CN115797654B - Bubble bottom parameter identification method in gas-liquid two-phase flow - Google Patents
Bubble bottom parameter identification method in gas-liquid two-phase flow Download PDFInfo
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Abstract
The invention discloses a bubble bottom parameter identification method in gas-liquid two-phase flow, which comprises the following steps: collecting and reading bubble image data to generate a bubble data set; processing the bubble data set to obtain a bubble parameter data set; calculating the bubble parameter data set to obtain a bubble bottom calculation result; and identifying the bubble bottom calculation result to obtain bubble bottom parameters. The method provided by the invention can accurately identify the relevant parameters of the bubble bottom and the drawing of the outline in the gas-liquid two-phase flow. The bubble motion images before and after the analysis processing can be clearly and truly identified by clearly observing the bubble bottom contact area. Further, the bubble motion characteristics, particularly the influence of the bubble bottom motion on heat transfer, can be analyzed and expressed from multiple dimensions according to the identified relationship of the bubble bottom contact surface, the bubble bottom circularity, the bubble bottom inclination angle and the bubble bottom diameter over time. Provides a theoretical support for exploring bubble dynamics.
Description
Technical Field
The invention belongs to the field of gas-liquid two-phase flow, and particularly relates to a method for identifying bubble bottom parameters in gas-liquid two-phase flow.
Background
The gas-liquid two-phase flow is widely applied to the fields of aerospace engineering, petrochemical engineering, nuclear energy engineering, energy engineering and power engineering, so that the gas-liquid two-phase flow has great significance. The gas-liquid two-phase flow has various types and frequent flow behavior change, and particularly the mass and energy transfer between the gas-liquid two phases in the boiling process are always in a nonlinear dynamic state. For the above reasons, it becomes difficult to analyze the gas-liquid flow characteristics in the gas-liquid two phases and the parameters related to the gas-liquid two phases. How to analyze and capture the motion characteristics and parameters of the gas-liquid two phases by utilizing the existing theory is very critical. At present, the research methods about the motion characteristics of bubbles in gas-liquid two-phase flow mainly comprise a numerical simulation method and an experimental method. The numerical simulation method is limited by the development of two-phase flow theory, and most of the numerical simulation methods adopt an empirical formula to predict the motion characteristics of bubbles, so that the numerical simulation method has certain limitation. Therefore, experimental methods are mostly adopted for researching the characteristics of bubbles in gas-liquid two-phase flow, and the current methods for measuring the parameters of the bubbles are divided into a contact type method and a non-contact type method, wherein the contact type method comprises an optical fiber probe method, a sampling probe method, a conductivity method and a phase-sensitive constant temperature velocimetry; non-contact methods include image processing, X-ray techniques, laser doppler interferometry, and the like. The greatest advantage of the non-contact method over the contact method is that the bubbles and the spatial flow field are not disturbed by the measuring device. With the development of high-speed cameras, image processing techniques are widely used for bubble parameter identification.
Although there are many methods for bubble image processing at present, there are still disadvantages. Firstly, for continuous bubbles in an image, a plurality of processing methods do not divide the continuous bubbles, and then the bubble bottom circularity, the bubble bottom inclination angle and the bubble bottom contact area are calculated; secondly, for flows like flow in narrow channels, the influence of channel size causes the growth of partial bubbles to be limited, and the parameter calculation of the limited bubbles is corrected according to the size effect. In summary, in the narrow channel, the conventional image recognition method cannot meet the calculation of the bubble characteristic parameters of the limited bubbles, and the parameter estimation error of the continuous bubbles is larger.
Disclosure of Invention
The invention aims to provide a bubble bottom parameter identification method in gas-liquid two-phase flow, so as to solve the problems in the prior art.
In order to achieve the above object, the present invention provides a method for identifying bubble bottom parameters in gas-liquid two-phase flow, comprising:
collecting and reading bubble image data to generate a bubble data set;
processing the bubble data set to obtain a bubble parameter data set;
calculating the bubble parameter data set to obtain a bubble bottom calculation result;
and identifying the bubble bottom calculation result to obtain bubble bottom parameters.
Preferably, the processing the bubble data set, and the process of obtaining the bubble parameter data set includes:
performing initialization definition processing on the bubble data set to generate an initialization data set;
extracting features of the initialized data set to obtain the bubble parameter data set;
the bubble parameter data set comprises an image processing global variable structure array, a window clipping size, an image frame rate and a pixel size, a background connected domain deletion threshold and a bubble grouping size.
