CN106682278A - Supersonic flow field predicting accuracy determination device and method based on image processing - Google Patents
Supersonic flow field predicting accuracy determination device and method based on image processing Download PDFInfo
- Publication number
- CN106682278A CN106682278A CN201611111411.3A CN201611111411A CN106682278A CN 106682278 A CN106682278 A CN 106682278A CN 201611111411 A CN201611111411 A CN 201611111411A CN 106682278 A CN106682278 A CN 106682278A
- Authority
- CN
- China
- Prior art keywords
- flow field
- graph
- numerical simulation
- image
- schlieren
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012545 processing Methods 0.000 title claims abstract description 26
- 238000004088 simulation Methods 0.000 claims abstract description 84
- 238000002474 experimental method Methods 0.000 claims abstract description 81
- 230000035939 shock Effects 0.000 claims abstract description 61
- 238000000605 extraction Methods 0.000 claims abstract description 8
- 238000009826 distribution Methods 0.000 claims description 23
- 230000002596 correlated effect Effects 0.000 claims description 7
- 238000005259 measurement Methods 0.000 claims description 7
- 238000007781 pre-processing Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 2
- 238000012360 testing method Methods 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 2
- 230000000875 corresponding effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000011496 digital image analysis Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
Classifications
-
- 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
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/30—Noise filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Geometry (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Computer Hardware Design (AREA)
- Image Analysis (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
The invention discloses a supersonic flow field predicting accuracy determination device and method based on image processing. The method comprises the steps that 1, a supersonic flow field schlieren experiment map and a numerical simulation map are obtained; 2, the schlieren experiment map is adopted as a reference, the schlieren experiment map and the numerical simulation map are aligned; 3, flow field shock wave lines in the schlieren experiment map and the numerical simulation map are extracted respectively; 4, position coordinates of points forming the flow field shock wave lines in the schlieren experiment map and the numerical simulation map are extracted respectively, the curve fitting degree of the two flow field shock wave lines are calculated, and the similarity of the schlieren experiment map and the numerical simulation map is determined. According to the flow field image obtained through a schlieren experiment and a cloud atlas predicted by flow field simulation, firstly, images with fully-corresponding sizes and positions are obtained by matching characteristic points. Flow field characteristic lines needing to be researched are obtained through characteristic line extraction, and finally the flow field similarity of the schlieren experiment map and the numerical simulation map is obtained through curve fitting, so that flow field simulation predicting result accuracy determination is achieved by means of the flow field schlieren experiment result.
Description
[ technical field ] A method for producing a semiconductor device
The invention belongs to the field of aerospace, relates to an image processing method, and particularly relates to a device and a method for judging the accuracy of simulation of an ultrasonic flow field through a schlieren experiment result.
[ background of the invention ]
In the supersonic flow field prediction of an aircraft, the correctness of the flow field prediction needs to be quantitatively judged. How to judge the accuracy of the flow field simulation prediction result by using the schlieren experiment result of the flow field is a problem which needs to be solved urgently in aerodynamic research.
The method utilizes a flow field image obtained by a schlieren experiment and a cloud image of flow field simulation prediction, processes and analyzes the image through a computer, thereby identifying the similarity of the experimental image and the numerical simulation image, establishing a reasonable accuracy criterion according to the physical characteristics of the flow field and giving the accuracy of the flow field prediction.
[ summary of the invention ]
The invention provides a device and a method for judging the prediction accuracy of an ultrasonic flow field based on image processing.
The invention adopts the following technical scheme:
a supersonic flow field prediction accuracy judgment method based on image processing comprises the following steps:
(1) acquiring a schlieren experiment graph and a numerical simulation graph of the supersonic flow field;
(2) registering the numerical simulation graph and the schlieren experimental graph by taking the schlieren experimental graph as a reference;
(3) respectively extracting flow field shock wave lines in the schlieren experiment graph and the numerical simulation graph;
(4) and respectively extracting the position coordinates of all the composition points of the flow field shock wave lines in the schlieren experiment graph and the numerical simulation graph, so as to calculate the curve fitting degree of the two flow field shock wave lines and determine the similarity of the schlieren experiment graph and the numerical simulation graph.
