CN112948966B - Automatic detection method for wing section resistance integral area - Google Patents
Automatic detection method for wing section resistance integral area Download PDFInfo
- Publication number
- CN112948966B CN112948966B CN202110147110.0A CN202110147110A CN112948966B CN 112948966 B CN112948966 B CN 112948966B CN 202110147110 A CN202110147110 A CN 202110147110A CN 112948966 B CN112948966 B CN 112948966B
- Authority
- CN
- China
- Prior art keywords
- points
- noise
- point
- sample
- resistance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M9/00—Aerodynamic testing; Arrangements in or on wind tunnels
- G01M9/06—Measuring arrangements specially adapted for aerodynamic testing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Geometry (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Fluid Mechanics (AREA)
- Length Measuring Devices With Unspecified Measuring Means (AREA)
Abstract
An automatic detection method for an airfoil resistance integral area is basically characterized in that 1, the total pressure of a wake area is negative; 2. only M consecutive points are noisy, and the region consisting of M consecutive points is a real noisy point. Under the condition of the same parameter setting, the solved wing profile resistance integral area is fixed, the influence of human factors is eliminated, objectivity is improved, the detection efficiency of the resistance integral area is improved, the detection efficiency of the wing profile resistance integral area is effectively improved, and time is saved.
Description
Technical Field
The invention relates to the field of data processing methods, in particular to an automatic detection method for an airfoil resistance integral region.
Background
In wind tunnel tests of airfoils, the airfoil resistance is usually measured by using a momentum method. The basic principle of using the momentum method to measure the airfoil resistance is as follows: during the test, the pressure (total pressure and static pressure) of a certain section of the model wake region is measured by using the tail harrow, and then the measured pressure data is integrated, so that the resistance of the airfoil profile is obtained.
In a low-speed wind tunnel experiment, zhou Ruixing and Xi Zhongxiang discuss the research of an airfoil experimental resistance measurement method (the 4 th stage of 1995 of aerodynamic experiment and measurement control) on how to measure airfoil resistance by using a momentum method, and a novel tail rake is designed to accurately measure a wake region under the condition of a large attack angle. The zhizhenli and Jiao Yuqin discuss the measurement method of the airfoil resistance in the numerical calculation research of the airfoil resistance test measurement method (the 14 th 2010 of science and technology and engineering) and the accurate measurement research of the two-dimensional configuration resistance under high lift force (the 2 nd 25 of 2011 of experimental hydrodynamics). In the numerical calculation research of the airfoil test resistance measurement method and the accurate measurement research of the two-dimensional configuration resistance under high lift, a resistance integral formula is corrected, so that the precision of the resistance measurement by a momentum method is improved. Xu Saiwen et al discussed the resistance measurement technology of airfoil transonic velocity in "experimental drag measurement research of airfoil transonic velocity based on wake method" (experimental hydrodynamics, 6 th 2019), and studied the influence of the front and rear positions and the up and down heights of the tail rake on the measurement result. Through these studies, resistance measurements using the momentum method have been developed, however, there are still areas to be improved in the specific implementation. For example, zhi zhe li and Jiao Yuqin in the "numerical calculation research of airfoil test resistance measurement method" indicate that when integrating the pressure in the model trail region, it is necessary to first select a suitable resistance integration region, that is, a pressure loss region. When selecting the resistance integration area, the current general method is to select the left end point and the right end point of the integration according to the artificial experience, and the method has strong subjectivity and depends heavily on manual judgment. No special investigation has been seen in the published literature regarding the automatic selection of the resistance integration zone.
Disclosure of Invention
In order to overcome the defects of low efficiency, time and labor waste caused by manual judgment in the prior art, the invention provides an automatic detection method for an integral area of airfoil resistance.
The specific process of the invention is as follows:
step 1: determining an intercept C to be solved:
taking the coordinates of the tail rake pressure measuring points and the obtained pressure of the tail rake pressure measuring points as sample data; fitting the sample data to determine the parameter C to be solved. The sample points are the data of the tail rake measurements that are read in.
