, on Guo et al have demonstrated that signed distance fields characterize the appearance of obstacles better than binary coding. The main difference between the two is that: binary coding is suitable for pixel level operation, mainly for representing the visibility of an area; while SDF represents the distance of a particular geometry, it can provide more information about the shape itself. In terms of flow field barrier feature representation, the SDF method is mainly adopted to represent the shape information at present, and remarkable results are achieved. However, for the flow field law which is continuously changed, the SDF only reflects the information of the shape change, and the influence of the information of different shapes on the evolution process of the flow field is not fully reflected.
Currently, there is a need to develop a method for characterizing the appearance of two-dimensional flow field obstructions.
Disclosure of Invention
The invention aims to provide a method for characterizing the appearance of a two-dimensional flow field barrier.
The method for characterizing the appearance of the two-dimensional flow field barrier adopts a signed area field SAF to describe the appearance of the barrier. The signed area field SAF is modified based on the signed distance field SDF. Each point in the signed area field SAF represents the maximum area of the point to the obstacle surface, which value varies as the obstacle profile varies.
The derivation of the signed area field SAF is shown in fig. 1: first for the NS equationVolume fraction is performed to obtain/>; And then according to the Gaussian theorem, obtain; Finally, according to Newton iteration and other methods, a discrete format NS equation is obtained to obtain flow field parameters. The derivation process shows the importance of the area to the solution of the NS equation, the Gaussian theorem converts the volume integral into the area integral, and then the iterative method is obtained according to various iterative methods. Therefore, the adoption of the signed area field SAF to represent the shape of the obstacle is beneficial to finding the requirement of flow field solution rules.
The method for characterizing the appearance of the two-dimensional flow field barrier adopts a signed area field SAF to describe the appearance of the barrier, and comprises the following steps:
s10, obtaining Mask images representing the appearance of the obstacle;
S20, creating a zero level set;
The zero level set is the geometric boundary of the flow field obstruction region Set of points/>：
，
Wherein,Is a flow field region; /(I)As a function of the level set; when/>When the point/>, is representedOn the set boundary; when/>When the point/>, is representedWithin the set boundary; when/>When the point/>, is representedOutside the aggregate boundary;
S30, defining a symbol area function related to the level set function;
Definition and level set function Related symbol area function/>The method comprises the following steps:
，
Wherein the sign area function Is a directed distance function for measuring points/>To a closed geometric boundary/>The sign function takes on the value/>By dot/>Whether to decide inside or outside the shape; when/>In the case of shape interior,/>; When/>At the time of the outer part of the shape,；
S40, calculating a signed area field SAF;
First, a point in a two-dimensional flow field is calculated A maximum curved surface formed by any two points on the boundary of the obstacle; and finally, circularly solving the maximum area from each point in the two-dimensional flow field to the boundary of the obstacle, thereby obtaining the signed area field SAF.
The method for representing the appearance of the two-dimensional flow field obstacle is based on the appearance characterization representation method of the signed area field, can describe the appearance information of the obstacle, and meets the requirement of searching flow field solution rules.
Drawings
FIG. 1 is a derivation of a signed area field SAF;
FIG. 2 is a flow chart of a method of the present invention for characterizing the appearance of a two-dimensional flow field barrier;
FIG. 3a is a Mask image characterizing the appearance of an obstacle;
FIG. 3b is a maximum curved surface formed by one point in a two-dimensional flow field and any two points on the boundary of an obstacle;
Fig. 3c shows the signed area field SAF obtained by the solution.
Detailed Description
The invention is described in detail below with reference to the drawings and examples.
As shown in fig. 2, the method for characterizing the appearance of a two-dimensional flow field barrier according to the present invention uses a signed area field SAF to characterize the appearance of the barrier, comprising the steps of:
s10, obtaining Mask images representing the appearance of the obstacle;
S20, creating a zero level set;
The zero level set is the geometric boundary of the flow field obstruction region Set of points/>：
，
Wherein,Is a flow field region; /(I)As a function of the level set; when/>When the point/>, is representedOn the set boundary; when/>When the point/>, is representedWithin the set boundary; when/>When the point/>, is representedOutside the aggregate boundary;
S30, defining a symbol area function related to the level set function;
Definition and level set function Related symbol area function/>The method comprises the following steps:
，
Wherein the sign area function Is a directed distance function for measuring points/>To a closed geometric boundary/>The sign function takes on the value/>By dot/>Whether to decide inside or outside the shape; when/>In the case of shape interior,/>; When/>At the time of the outer part of the shape,；
S40, calculating a signed area field SAF;
First, a point in a two-dimensional flow field is calculated A maximum curved surface formed by any two points on the boundary of the obstacle; and finally, circularly solving the maximum area from each point in the two-dimensional flow field to the boundary of the obstacle, thereby obtaining the signed area field SAF.