In the step (1), after acquiring a schlieren experiment graph and a numerical simulation graph of the supersonic flow field, denoising and gray level processing are carried out on the schlieren experiment graph and the numerical simulation graph, and gray level analysis is respectively carried out on the image through a gray level distribution histogram and gray level distribution statistic of the image.
In the step (1), after acquiring a schlieren experiment chart and a numerical simulation chart of the supersonic flow field, respectively performing the following steps:
(1.1) denoising the image;
(1.2) converting the image from the GBR space to a gray scale space to obtain a gray scale distribution histogram and gray scale distribution statistics;
and (1.3) judging the contrast of the image according to the gray value distribution condition on the gray histogram.
The specific method of the step 2 comprises the following steps: and (4) taking an inflection point in the image as a characteristic point, and realizing registration of the numerical simulation graph and the schlieren experiment graph through characteristic point detection and characteristic matching.
The specific method of the step 3 comprises the following steps:
(3.1) reading RGB values of the shock wave lines in the schlieren experiment graph and the numerical simulation graph respectively, and setting the area to be black;
(3.2) calculating a global threshold value of the image obtained in the step (3.1), and converting the gray-scale image into a binary image according to the global threshold value;
(3.3) shielding the part which does not need to be researched from the binary image obtained in the step (3.2), and reserving the area which specifically needs to be researched, wherein the area which specifically needs to be researched at least comprises the area of the shock wave line;
(3.4) multiplying each pixel point in the images obtained in the step (3.2) and the step (3.3) respectively to obtain a shock wave line;
and (3.5) identifying an edge line of the image, namely a flow field shock wave line to be researched by calling a bwmorphh deburring function.
Judging the similarity of the schlieren experiment graph and the numerical simulation graph by adopting a correlation coefficient, wherein the correlation coefficient is calculated according to the following formula:
wherein Cov (X, Y) is covariance, D (X) is variance of shock wave line in schlieren experiment graph, and D (Y) is variance of shock wave line in numerical simulation graph;
if the correlation coefficient is less than 0.3, the two variables have no straight line correlation; if the correlation coefficient is more than 0.3, the two variables are linearly correlated, wherein 0.3 to 0.5 are low correlation, 0.5 to 0.8 are significant correlation, and 0.8 to 1 are high correlation; the closer the absolute value of the correlation coefficient is to 1, the more correlated the two variables are.
Judging the similarity of the schlieren experiment graph and the numerical simulation graph by using a determination coefficient, wherein the determination coefficient is calculated according to the following formula:
wherein,
SSE is the sum of the squares, SSR is the sum of the squares of the differences between the simulated plot data and the mean of the experimental plot data, SST is the sum of the squares of the differences between the experimental plot data and the mean of the experimental plot data, wiIs the weight of the ith point of the shock line,is the ordinate of the ith point of the shock wave line in the numerical simulation graph,is the mean value of the ordinate of the ith point of the shock wave line in the schlieren experiment chart, yiIs the ordinate of the ith point of the shock wave line in the schlieren experiment chart, and n is the number of the shock wave line composition points;
and determining whether the coefficient represents the fitting quality of the two groups of data, wherein the closer the value is to 1, the higher the fitting degree of the two groups of data is.
Judging the similarity of the schlieren experiment graph and the numerical simulation graph by adopting a root mean square error, wherein the root mean square error is calculated according to the following formula:
wherein, wiIs the weight of the ith point of the shock line, yiIs the ordinate of the ith point of the shock wave line in the schlieren experiment chart,is the ordinate of the ith point of the shock wave line in the numerical simulation graph; n is the number of shock line composition points.
The root mean square error reflects the degree of deviation of the measured data from the true value, and the smaller the value, the higher the measurement accuracy.
An ultrasonic flow field prediction accuracy judging device based on image processing comprises an image preprocessing module, a characteristic point matching module, a characteristic line extraction module and an image similarity identification module which are connected in sequence, wherein,
the image preprocessing module is used for acquiring a schlieren experiment graph and a numerical simulation graph of the supersonic flow field and performing noise reduction and gray level processing on the image;
the characteristic point matching module is used for registering the numerical simulation graph and the schlieren experiment graph, wherein the schlieren experiment graph is taken as a reference;
the characteristic line extraction module is used for extracting flow field shock wave lines in the schlieren experiment graph and the numerical simulation graph;
an image similarity identification module: the method is used for extracting the position coordinates of all the composition points of the flow field shock wave lines in the schlieren experiment graph and the numerical simulation graph, so that the curve fitting degree of the two flow field shock wave lines is calculated, and the similarity of the schlieren experiment graph and the numerical simulation graph is determined.