Fitting the sample data by equation (1):
X·C＝Y (1)
in the formula: x is the coordinate of the pressure measuring point of the tail rake, X = [ X = [) 1 ，x 2 ，……，x n ]N is the total number of tail rake pressure measuring points; c is the intercept to be solved; y is the measured pressure of the tail rake, Y = [ Y = 1 ，y 2 ，……，y n ]。
In the formula (1), the coordinate X of the pressure measuring point of the tail rake is = [ X = 1 ，x 2 ，……，x n ]Wherein x is 1 ，x 2 ，……，x n One equation is respectively used, and the number of X is the number of equations.
During fitting, a linear form with the slope of 0 and y = C is taken, and the number of equations is the same as the number of sample points in sample data, so that the number of equations in the equation (1) is greater than the number of unknown parameters, and the intercept C to be solved is obtained by a least square method:
C＝(X T X) -1 ·X T Y (2)
step 2: error calculation for each sample point:
determining an estimate of pressure Y of a tail rake
In the formula (I), the compound is shown in the specification,
y is an estimate of y.
Obtaining an estimated value
Error σ for each sample point i Obtained by the formula (4):
σ i ＝|y i -y i | (4)
and step 3: calculating a least squares solution error:
solving Y and fitting result by formula (5)
Error σ of the least-squares solution therebetween;
and 4, step 4: noise point detection:
assuming that the total pressure of the wake region is negative, a noise determination threshold K =2 σ and the number of continuous points M =5, the following method is used to determine whether a sample point is a noise.
Firstly, the method comprises the following steps: traversing all sample points, the sample point error σ calculated in step 2 i Greater than a given noise decision threshold K, i.e. sigma i >Marking the sample point of K as an initial noise point;
secondly, the method comprises the following steps: and detecting whether the sequence numbers of the initial noise points are continuous or not.
When the number of continuous initial noise points is more than 5, marking the initial noise points as noise points; and the rest discontinuous initial noise points or the number of continuous points is less than or equal to 5, and the initial noise points are marked as sample points again.
And 5: noise point screening:
and if the noise exists, screening the noise, repeating the steps 1 to 4 on the rest sample points, and continuously screening the noise on a new iteration result.
If no noise is detected in the current round, the iteration is ended and step 6 is entered.
Step 6: detecting a resistance integration area:
after the iteration in step 5 is finished, the region formed on the X coordinate among the screened noise points is the final resistance integration region, and the regions formed on the X coordinate among the rest of the sample points are non-resistance integration regions.
And completing automatic detection of the airfoil resistance integration area.
Fig. 1 shows a common wake total pressure loss mode, an experimental state is a natural transition, an attack angle is α =0 °, and an integral region needs to be selected for accurately calculating a resistance coefficient. When the integral area is selected in the prior art, a large amount of manual judgment needs to be carried out, the efficiency is low, and time and labor are wasted. The automatic detection method for the airfoil resistance integral area provided by the invention reduces the influence of human factors, improves the efficiency and saves the time.
The data in fig. 1 was processed using the algorithm in fig. 2, with the threshold K chosen as K =3 σ, with the result shown in fig. 3.
As can be seen from fig. 3, the integrated area obtained using the above method has a clearly inaccurate place (the place at the dashed box) because the actual physical fact is not taken into account when calculating the error. In fact, there is always a pressure loss in the wake region, i.e. the total pressure in the wake region should be negative compared to the fitted line.
The invention firstly provides a basic algorithm for automatically detecting an integral region, and highlights two basic characteristics: 1. the total pressure of the wake area is negative; 2. only M consecutive points are noisy, and the region consisting of M consecutive points is a real noisy point.
The experimental data in fig. 1 were processed by the present invention, with parameters selected as K =2 σ and M =5, and the processing results are shown in fig. 6. Therefore, the method gets rid of manual judgment, can automatically find the integral area, and improves the efficiency.
Under the condition of the same parameter setting, the solved airfoil resistance integral area is fixed, so that the method gets rid of the artificial influence factor and has higher objectivity. The invention improves the detection efficiency of the resistance integration area: under the condition that a person has a large amount of resistance integration area detection experience and is skilled in mastering computer operation, 250s is needed for manually detecting the wing resistance integration area, while 3.4s is needed for using the method disclosed by the invention, so that the efficiency of detecting the wing resistance integration area is effectively improved.