Preferably, the process of calculating the bubble parameters and obtaining the bubble bottom calculation result includes:
reading the bubble parameter data set, and converting an image in the bubble parameter data set into a binary gray scale map;
processing and measuring the binarized gray level map to generate a vaporization core data set and a growth and slippage bubble data set;
and calculating the vaporization core data set and the growth and slippage bubble data set to obtain the bubble bottom calculation result.
Preferably, the process of converting the image in the bubble parameter dataset into a binary gray scale map includes:
extracting an image in the bubble parameter data set, converting the image into a double-precision value image,
and calculating the double-precision-value image based on thresholding to obtain the binarized gray level image.
Preferably, the process of processing and measuring the binarized gray scale map to generate a vaporized core dataset and a growth and slip bubble dataset includes:
removing the background color of the binarized gray scale map to generate a de-coloring gray scale map;
performing inverse color transformation and color gamut operation on the color-removed gray scale image to generate a color-removed binary image;
measuring the attribute of the de-coloring binary image, dividing the continuous bubble to generate the vaporization core data set and the growth and slippage bubble data set,
preferably, the process of calculating the vaporization core data set and the growth and slip bubble data set to obtain the bubble bottom calculation result includes:
calculating the vaporization core data set based on the pixel size to obtain vaporization core bubble bottom parameters;
calculating the slippage bubble data set based on the pixel size to obtain slippage bubble bottom parameters;
and calculating based on the vaporization core bubble bottom parameter and the sliding bubble bottom parameter to generate a bubble bottom calculation result.
Preferably, the process of identifying the bubble bottom calculation result and obtaining the bubble bottom parameter includes:
calculating a correlation degree based on the bubble bottom calculation result;
searching based on the correlation degree to generate a search target;
calculating real parameters of the bubble based on the retrieval target;
and obtaining the bubble bottom parameters based on the real parameters.
Preferably, the bubble bottom parameters include, but are not limited to, bubble bottom contact area, bubble bottom contact diameter, circularity of the bubble bottom liquid film, and contact angle of the bubble bottom liquid film.
The invention has the technical effects that: the method provided by the invention can accurately identify the relevant parameters of the bubble bottom and the drawing of the outline in the gas-liquid two-phase flow. The bubble motion images before and after the analysis processing can be clearly and truly identified by clearly observing the bubble bottom contact area. Further, the bubble motion characteristics, particularly the influence of the bubble bottom motion on heat transfer, can be analyzed and expressed from multiple dimensions according to the identified relationship of the bubble bottom contact surface, the bubble bottom circularity, the bubble bottom inclination angle and the bubble bottom diameter over time. Provides a theoretical support for exploring bubble dynamics.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a flow chart of a parameter identification method in an embodiment of the invention;
FIG. 2 is a schematic diagram showing the change of the contact area of the foam bottom with time according to the embodiment of the present invention;
FIG. 3 is a graph showing the change of the inclination angle of the bottom of the foam with time according to the embodiment of the present invention;
FIG. 4 is a graph showing the change of the bubble bottom circularity with time according to an embodiment of the present invention;
FIG. 5 is a graph showing the change of the diameter of the bubble bottom with time in the embodiment of the invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
Example 1
As shown in fig. 1, in this embodiment, a method for identifying bubble bottom parameters in gas-liquid two-phase flow is provided, including:
in this embodiment, taking an image acquired by a high-speed camera as an example, characteristic parameters of a bubble bottom liquid film of bubbles generated in gas-liquid two-phase flow are identified by a specific image processing technology, wherein the characteristic parameters include a bubble bottom contact area, a bubble bottom contact diameter, a circularity of a bubble bottom liquid film, a contact angle of the bubble bottom liquid film, and the like.
The picture and the required calculation parameters are initialized, and the initialization definition is mainly carried out for the calculation parameters, wherein the initialization definition comprises an image processing global variable structure array, a window shearing size, an image frame rate and a pixel size, a background connected domain deleting threshold value and a bubble grouping size.
In this embodiment, taking an image acquired by a high-speed camera as an example, the image is 448 pixels×512 pixels in size.
An image is read from a graphics file using imread.
In a further optimized scheme, the original image is automatically read through a computer program, and the read image is cached in a computer memory system. And simultaneously, using the structure array to store the image naming information and the image information for processing the picture naming completion, and consistent with the initial input picture.