And judging the similarity of the schlieren experiment graph and the numerical simulation graph according to the correlation coefficient or the determination coefficient or the root mean square error.
The invention has the following beneficial effects: according to the method, the flow field image obtained by the schlieren experiment and the cloud image of the flow field simulation prediction are subjected to feature point matching to obtain the image with the completely corresponding size and position, the flow field feature line to be researched is obtained through feature line extraction, and finally the flow field similarity of the schlieren experiment image and the flow field similarity of the numerical simulation image are obtained through curve fitting, so that the accuracy judgment of the flow field simulation prediction result by using the schlieren experiment result of the flow field is realized.
[ description of the drawings ]
FIG. 1 is a flow chart of the determination method of the present invention.
FIG. 2 shows the results of the test applied to the examples of the present invention: the method comprises the following steps of (a) respectively representing a schlieren experiment graph and a numerical simulation graph of an ultrasonic flow field, (c) respectively representing a gray distribution histogram of the schlieren experiment graph and the numerical simulation graph of the ultrasonic flow field, (e) respectively representing a feature point selection graph of the schlieren experiment graph and the numerical simulation graph, (f) respectively representing an image after the registration of the numerical simulation graph, and (h) respectively representing a shock wave line extracted from the schlieren experiment graph and the numerical simulation graph.
[ detailed description ] embodiments
The invention provides a device and a method for judging the accuracy of supersonic flow field prediction based on image processing, which provide a tool for quantitative analysis of supersonic flow field prediction.
The detailed process of the accuracy judgment of the flow field simulation prediction result based on the flow field schlieren experiment result will be clearly and completely described below with reference to the attached drawings.
(1) The image preprocessing module is used for preprocessing an input image, removing some interference information irrelevant to a flow field and enhancing the characteristic information of the flow field image;
(2) the characteristic point matching module is used for identifying characteristic points in the two images and determining the one-to-one corresponding relation between each characteristic point so as to realize the calibration of the size and the position of the image;
(3) the characteristic line extraction module is used for extracting the concerned shock wave characteristic line from the matched flow field image;
(4) and the image similarity identification module is used for determining the image similarity between the input image and the data original image through curve fitting degree calculation based on the positions of all the extracted component points of the shock wave lines.
The image preprocessing module in the step (1): the method comprises the steps of carrying out noise reduction processing and gray scale processing on an image to be identified (namely a schlieren experiment graph and a numerical simulation graph), and carrying out gray scale analysis on the image through a gray scale distribution histogram and gray scale distribution statistic of the image. Specifically, a Gaussian filter is adopted to perform noise reduction processing on an image, the image is converted into a gray scale space from an RGB space, a gray scale distribution histogram and gray scale distribution statistics are obtained, and then the gray scale distribution characteristics of the image are analyzed. The schlieren test chart and the numerical simulation chart are shown in (a) and (b) of fig. 2. The gray distribution histograms of the schlieren test chart and the numerical simulation chart are shown in (c) and (d). The gray scale distribution statistics of the image are shown in table 1.
The contrast of the image can be judged according to the gray value distribution condition on the gray histogram. As can be seen from fig. 2(c) and 2(d), the gray scale values of the schlieren test chart are mainly distributed in the range of 70 to 230, wherein the peak 1636 is included at the gray scale value of 83, and the peak 909 is included at the gray scale value of 137, which are the two most main gray scale distributions on the image, and this is consistent with the schlieren test chart. The gray values of the numerical simulation plots are mainly distributed over the full field, with peak 13669 at gray value 3 and peak 19030 at gray value 255, being the two most dominant gray distributions on the image, which is consistent with the numerical simulation plots. The contrast of the numerical simulation graph is higher than that of the schlieren experiment graph, which can be obtained by the gray distribution.