Drawings
FIG. 1 is a typical airfoil wake total pressure loss;
FIG. 2 is a flow chart of the method for automatically detecting the airfoil resistance integral region without requiring pressure in the wake region and without considering noise continuity according to the present invention;
FIG. 3 is the result of automatic identification of the integral region of the resistance coefficient when K =3 σ, without requiring the pressure of the wake region and without considering the continuity of noise;
fig. 4 is the result of automatic identification when K =3 σ, the total pressure of the wake region is negative and the continuity of noise points is not considered;
fig. 5 is an automatic recognition result in which the total pressure of the wake region is negative and the continuity of noise is not considered when K =2 σ;
fig. 6 is an automatic recognition result in which the total pressure of the wake region is negative and the continuity of noise points is considered when K =2 σ;
FIG. 7 is a flowchart of an automatic detection method for an airfoil resistance integration region in the case of negative total pressure in the wake region and noise continuity consideration according to the technical solution of the present invention;
fig. 8 is a flow chart of the present invention.
In the figure: 1. a non-resistance integration region; 2. a resistance integration region.
Detailed Description
The invention relates to an automatic detection method for an airfoil resistance integral area, which comprises the following specific processes:
step 1: determining the intercept C to be solved
And determining the parameter C to be solved by inputting sample data and performing data fitting.
The sample data is the coordinates of the tail harrow pressure measuring point and the obtained pressure of the tail harrow pressure measuring point.
A straight line with a slope of 0 is selected, and sample data is fitted according to the equations (1) and (2).
X·C＝Y (1)
In the formula: x is the coordinate of the pressure measuring point of the tail rake, and X = [ X = 1 ，x 2 ，……，x n ]N is the total number of tail rake pressure measuring points, and n =198 in the embodiment; c is the intercept to be solved; y is the measured pressure of the tail rake, Y = [ Y ] 1 ，y 2 ，……，y n ]。
In the formula (1), the coordinate X of the pressure measuring point of the tail rake is = [ X = 1 ，x 2 ，……，x n ]Wherein x is 1 ，x 2 ，……，x n One equation is respectively used, and the number of X is the number of equations.
This embodiment selects a straight line with a slope of 0, i.e. y = C, so that there is only one unknown parameter. And the number of the equations is the same as that of the sample points in the sample data, and the sample points are the data measured by the read tail rake. In this embodiment, the number of raw data measured by the tail rake is n =198, and therefore, in equation (1), the number of equations is greater than the number of unknown parameters, which is a contradictory equation set, and the least square method is used to solve the contradictory equation set, so as to obtain the intercept C to be solved:
C＝(X T X) -1 ·X T Y (2)
step 2: error calculation for each sample point:
estimate of Y
Obtained by the formula (3):
in the formula (I), the compound is shown in the specification,
y is an estimate of y.
After obtaining the estimated value Y, the error sigma of each sample point i Obtained by the formula (4):
σ i ＝|y i -y i | (4)
and 3, step 3: calculating a least squares solution error:
solving Y and fitting result by formula (5)
Error σ of the least-squares solution therebetween;
and 4, step 4: noise point detection:
when the total pressure of the wake region is negative and a noise determination threshold K =2 σ and the number of consecutive points M =5 are given, whether the sample point is a noise is determined using the following method.
Firstly, the method comprises the following steps: traversing all sample points, the sample point error σ calculated in step 2 i Greater than a given noise decision threshold K, i.e. sigma i >Marking the sample point of K as an initial noise point;
secondly, the method comprises the following steps: and detecting whether the sequence numbers of the initial noise points are continuous or not.
When the number of continuous initial noise points is more than 5, marking the initial noise points as noise points; and the number of the other discontinuous initial noise points or the number of the continuous points is less than or equal to 5 and is marked as the sample point again.
And 5: noise point screening:
and if the noise exists, screening the noise, repeating the steps 1 to 4 on the rest sample points, and continuously screening the new iteration result on the noise.
If no noise is detected in the current round, the iteration ends and step 6 is entered.
Step 6: and (3) detecting a resistance integration area:
after the iteration of the step 5 is finished, the region formed on the X coordinate among the screened noise points is a final resistance integration region 2, and the regions formed on the X coordinate among the rest sample points are non-resistance integration regions 1.