And calculating the bubble sliding interval time through the image refreshing rate, and storing the bubble sliding interval time into a variable for calculating the bubble growth speed and the sliding speed.
The RGB image or the color map is converted into a gray map by utilizing an RGB2gray function, the image is converted into a double-precision value by utilizing an im2double function, and the two-dimensional gray image is binarized by further calling an immarize function through thresholding. And converting the RGB image into a gray scale image, and converting the image variable into a double-precision value for storage. The Gamma transformation is used to enhance the image, and the non-linear transformation is used to correct the overexposed and underexposed contents. The concrete transformation mode is as follows:
and removing the bubble fee contact area through removing the background color of the picture, and performing inverse color transformation and color gamut operation.
Global image thresholds were calculated using the Otsu method, normalized to the range [0,1]. The method comprises the following steps:
computing a histogram and probability for each intensity level
Setting omega i (0) Sum mu i (0) Initial value of (1)
All possible thresholds t=1..maximum intensity are traversed
Updating omega i Sum mu i
Calculation of
The required threshold corresponds to the maximum
Calculating two corresponding maxima, namely
Is maximum value +.>
Is a larger or equal maximum value
According to the global image threshold calculated using the Otsu method, an imminaize function in Matlab program is called to convert the gray image into a binary image using a threshold conversion method, all values above the global threshold are replaced with 1 and all other values are set to 0.
The continuous bubbles are divided and the connected areas are divided into two groups by measuring the attributes of the binary image areas, one group is a bubble core, and the other group is a growth and slippage bubble.
And further removing the cross-scale bubble area according to the size of the grouping bubbles to respectively obtain vaporization core bubbles and growth slipping bubbles.
According to the global image threshold calculated using the Otsu method, an imminaize function in Matlab program is called to convert the gray image into a binary image using a threshold conversion method, all values above the global threshold are replaced with 1 and all other values are set to 0.
In order to obtain parameters in the image more accurately, gaussian blur based on an imfilter function is needed to filter noise of the binary image. And detecting the edge defect bubbles and performing equivalent treatment. The specific Gaussian blur method of the two-dimensional space comprises the following steps:
where r is the blur radius r 2 ＝u 2 +v 2 Sigma is the standard deviation of the normal distribution. In two dimensions, the contour of the curved surface generated by this formula is a concentric circle that is normally distributed from the center. The convolution matrix of pixels with non-zero distribution is transformed from the original image. The value of each pixel is a weighted average of the values of surrounding neighboring pixels. The original pixel has the maximum Gaussian distribution value, so the adjacent pixels have the maximum weight, and the weights of the adjacent pixels are smaller as the adjacent pixels are farther from the original pixel. This blurring process preserves the edge effects higher than other equalization blurring filters.
The total area, total circularity, and total contact angle of the vaporized core bubble bottom were calculated.
The average area, average circularity, and average contact angle of the vaporized core bubble bottom were weighted.
The total area, total circularity, and total contact angle of the growth slip bubble bottom were calculated.
And removing the green background to obtain a bubble area, and removing the bubble non-contact area through inverse color transformation and color gamut operation.
The average contact area, average circularity and average contact angle of the growth slip bubble bottom were calculated by weighting.
The true area, true circularity, true tilt angle and true diameter of all bubble bottoms are further calculated according to the pixel size.
Image dilation and erosion were performed using mathematical morphology. And creating a square structural element with the width of 2 pixels, continuously using the structural element to perform multiple expansion on the bubble image, and performing multiple corrosion by using the same structural element on the basis of an expansion gray scale image to finally obtain the image of the complete form of the bubble.
Labeling connected components in the two-dimensional binary image, and deleting small bubbles from the binary image according to the connected quantity association operation.
The properties of the binary image areas are measured. The communicating vessels were divided into two groups, 1 group being the vaporisation core and 2 groups being the growth and slipping bubbles.
And calculating ovality and diameter of each bubble bottom one by one, storing the ovality and the diameter into a structure array, and calculating the average diameter, the average direction and the average ovality of the bubble bottoms of the vaporization core bubbles and the growth slip bubbles by using a cyclic weighted average.
And calculating the correlation degree, assigning the correlation degree to the maximum correlation degree, and recording the maximum correlation degree window. And searching the connected domain in the maximum correlation window to obtain the center of the connected domain. If there are a plurality of connected domains, the closest one of the areas is used as the search target.