Example 1: the signed area field SAF is calculated for the airfoil according to the embodiment, and the specific steps are as follows:
a. obtaining Mask images characterizing airfoil boundaries as shown in FIG. 3 a;
b. as shown in fig. 3b, a point in the two-dimensional flow field is calculated Any two points/>, on the boundary with the airfoil、So that the triangle area formed by the three points is the largest;
c. Three points are obtained 、/>、/>The number of pixel points in the curved surface S is calculated to obtain the point/>Is a value of (2);
d. The maximum area from each point in the two-dimensional flow field to the airfoil boundary is circularly calculated to obtain the signed area field SAF of the airfoil as shown in FIG. 3 c.
Although embodiments of the invention have been disclosed in the foregoing description and illustrated in the drawings, it will be understood by those skilled in the art that the present invention is not limited to the specific details and illustrations of features and steps set forth herein, and that all features of the invention disclosed, or steps of the method or process, except for mutually exclusive features and/or steps, may be combined in any manner without departing from the principles of the invention.
Claims (1)
1. A method for characterizing the appearance of a two-dimensional flow field barrier, wherein the method for characterizing the appearance of the two-dimensional flow field barrier uses a signed area field SAF to characterize the appearance of the barrier, comprising the steps of:
s10, obtaining Mask images representing the appearance of the obstacle;
S20, creating a zero level set;
The zero level set is the geometric boundary of the flow field obstruction region Set of points/>：
，
Wherein,Is a flow field region; /(I)As a function of the level set; when/>When the point/>, is representedOn the set boundary; when/>When the point/>, is representedWithin the set boundary; when/>When the point/>, is representedOutside the aggregate boundary;
S30, defining a symbol area function related to the level set function;
Definition and level set function Related symbol area function/>The method comprises the following steps:
，
Wherein the sign area function Is a directed distance function for measuring points/>To a closed geometric boundaryThe sign function takes on the value/>By dot/>Whether to decide inside or outside the shape; when/>In the case of shape interior,/>; When/>At the time of the outer part of the shape,；
S40, calculating a signed area field SAF;
First, a point in a two-dimensional flow field is calculated A maximum curved surface formed by any two points on the boundary of the obstacle; and finally, circularly solving the maximum area from each point in the two-dimensional flow field to the boundary of the obstacle, thereby obtaining the signed area field SAF.
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| WO2002003173A2 (en) * | 2000-07-01 | 2002-01-10 | Aemp Corporation | Equal order method for fluid flow simulation |
| CN106445882A (en) * | 2016-07-12 | 2017-02-22 | 南京航空航天大学 | Improved CLSVOF method for quickly building signed distance function by VOF function |
| CN109165423A (en) * | 2018-08-03 | 2019-01-08 | 北京航空航天大学 | It is a kind of based on stream function around Flowing Field modeling method |
| WO2020244217A1 (en) * | 2019-06-06 | 2020-12-10 | 大连理工大学 | Improved mode decomposition method applicable to flow field analysis and reconstruction of internal solitary wave test |
| CN115310176A (en) * | 2022-07-18 | 2022-11-08 | 哈尔滨工业大学 | Two-dimensional flow field aerodynamic force reconstruction method based on OpenFOAM and derivative moment transformation |
| CN115630589A (en) * | 2022-10-17 | 2023-01-20 | 华中科技大学 | Two-dimensional steady laminar flow field rapid forecasting method based on convolutional neural network |
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| WO2002003173A2 (en) * | 2000-07-01 | 2002-01-10 | Aemp Corporation | Equal order method for fluid flow simulation |
| CN106445882A (en) * | 2016-07-12 | 2017-02-22 | 南京航空航天大学 | Improved CLSVOF method for quickly building signed distance function by VOF function |
| CN109165423A (en) * | 2018-08-03 | 2019-01-08 | 北京航空航天大学 | It is a kind of based on stream function around Flowing Field modeling method |
| WO2020244217A1 (en) * | 2019-06-06 | 2020-12-10 | 大连理工大学 | Improved mode decomposition method applicable to flow field analysis and reconstruction of internal solitary wave test |
| CN115310176A (en) * | 2022-07-18 | 2022-11-08 | 哈尔滨工业大学 | Two-dimensional flow field aerodynamic force reconstruction method based on OpenFOAM and derivative moment transformation |
| CN115630589A (en) * | 2022-10-17 | 2023-01-20 | 华中科技大学 | Two-dimensional steady laminar flow field rapid forecasting method based on convolutional neural network |
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| "Convolutional Neural Networks for Steady Flow Approximation";Guo Xiaoxiao;Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining;20160813;481-490 * |
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