Experiments prove that the method gets rid of a large number of manual judgment links, and has the advantages of fast convergence, high detection efficiency and strong practicability.
Claims (4)
1. An automatic detection method for an airfoil resistance integral region is characterized by comprising the following specific processes:
step 1: determining an intercept C to be solved:
taking the coordinates of the tail rake pressure measuring points and the obtained pressure of the tail rake pressure measuring points as sample data; fitting the sample data to determine the intercept C to be solved;
fitting the sample data by equation (1):
X·C＝Y (1)
in the formula: x is the coordinate of the pressure measuring point of the tail rake, X = [ X = [) 1 ，x 2 ，……，x n ]N is the total number of tail rake pressure measuring points; c is the intercept to be solved; y is the measured pressure of the tail rake, Y = [ Y ] 1 ，y 2 ，……，y n ]；
Step 2: error calculation for each sample point:
determining an estimate of the pressure Y of the tail rake
In the formula (I), the compound is shown in the specification,
is an estimate of y;
obtaining an estimated value
Error σ for each sample point i Obtained by the formula (4):
and step 3: calculating a least squares solution error:
solving the error sigma of the least square solution of the fitting result in the step 1 by an equation (5);
and 4, step 4: noise point detection:
setting the total pressure of the trail region as negative, setting a noise judgment threshold value K =2 sigma and the number of continuous points M =5, and judging whether the sample point is a noise point by using the following method;
firstly, the method comprises the following steps: traversing all sample points, the sample point error σ calculated in step 2 i Greater than a given noise decision threshold K, i.e.' sigma i >Marking the sample point of K as an initial noise point;
secondly, the method comprises the following steps: detecting whether the serial numbers of the initial noise points are continuous or not;
when the number of the continuous initial noise points is more than 5, marking the initial noise points as noise points; the number of the other discontinuous initial noise points or the number of the continuous points is less than or equal to 5 and is marked as a sample point again;
and 5: noise point screening:
if the noise point exists, screening the noise point, repeating the steps 1-4 on the rest sample points, and continuously screening the noise point on a new iteration result;
if no noise point is detected in the current round, the iteration is ended and the step 6 is entered;
step 6: and (3) detecting a resistance integration area:
after the iteration in the step 5 is finished, a region formed on the X coordinate among the screened noise points is a final resistance integration region, and regions formed on the X coordinate among the rest sample points are non-resistance integration regions;
and completing automatic detection of the airfoil resistance integration area.
2. The method as claimed in claim 1, wherein the sample point is read-in data of tail rake measurement.
3. The method for automatically detecting the airfoil resistance integration area according to claim 1, wherein the unknown quantity X = [ X ] in the formula (1) 1 ，x 2 ，……，x n ]Wherein x is 1 ，x 2 ，……，x n One equation, X, respectivelyThe number is the number of equations.
4. The method for automatically detecting the airfoil resistance integration region according to claim 1, wherein during fitting, a linear form with a slope of 0 and y = C is taken, and the number of equations is the same as the number of sample points in sample data, so that the number of equations in the formula (1) is greater than the number of unknown parameters, and the intercept C to be solved is obtained by solving through a least square method:
C＝(X T X) -1 ·X T Y (2)。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110147110.0A CN112948966B (en) | 2021-02-03 | 2021-02-03 | Automatic detection method for wing section resistance integral area |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110147110.0A CN112948966B (en) | 2021-02-03 | 2021-02-03 | Automatic detection method for wing section resistance integral area |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112948966A CN112948966A (en) | 2021-06-11 |
| CN112948966B true CN112948966B (en) | 2023-02-14 |
Family
ID=76241962