As shown in fig. 2-5, the contact area, circularity, tilt angle, and bubble bottom contact diameter of all bubble bottoms were further calculated according to the pixel size.
And calculating the centroid of the bubble, and calculating the sliding speed of the bubble according to the centroid distance of the adjacent pictures.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. The method for identifying the bubble bottom parameters in the gas-liquid two-phase flow is characterized by comprising the following steps of:
collecting and reading bubble image data to generate a bubble data set;
processing the bubble data set to obtain a bubble parameter data set;
calculating the bubble parameter data set to obtain a bubble bottom calculation result;
identifying the bubble bottom calculation result to obtain bubble bottom parameters;
the bubble data set is processed, and the process of acquiring the bubble parameter data set comprises the following steps:
performing initialization definition processing on the bubble data set to generate an initialization data set;
extracting features of the initialized data set to obtain the bubble parameter data set;
the bubble parameter data set comprises an image processing global variable structure array, a window shearing size, an image frame rate, a pixel size, a background connected domain deletion threshold value and a bubble grouping size;
the bubble parameters are calculated, and the process of obtaining the bubble bottom calculation result comprises the following steps:
reading the bubble parameter data set, and converting an image in the bubble parameter data set into a binary gray scale map;
processing and measuring the binarized gray level map to generate a vaporization core data set and a growth and slippage bubble data set;
and calculating the vaporization core data set and the growth and slippage bubble data set to obtain the bubble bottom calculation result.
2. The method for identifying bubble bottom parameters in gas-liquid two-phase flow according to claim 1, wherein the process of converting the image in the bubble parameter dataset into a binary gray scale map comprises:
and extracting an image in the bubble parameter data set, converting the image into a double-precision value image, and calculating the double-precision value image based on thresholding to obtain the binarized gray level image.
3. The method for identifying bubble bottom parameters in gas-liquid two-phase flow according to claim 1, wherein the process of processing and measuring the binary gray scale map to generate a vaporization core dataset and a growth and slip bubble dataset comprises:
removing the background color of the binarized gray scale map to generate a de-coloring gray scale map;
performing inverse color transformation and color gamut operation on the color-removed gray scale image to generate a color-removed binary image;
and measuring the attribute of the de-coloring binary image, and dividing the continuous bubble to generate the vaporization core data set and the growth and slippage bubble data set.
4. The method for identifying bubble bottom parameters in gas-liquid two-phase flow according to claim 1, wherein the step of calculating the vaporization core data set and the growth and slip bubble data set to obtain the bubble bottom calculation result comprises the steps of:
calculating the vaporization core data set based on the pixel size to obtain vaporization core bubble bottom parameters;
calculating the slippage bubble data set based on the pixel size to obtain slippage bubble bottom parameters;
and calculating based on the vaporization core bubble bottom parameter and the sliding bubble bottom parameter to generate a bubble bottom calculation result.
5. The method for identifying a bubble bottom parameter in a gas-liquid two-phase flow according to claim 1, wherein the step of identifying the bubble bottom calculation result and obtaining the bubble bottom parameter comprises the steps of:
calculating a correlation degree based on the bubble bottom calculation result;
searching based on the correlation degree to generate a search target;
calculating real parameters of the bubble based on the retrieval target;
and obtaining the bubble bottom parameters based on the real parameters.
6. The method for identifying bubble bottom parameters in gas-liquid two-phase flow according to claim 1, wherein the bubble bottom parameters include, but are not limited to, bubble bottom contact area, bubble bottom contact diameter, circularity of bubble bottom liquid film, and contact angle of bubble bottom liquid film.
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| JP5086563B2 (en) * | 2006-05-26 | 2012-11-28 | オリンパス株式会社 | Image processing apparatus and image processing program |
| CN202599852U (en) * | 2012-04-23 | 2012-12-12 | 长安大学 | Identification and measurement device of bubbles in gas-liquid two-phase bubble flow |
| CN107705283B (en) * | 2017-06-14 | 2020-11-17 | 华北理工大学 | Particle and bubble collision detection method based on Otsu image segmentation |
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| CN113763395B (en) * | 2021-08-31 | 2023-10-17 | 中国长江三峡集团有限公司 | Method for analyzing cavitation bubble dynamics based on image |
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