TABLE 1 Gray level distribution statistics of schlieren experiment plots
| Mean value | Standard deviation of | Smoothness of the surface | Third moment | Consistency | Entropy of the entropy | |
| Experiment of | 123.2527 | 33.5238 | 0.9991 | 0.0584 | 0.0158 | 6.4022 |
| Simulation (Emulation) | 121.5632 | 116.0401 | 0.9999 | 4.6071 | 0.2386 | 3.7340 |
| Phase difference | -1.6895 | 82.5163 | 0.0008 | 4.5487 | 0.2228 | -2.6682 |
Table 1 reflects some basic characteristics of the schlieren experiment chart and the numerical simulation chart. As can be seen from table 1, the mean and smoothness of the two figures are less different. The standard deviation of the schlieren experiment chart is small, and the standard deviation of the numerical simulation chart is large, which is consistent with the contrast of the image.
Step (2) feature point matching module: and registering the numerical simulation graph and the schlieren experimental graph by taking the schlieren experimental graph as a reference. Specifically, a manual registration method based on feature points is adopted, five feature points are manually selected from an image, a cpselect function is utilized to detect the feature points, feature matching is carried out, and a numerical simulation graph and a schlieren experiment graph are registered. Thereby achieving a perfect match of the size and position of the two images. The selected feature points are turning points in the image, so that the feature points can be easily distinguished and correctly detected. The feature points are selected as shown in fig. 2(e) and 2 (f). Fig. 2(g) is the registered image.
And (3) a characteristic line extraction module: a threshold segmentation method in an RGB vector space is adopted, and flow field shock wave lines concerned in a schlieren experiment graph and a numerical simulation graph are respectively extracted through a manual selection method. In particular, the amount of the solvent to be used,
(1) reading the approximate range of R, G and B values of a characteristic line (namely a flow field shock wave line) needing to be reserved by using a Data Cursor tool in a schlieren experiment graph and a numerical simulation graph respectively, and setting a pixel point region with the RGB value of 140-162 of an image as 0, namely black according to the approximate range;
(2) calculating a global threshold of the obtained image according to a function level (graythresh) (X), and then converting the gray scale map into a binary map, wherein X is the image and level represents the global threshold of the image.
(3) And (3) shielding the part which does not need to be researched by using a Mask according to the obtained binary image, reserving the area which specifically needs to be researched (the area is an area at least containing a shock wave line), wherein the area reserved by the Mask is white, and the other areas are black.
(4) And (4) multiplying the binary images obtained in the step (2) and the step (3), namely multiplying the numerical values of all pixel points in the image, and ensuring that the processed image only keeps the shock wave line.
(5) And calling a bwmorphh deburring function, and automatically identifying the edge line of the image, namely the flow field shock wave line to be researched by the computer. The edge detection results are shown in fig. 2(h) and 2 (i).
And (4) an image similarity identification module: and respectively extracting the position coordinates of all the composition points of the shock lines of the two images, and calculating the curve fitting degree, thereby determining the image similarity between the input image and the original data image. The fitting degree of the curve is judged by determining the coefficient and the root mean square error through the correlation coefficient respectively, and the formula is as follows.
Correlation coefficient:
the correlation coefficient reflects the degree of similarity of the two sets of data. Wherein Cov (X, Y) is covariance, D (X) is variance of shock wave line in schlieren experiment picture, and D (Y) is variance of shock wave line in numerical simulation picture. If the correlation coefficient is less than 0.3, the two variables have no straight line correlation. If the correlation coefficient is greater than 0.3, the two variables are linearly correlated. Where 0.3 to 0.5 is low correlation, 0.5 to 0.8 is significant correlation, and 0.8 to 1 is high correlation. The closer the absolute value of the correlation coefficient is to 1, the more correlated the two variables are. The correlation coefficient of the two images measured in this example was 0.9994, so the two images were highly correlated.
Determining the coefficient (R-square):
wherein,
in the above formula, SSE is the sum-of-square variance, SSR is the sum of squares of the differences between the simulated chart data and the mean of the experimental chart data, SST is the sum of squares of the differences between the experimental chart data and the mean of the experimental chart data, wiIs the weight of the ith point of the shock line,is the ordinate of the ith point of the shock wave line in the numerical simulation graph,is the mean value of the ordinate of the ith point of the shock wave line in the schlieren experiment chart, yiIs the ordinate of the ith point of the shock wave line in the schlieren experiment chart, and n is the number of the shock wave line composition points;
and determining whether the coefficient represents the fitting quality of the two groups of data, wherein the closer the value is to 1, the higher the fitting degree of the two groups of data is. The determination coefficient of the two images measured in this embodiment is 1.0869, so the fitting degree of the two images is high.