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110147110.0A Active CN112948966B (en) | 2021-02-03 | 2021-02-03 | Automatic detection method for wing section resistance integral area |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112948966B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB615318A (en) * | 1944-06-07 | 1949-01-05 | Glenn L Martin Co | Improvements in or relating to low drag airfoils |
| CN102749181A (en) * | 2012-07-19 | 2012-10-24 | 西北工业大学 | Wind tunnel test method on basis of momentum theory |
| CN109376418A (en) * | 2018-10-16 | 2019-02-22 | 湘潭大学 | A kind of pneumatic Robust Optimal Design method of small wind airfoil type suitable for turbulent flow operating condition |
| CN110610050A (en) * | 2019-09-18 | 2019-12-24 | 中国人民解放军国防科技大学 | Airfoil aerodynamic drag reduction method based on improved radial basis function deformation algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9738375B2 (en) * | 2013-12-05 | 2017-08-22 | The Boeing Company | One-piece composite bifurcated winglet |
-
2021
- 2021-02-03 CN CN202110147110.0A patent/CN112948966B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB615318A (en) * | 1944-06-07 | 1949-01-05 | Glenn L Martin Co | Improvements in or relating to low drag airfoils |
| CN102749181A (en) * | 2012-07-19 | 2012-10-24 | 西北工业大学 | Wind tunnel test method on basis of momentum theory |
| CN109376418A (en) * | 2018-10-16 | 2019-02-22 | 湘潭大学 | A kind of pneumatic Robust Optimal Design method of small wind airfoil type suitable for turbulent flow operating condition |
| CN110610050A (en) * | 2019-09-18 | 2019-12-24 | 中国人民解放军国防科技大学 | Airfoil aerodynamic drag reduction method based on improved radial basis function deformation algorithm |
Non-Patent Citations (4)
| Title |
|---|
| CFD vs. XFOIL of airfoil analysis at low reynolds numbers;Onur Günel等;《IEEE Xplore》;20170323;第628-632页 * |
| 基于尾迹法的翼型跨声速实验阻力测量研究;徐思文等;《实验流体力学》;20191215;第33卷(第06期);全文 * |
| 翼型试验阻力测量方法的数值计算研究;支真莉等;《科学技术与工程》;20100531;第10卷(第14期);第3384-3388页 * |
| 翼型阻力计算方法的数值模拟研究;周伟等;《科学技术与工程》;20111130;第11卷(第33期);第8229-8237页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112948966A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111709465B (en) | Intelligent identification method for rough difference of dam safety monitoring data | |
| WO2016155241A1 (en) | Method, system and computer device for capacity prediction based on kalman filter | |
| CN108801387B (en) | System and method for measuring remaining oil quantity of airplane fuel tank based on learning model | |
| CN102393881B (en) | A kind of high-precision detecting method of real-time many sensing temperatures data fusion | |
| CN111780948B (en) | Method for measuring transition process characteristic of aircraft boundary layer in hypersonic flight test | |
| CN113838054A (en) | Mechanical part surface damage detection method based on artificial intelligence | |
| CN111862054B (en) | Rivet contour point cloud extraction method | |
| CN108920861B (en) | Equivalent method for damage factor of structural unit containing crack beam | |
| CN105571645A (en) | Automatic dam monitoring method | |
| CN108052886A (en) | A kind of puccinia striiformis uredospore programming count method of counting | |
| CN107193782A (en) | A kind of method of abnormal value removing and correction fitted based on multinomial | |
| CN104535478A (en) | Hole blocking judging and result correcting method based on particle flow stability | |
| CN111912346B (en) | Nest hole online detection method suitable for robot drilling and riveting system on surface of airplane | |
| CN112948966B (en) | Automatic detection method for wing section resistance integral area | |
| CN109753634B (en) | Historical data steady-state value-based dynamic system gain estimation method | |
| CN112331266A (en) | Method for eliminating PCR fluorescence baseline period fluctuation | |
| CN118038981A (en) | Method and measuring instrument for extracting Cq value based on curvature change of qPCR amplification curve | |
| CN107312850A (en) | A kind of detection method of the invalid amplifications of PCR | |
| CN112046761B (en) | Airplane icing on-line detection method based on statistical test and filtering | |
| CN115455833B (en) | Pneumatic uncertainty characterization method considering classification | |
| CN111400826A (en) | Method and system for predicting ice shape at any moment | |
| CN105571558A (en) | Compressor blade spanwise waviness quantitative determination method | |
| CN113761814A (en) | Wing type transition region judgment method based on supervised learning model | |
| Morgan et al. | Vortex detection methods for use with PIV and CFD data | |
| CN112485335A (en) | Tomography method for detecting internal defects of trees based on ray segmentation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |