CN112948966A - Automatic detection method for wing section resistance integral area - Google Patents

Automatic detection method for wing section resistance integral area Download PDF

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CN112948966A
CN112948966A CN202110147110.0A CN202110147110A CN112948966A CN 112948966 A CN112948966 A CN 112948966A CN 202110147110 A CN202110147110 A CN 202110147110A CN 112948966 A CN112948966 A CN 112948966A
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魏斌斌
高永卫
胡豹
郝礼书
李可心
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Northwestern Polytechnical University
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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 airfoil resistance integral area is fixed, the influence of human factors is eliminated, the objectivity is improved, the efficiency of detecting the resistance integral area is improved, the efficiency of detecting the airfoil resistance integral area is effectively improved, and the time is saved.

Description

Automatic detection method for wing section resistance integral area
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, Zhouyixing and Wu Xiang discuss the study of an airfoil experimental resistance measurement method (No. 4 of pneumatic experiment and measurement control 1995) to discuss how to use a momentum method to measure airfoil resistance, and a novel tail rake is designed to accurately measure a tail area under the condition of a large attack angle. The zhizhenli and the jiaozui respectively discuss a measuring method of airfoil resistance in the numerical calculation research of an airfoil test resistance measuring method (the 14 th 2010 of scientific technology and engineering) and the accurate measurement research of two-dimensional configuration resistance under high lift force (the 2 nd 25 of 2011 of experimental hydromechanics). 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. Xunsiwen et al discussed a resistance measurement technique of airfoil transonic velocity in "experimental resistance measurement study 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 jiaozhen in the document "numerical calculation research of airfoil test resistance measurement method" states that when integrating the pressure in the model wake 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 for the automatic selection of the resistance integration area.
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, and X is [ X ]1，x2，……，xn]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 ═ Y1，y2，……，yn]。
In the formula (1), the coordinate X of the pressure measuring point of the tail rake is ═ X1，x2，……，xn]Wherein x is1，x2，……，xnOne 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 the y equal to 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 solution is carried out by a least square method to obtain the intercept C to be solved:
C＝(XTX)-1·XTY (2)
step 2: error calculation for each sample point:
determining an estimate of pressure Y of a tail rake
Figure BDA0002930665990000021
Figure BDA0002930665990000022
In the formula (I), the compound is shown in the specification,
Figure BDA0002930665990000023
y is an estimate of y.
Obtaining an estimated value
Figure BDA0002930665990000024
Error σ for each sample pointiObtained by the formula (4):
σi＝|yi-yi| (4)
and step 3: calculating a least squares solution error:
solving Y and fitting result by formula (5)
Figure BDA0002930665990000025
Error σ of the least-squares solution therebetween;
Figure BDA0002930665990000026
and 4, step 4: noise point detection:
assuming that the total pressure of the trail region is negative, the given noise determination threshold value K is 2 σ, and the number of continuous points M is 5, the following method is used to determine whether the sample point is a noise point.
Firstly, the method comprises the following steps: traversing all sample points, the sample point error σ calculated in step 2iGreater than a given noise decision threshold K, i.e. sigmai>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: and (3) 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: and (3) 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 form, an experimental state is a natural transition, an attack angle α is 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 is processed using the algorithm in fig. 2, and the threshold K is chosen to be 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, i.e. the total pressure in the wake should be negative compared to the line fitted.
The invention firstly provides a basic algorithm for automatically detecting an integral area, 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 K2 σ and M5, and the processing results are shown in fig. 6. Therefore, the method and the device have the advantages that manual judgment is eliminated, the integral area can be automatically found, and the efficiency is improved.
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 is 3 σ, the pressure in the wake region is not required and the continuity of noise points is not considered;
FIG. 4 is the result of automatic identification when the total pressure of the wake region is negative and the continuity of noise is not considered when K is 3 σ;
FIG. 5 is the result of automatic identification when K is 2 σ, the total pressure of the wake region is negative and the continuity of noise points is not considered;
fig. 6 is an automatic identification result in which the total pressure of the wake region is negative and the continuity of noise points is considered when K is 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 the 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 is [ X ]1，x2，……，xn]N is the total number of tail rake pressure measuring points, and in the embodiment, n is 198; c is the intercept to be solved; y is the measured pressure of the tail rake, Y ═ Y1，y2，……，yn]。
In the formula (1), the coordinate X of the pressure measuring point of the tail rake is ═ X1，x2，……，xn]Wherein x is1，x2，……，xnOne 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, and therefore, there is only one unknown parameter. The number of the equations is the same as the number of the sample points in the sample data, and the sample points are the read data measured by the tail rake. In this embodiment, the number of raw data measured by the tail rake is n 198, so that in equation (1), the number of equations is greater than the number of unknown parameters, which is a contradiction equation set, and the solution is performed by using the least square method to obtain the intercept C to be solved:
C＝(XTX)-1·XTY (2)
step 2: error calculation for each sample point:
estimate of Y
Figure BDA0002930665990000051
Obtained by the formula (3):
Figure BDA0002930665990000052
in the formula (I), the compound is shown in the specification,
Figure BDA0002930665990000053
y is an estimate of y.
After obtaining the estimated value Y, the error sigma of each sample pointiObtained by the formula (4):
σi＝|yi-yi| (4)
and step 3: calculating a least squares solution error:
solving Y and fitting result by formula (5)
Figure BDA0002930665990000054
Error σ of the least-squares solution therebetween;
Figure BDA0002930665990000055
and 4, step 4: noise point detection:
when the total pressure of the trail region is negative, the given noise determination threshold value K is 2 σ and the number of continuous points M is 5, the following method is used to determine whether the sample point is a noise point.
Firstly, the method comprises the following steps: traversing all sample points, the sample point error σ calculated in step 2iGreater than a given noise decision threshold K, i.e. sigmai>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: and (3) 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 ends and step 6 is entered.
Step 6: and (3) detecting a resistance integration area:
and 5, after the iteration of the step 5 is finished, a region formed on the X coordinate among the screened noise points is a final resistance integration region 2, and 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 integration area is characterized by comprising the following specific steps:
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;
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, and X is [ X ]1，x2，……，xn]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 ═ Y1，y2，……，yn]；
Step 2: error calculation for each sample point:
determining an estimate of pressure Y of a tail rake
Figure FDA0002930665980000011
Figure FDA0002930665980000012
In the formula (I), the compound is shown in the specification,
Figure FDA0002930665980000013
y is an estimate of y;
obtaining an estimated value
Figure FDA0002930665980000014
Error σ for each sample pointiObtained by the formula (4):
σi＝|yi-yi| (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);
Figure FDA0002930665980000015
and 4, step 4: noise point detection:
setting the total pressure of the trail area as negative, setting a noise judgment threshold value K as 2 sigma and the number of continuous points M as 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 2iGreater than a given noise decision threshold K, i.e. sigmai>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 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: and (3) 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 region as claimed in claim 1, whereinThe unknown quantity X ═ X in the above formula (1)1，x2，……，xn]Wherein x is1，x2，……，xnOne equation is respectively used, and the number of X 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＝(XTX)-1·XTY (2)。
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GB615318A (en) * 1944-06-07 1949-01-05 Glenn L Martin Co Improvements in or relating to low drag airfoils
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US20160009379A1 (en) * 2013-12-05 2016-01-14 The Boeing Company One-Piece Composite Bifurcated Winglet
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

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