Root Mean Square Error (RMSE):
root Mean Square Error (RMSE) is the square root of the ratio of the square of the deviation of an observed value from a true value to the number of observations, which in actual measurement is always finite, the true value can only be replaced by the most reliable (best) value. The root mean square error is very sensitive to the response of extra large or extra small errors in a set of measurements, so the root mean square error can well reflect the precision of the measurement. The root mean square error reflects the degree to which the measured data deviates from the true value, and the smaller the value, the higher the measurement accuracy, so the root mean square error can be used as a standard for evaluating the accuracy of the measurement process. The root mean square error measured in this example is 3.1742, and it can be seen that the error of the two curves is small and the similarity is high.
The invention finds an effective supersonic flow field accuracy judging technology. Applied to this example, the correlation coefficient of the schlieren test chart and the numerical simulation chart was 0.9994, the determination coefficient (R-square) was 1.0869, and the Root Mean Square Error (RMSE) was 3.1742. Therefore, the similarity of the flow fields in the two graphs is high, and the fitting degree is good.
The supersonic flow field accuracy judging technology provided by the invention can effectively identify the similarity of the schlieren experiment graph and the numerical simulation graph of the supersonic flow field, thereby realizing the accuracy judgment of the flow field simulation prediction result by using the schlieren experiment result of the flow field, being applicable to the accuracy judgment of different flow field predictions, and having high identification efficiency and strong adaptability.
The invention has the advantages that:
(1) the invention takes the schlieren experiment chart and the numerical simulation chart of the supersonic flow field as the image source, and the image acquisition method is simple and effective and is easy to process;
(2) the invention judges the correctness of flow field prediction by computer image analysis and processing, the processing speed is high, and the calculation result is accurate:
(3) the invention designs an extraction algorithm of flow field characteristic lines based on RGB vector space threshold segmentation, and has high identification efficiency and adaptability;
(4) the method can be applied to different flow fields, and has important value for judging the correctness of the flow field prediction of the supersonic aircraft.
Claims (10)
1. A supersonic flow field prediction accuracy judgment method based on image processing is characterized in that: the method comprises the following steps:
(1) acquiring a schlieren experiment graph and a numerical simulation graph of the supersonic flow field;
(2) registering the numerical simulation graph and the schlieren experimental graph by taking the schlieren experimental graph as a reference;
(3) respectively extracting flow field shock wave lines in the schlieren experiment graph and the numerical simulation graph;
(4) and respectively extracting the position coordinates of all the composition points of the flow field shock wave lines in the schlieren experiment graph and the numerical simulation graph, so as to calculate the curve fitting degree of the two flow field shock wave lines and determine the similarity of the schlieren experiment graph and the numerical simulation graph.
2. The method for determining the prediction accuracy of the supersonic flow field based on image processing according to claim 1, wherein: in the step (1), after acquiring a schlieren experiment graph and a numerical simulation graph of the supersonic flow field, denoising and gray level processing are carried out on the schlieren experiment graph and the numerical simulation graph, and gray level analysis is respectively carried out on the image through a gray level distribution histogram and gray level distribution statistic of the image.
3. The method for determining the prediction accuracy of the supersonic flow field based on image processing according to claim 1 or 2, wherein: in the step (1), after acquiring a schlieren experiment chart and a numerical simulation chart of the supersonic flow field, respectively performing the following steps:
(1.1) denoising the image;
(1.2) converting the image from the GBR space to a gray scale space to obtain a gray scale distribution histogram and gray scale distribution statistics;
and (1.3) judging the contrast of the image according to the gray value distribution condition on the gray histogram.
4. The method for determining the prediction accuracy of the supersonic flow field based on image processing according to claim 1, wherein: the specific method of the step 2 comprises the following steps: and (4) taking an inflection point in the image as a characteristic point, and realizing registration of the numerical simulation graph and the schlieren experiment graph through characteristic point detection and characteristic matching.
5. The method for determining the prediction accuracy of the supersonic flow field based on image processing according to claim 1, wherein: the specific method of the step 3 comprises the following steps:
(3.1) reading RGB values of the shock wave lines in the schlieren experiment graph and the numerical simulation graph respectively, and setting the area to be black;
(3.2) calculating a global threshold value of the image obtained in the step (3.1), and converting the gray-scale image into a binary image according to the global threshold value;
(3.3) shielding the part which does not need to be researched from the binary image obtained in the step (3.2), and reserving the area which specifically needs to be researched, wherein the area which specifically needs to be researched at least comprises the area of the shock wave line;
(3.4) multiplying each pixel point in the images obtained in the step (3.2) and the step (3.3) respectively to obtain a shock wave line;
and (3.5) identifying an edge line of the image, namely a flow field shock wave line to be researched by calling a bwmorphh deburring function.
6. The method for determining the prediction accuracy of the supersonic flow field based on image processing according to claim 1, wherein: judging the similarity of the schlieren experiment graph and the numerical simulation graph by adopting a correlation coefficient, wherein the correlation coefficient is calculated according to the following formula:
wherein Cov (X, Y) is covariance, D (X) is variance of shock wave line in schlieren experiment graph, and D (Y) is variance of shock wave line in numerical simulation graph;
if the correlation coefficient is less than 0.3, the two variables have no straight line correlation; if the correlation coefficient is more than 0.3, the two variables are linearly correlated, wherein 0.3 to 0.5 are low correlation, 0.5 to 0.8 are significant correlation, and 0.8 to 1 are high correlation; the closer the absolute value of the correlation coefficient is to 1, the more correlated the two variables are.
7. The method for determining the prediction accuracy of the supersonic flow field based on image processing according to claim 1, wherein: judging the similarity of the schlieren experiment graph and the numerical simulation graph by using a determination coefficient, wherein the determination coefficient R-square is calculated according to the following formula:
wherein,
SSE is the sum-of-square variance, SSR is the sum of the squares of the differences between the simulated chart data and the mean of the experimental chart data, SST is the sum of the squares of the differences between the experimental chart data and the mean of the experimental chart data; w isiIs the weight of the ith point of the shock line,is the ordinate of the ith point of the shock wave line in the numerical simulation graph,is the mean value of the ordinate of the ith point of the shock wave line in the schlieren experiment chart, yiIs the ordinate of the ith point of the shock wave line in the schlieren experiment chart, and n is the number of the shock wave line composition points;
and determining whether the coefficient represents the fitting quality of the two groups of data, wherein the closer the value is to 1, the higher the fitting degree of the two groups of data is.
8. The method for determining the prediction accuracy of the supersonic flow field based on image processing according to claim 1, wherein: judging the similarity of the schlieren experiment graph and the numerical simulation graph by adopting a root mean square error, wherein the root mean square error is calculated according to the following formula:
wherein, wiIs the weight of the ith point of the shock line, yiIs the ordinate of the ith point of the shock wave line in the schlieren experiment chart,is the ordinate of the ith point of the shock wave line in the numerical simulation graph, and n is the number of the shock wave line composition points;
the root mean square error reflects the degree of deviation of the measured data from the true value, and the smaller the value, the higher the measurement accuracy.
9. The utility model provides a supersonic velocity flow field prediction accuracy decision maker based on image processing which characterized in that: an image preprocessing module, a characteristic point matching module, a characteristic line extracting module and an image similarity identifying module which are connected in sequence, wherein,
the image preprocessing module is used for acquiring a schlieren experiment graph and a numerical simulation graph of the supersonic flow field and performing noise reduction and gray level processing on the image;
the characteristic point matching module is used for registering the numerical simulation graph and the schlieren experiment graph, wherein the schlieren experiment graph is taken as a reference; the characteristic line extraction module is used for extracting flow field shock wave lines in the schlieren experiment graph and the numerical simulation graph;
an image similarity identification module: the method is used for extracting the position coordinates of all the composition points of the flow field shock wave lines in the schlieren experiment graph and the numerical simulation graph, so that the curve fitting degree of the two flow field shock wave lines is calculated, and the similarity of the schlieren experiment graph and the numerical simulation graph is determined.
10. The apparatus according to claim 9 for determining the prediction accuracy of a supersonic flow field based on image processing, wherein: and judging the similarity of the schlieren experiment graph and the numerical simulation graph according to the correlation coefficient or the determination coefficient or the root mean square error.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611111411.3A CN106682278B (en) | 2016-12-06 | 2016-12-06 | Supersonic flow field prediction accuracy decision maker and method based on image procossing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611111411.3A CN106682278B (en) | 2016-12-06 | 2016-12-06 | Supersonic flow field prediction accuracy decision maker and method based on image procossing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106682278A true CN106682278A (en) | 2017-05-17 |
| CN106682278B CN106682278B (en) | 2019-11-08 |
Family
ID=58867705
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611111411.3A Active CN106682278B (en) | 2016-12-06 | 2016-12-06 | Supersonic flow field prediction accuracy decision maker and method based on image procossing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106682278B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107977494A (en) * | 2017-11-20 | 2018-05-01 | 中国运载火箭技术研究院 | Gas handling system characteristic predicting method and system under hypersonic aircraft back-pressure |
| CN111257588A (en) * | 2020-01-17 | 2020-06-09 | 东北石油大学 | ORB and RANSAC-based oil phase flow velocity measurement method |
| CN112085651A (en) * | 2020-09-23 | 2020-12-15 | 中国空气动力研究与发展中心高速空气动力研究所 | Automatic shock wave detection and tracking algorithm based on image self-adaptive threshold and feature extraction |
| CN112241962A (en) * | 2020-10-19 | 2021-01-19 | 国网河南省电力公司电力科学研究院 | Method and system for calculating propagation speed of shock wave generated by discharge |
| CN112417709A (en) * | 2020-12-12 | 2021-02-26 | 西北工业大学 | Dynamic modal analysis method based on schlieren image |
| CN113324727A (en) * | 2019-07-16 | 2021-08-31 | 中国人民解放军空军工程大学 | Schlieren image processing method for compressed corner supersonic flow field structure |
| CN114383668A (en) * | 2022-03-24 | 2022-04-22 | 北京航空航天大学 | Variable background-based flow field measuring device and method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120275653A1 (en) * | 2011-04-28 | 2012-11-01 | Industrial Technology Research Institute | Method for recognizing license plate image, and related computer program product, computer-readable recording medium, and image recognizing apparatus using the same |
| CN103400378A (en) * | 2013-07-23 | 2013-11-20 | 清华大学 | Method for objectively evaluating quality of three-dimensional image based on visual characteristics of human eyes |
| CN103489161A (en) * | 2013-09-12 | 2014-01-01 | 南京邮电大学 | Gray level image colorizing method and device |
| CN103902782A (en) * | 2014-04-11 | 2014-07-02 | 北京理工大学 | POD (proper orthogonal decomposition) and surrogate model based order reduction method for hypersonic aerodynamic thermal models |
| CN104182272A (en) * | 2014-09-02 | 2014-12-03 | 哈尔滨工业大学 | Simulation testing platform and controlling method for hypersonic flight vehicle assessment |
| CN105825485A (en) * | 2016-03-30 | 2016-08-03 | 努比亚技术有限公司 | Image processing system and method |
-
2016
- 2016-12-06 CN CN201611111411.3A patent/CN106682278B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120275653A1 (en) * | 2011-04-28 | 2012-11-01 | Industrial Technology Research Institute | Method for recognizing license plate image, and related computer program product, computer-readable recording medium, and image recognizing apparatus using the same |
| CN103400378A (en) * | 2013-07-23 | 2013-11-20 | 清华大学 | Method for objectively evaluating quality of three-dimensional image based on visual characteristics of human eyes |
| CN103489161A (en) * | 2013-09-12 | 2014-01-01 | 南京邮电大学 | Gray level image colorizing method and device |
| CN103902782A (en) * | 2014-04-11 | 2014-07-02 | 北京理工大学 | POD (proper orthogonal decomposition) and surrogate model based order reduction method for hypersonic aerodynamic thermal models |
| CN104182272A (en) * | 2014-09-02 | 2014-12-03 | 哈尔滨工业大学 | Simulation testing platform and controlling method for hypersonic flight vehicle assessment |
| CN105825485A (en) * | 2016-03-30 | 2016-08-03 | 努比亚技术有限公司 | Image processing system and method |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107977494A (en) * | 2017-11-20 | 2018-05-01 | 中国运载火箭技术研究院 | Gas handling system characteristic predicting method and system under hypersonic aircraft back-pressure |
| CN113324727A (en) * | 2019-07-16 | 2021-08-31 | 中国人民解放军空军工程大学 | Schlieren image processing method for compressed corner supersonic flow field structure |
| CN113324727B (en) * | 2019-07-16 | 2023-05-05 | 中国人民解放军空军工程大学 | Schlieren image processing method for compressed corner supersonic flow field structure |
| CN111257588A (en) * | 2020-01-17 | 2020-06-09 | 东北石油大学 | ORB and RANSAC-based oil phase flow velocity measurement method |
| CN111257588B (en) * | 2020-01-17 | 2020-11-17 | 东北石油大学 | ORB and RANSAC-based oil phase flow velocity measurement method |
| CN112085651A (en) * | 2020-09-23 | 2020-12-15 | 中国空气动力研究与发展中心高速空气动力研究所 | Automatic shock wave detection and tracking algorithm based on image self-adaptive threshold and feature extraction |
| CN112241962A (en) * | 2020-10-19 | 2021-01-19 | 国网河南省电力公司电力科学研究院 | Method and system for calculating propagation speed of shock wave generated by discharge |
| CN112241962B (en) * | 2020-10-19 | 2022-07-26 | 国网河南省电力公司电力科学研究院 | Method and system for calculating propagation speed of laser wave generated by discharge |
| CN112417709A (en) * | 2020-12-12 | 2021-02-26 | 西北工业大学 | Dynamic modal analysis method based on schlieren image |
| CN114383668A (en) * | 2022-03-24 | 2022-04-22 | 北京航空航天大学 | Variable background-based flow field measuring device and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106682278B (en) | 2019-11-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106682278B (en) | Supersonic flow field prediction accuracy decision maker and method based on image procossing | |
| US8331650B2 (en) | Methods, systems and apparatus for defect detection | |
| CN104915963A (en) | Detection and positioning method for PLCC component | |
| CN105930852B (en) | A kind of bubble image-recognizing method | |
| CN107507170A (en) | A kind of airfield runway crack detection method based on multi-scale image information fusion | |
| CN109490306B (en) | Pork freshness detection method based on color and smell data fusion | |
| CN106557740B (en) | The recognition methods of oil depot target in a kind of remote sensing images | |
| CN105279772A (en) | Trackability distinguishing method of infrared sequence image | |
| CN105160355A (en) | Remote sensing image change detection method based on region correlation and visual words | |
| CN104794729A (en) | SAR image change detection method based on significance guidance | |
| CN102214290B (en) | License plate positioning method and license plate positioning template training method | |
| Wah et al. | Analysis on feature extraction and classification of rice kernels for Myanmar rice using image processing techniques | |
| CN104391294B (en) | A kind of radar plot correlating method based on connected component and template matches | |
| CN103729462B (en) | A kind of pedestrian retrieval method blocked based on rarefaction representation process | |
| CN104182768B (en) | The quality classification method of ISAR image | |
| CN104951765A (en) | Remote sensing image target division method based on shape priori information and vision contrast ratio | |
| CN101533466A (en) | Image processing method for positioning eyes | |
| CN115841488A (en) | Hole checking method of PCB (printed Circuit Board) based on computer vision | |
| CN107610119A (en) | The accurate detection method of steel strip surface defect decomposed based on histogram | |
| CN105374045B (en) | One kind is based on morphologic image given shape size objectives fast partition method | |
| CN105825215B (en) | It is a kind of that the instrument localization method of kernel function is embedded in based on local neighbor and uses carrier | |
| CN113269234B (en) | Connecting piece assembly detection method and system based on target detection | |
| CN105469099A (en) | Sparse-representation-classification-based pavement crack detection and identification method | |
| CN112633327A (en) | Staged metal surface defect detection method, system, medium, equipment and application | |
| CN114677428B (en) | Power transmission line icing thickness detection method based on unmanned aerial vehicle image processing |
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 |