A high-speed image analysis method for the interaction structure between multiple jet flames

Abstract

The present invention provides a high-speed image analysis method suitable for identifying the interaction structure between flames in multi-jet flames, and belongs to the technical field of flame structure analysis. The present invention processes the flame self-luminescence / induced fluorescence image taken by a high-speed camera through a digital image processing method to identify the overlapping area between multi-jet flames, and specifically includes the following steps: selecting the analysis area, dividing the analysis area, Gaussian filtering, feature corner point detection, feature corner point supplementation, optical flow field calculation, flame area discrimination, flame outer contour recognition, and adjacent flame interaction extraction. The present invention discloses a high-speed image analysis method suitable for identifying the interaction structure between flames in multi-jet flames, which can identify the interaction structure between multiple flames from the flame image, and has high recognition efficiency. It can provide a method for further analyzing the interaction between flames and is very suitable for use in the field of flame structure research in multi-nozzle combustion chambers.

Description

Technical Field

[0001] The present invention relates to the technical field of flame structure analysis, and in particular to a high-speed image analysis method for interaction structures among multiple jet flames. Background Art

[0002] In modern combustion technology, with the continuous improvement of energy efficiency and environmental protection requirements, optimization of the combustion process has become a key research area. Multi-injection nozzle arrangements are often used in various combustion chambers, such as rocket engines, afterburners, and micro-combustors. In multi-injection nozzle combustors, multiple flames coexist within a confined space. The flames interact with each other, presenting complex flame characteristics, affecting the temperature and heat release rate distribution within the combustion chamber, thereby changing the combustion efficiency, pollutant emissions, and combustion stability of the combustion system. Therefore, analyzing and understanding the interaction structure between multiple flames is crucial for optimizing the combustion process.

[0003] Experimental research is an important means in the field of flame structure research. In experiments, high-speed cameras can capture information such as the shape, boundary, brightness distribution of the flame in real time, and accurately analyze the interaction structure of the flame. Especially in the case of multiple flames, the flames generated by different nozzles affect each other, which may lead to flame merging, squeezing, and even combustion instability. These phenomena can be directly reflected at the visual level, and then combined with advanced image processing and data analysis techniques, the key features of multiple flames can be extracted. This information is of great significance for understanding the interaction mechanism of multiple jet flames and predicting their impact on combustion performance. Existing analysis methods mainly focus on the study of single flames or simple jet flame structures, and are relatively lacking in comprehensive analysis of the interaction of multiple jet flames.

[0004] Therefore, there is an urgent need for a visual analysis method specifically for the interaction structure of multi-jet flames, which can more accurately identify and track multiple flame boundaries, analyze the interaction behavior of flames in real time, and provide a basis for the design and optimization of combustion systems. Summary of the Invention

[0005] To solve the above problems, the present invention provides a high-speed image analysis method for the interaction structure between multiple jet flames, which can capture the interaction structure between multiple jet flames from the flame image, and is particularly suitable for extracting the interaction structure between radially distributed multiple flames, such as afterburner flames.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A high-speed image analysis method for the interaction structure between multiple jet flames comprises the following steps:

[0008] S1. Image acquisition: outside the fuel injection port of the multi-injection port combustion chamber, a high-speed camera is used to capture a high-speed video of the multi-jet flame self-luminescence, and the high-speed video is processed by an image interface program to obtain a plurality of single-frame images, wherein the flame self-luminescence includes methyl self-luminescence and hydroxyl self-luminescence;

[0009] S2. Analysis area selection: selecting an analysis area for a single frame image, excluding the area upstream of the fuel injection port during the selection process;

[0010] S3. Initial division of the analysis region: Based on the spatial position of each fuel injection port, the analysis region is initially divided. The center lines of the two injection ports are used as references, and the upper and lower center lines and the upper and downstream boundaries of a particular injection port are used to form an initial rectangular analysis region.

[0011] S4, Gaussian filtering: applying Gaussian filtering to the initial rectangular analysis area to smooth the image and reduce image noise;

[0012] S5. Feature corner point detection: Perform feature corner point detection on the current frame image after Gaussian filtering to obtain a special corner point set that can represent the key feature points in the flame flow process;

[0013] S6, feature corner point supplement: repeat the operation of step S5 to process the next frame of image, obtain a feature corner point set, and compare it with the special corner point set of the previous frame image, identify the feature corner points that are far away from the feature corner points of the previous frame, and save these feature corner points to the corner point set of the current frame;

[0014] S7, optical flow field calculation: Match the feature corner points in two adjacent frames, track the displacement of each feature corner point in consecutive frames, and obtain the motion vector set of each feature point in the flame;

[0015] S8, flame region identification: Analyze the acquired motion vector set, determine and classify the flame structure according to the motion direction and speed, determine the flame region to which each characteristic corner point belongs, and make the characteristic corner points in the current field form flame point clouds of different flame sources;

[0016] S9, flame outer contour recognition: performing concave hull outer contour recognition on each flame point cloud to extract the outer contour shape of the flame;

[0017] S10. Extraction of interaction between adjacent flames: Perform bitwise AND operation on each flame point cloud to obtain the interaction area between adjacent flames, and finally obtain the interaction structure between multiple jet flames.

[0018] Furthermore, in step S5, the goodFeatureToTrack algorithm is used to perform feature corner detection on the current frame image after Gaussian filtering.

[0019] Furthermore, in step S7, the Lucas-Kanade pyramid iterative algorithm is used to match the feature corner points in two adjacent image frames.

[0020] Furthermore, in step S8, when determining the flame region to which each characteristic corner point belongs, if it is the initial determination, the determination is made according to the rectangular space where the characteristic corner point is located; if it is a subsequent determination, the determination is made according to the flame source to which the upstream characteristic corner point of the vector belongs.

[0021] Furthermore, in step S9, the Moreira-Santos algorithm based on K-means clustering is used to perform flame outline recognition.

[0022] The present invention also provides an application of the above-mentioned high-speed image analysis method for the interaction structure between multiple jet flames, and the high-speed image analysis method is used to extract and analyze the interaction structure between radially distributed multiple flames.

[0023] In some embodiments, the radially distributed multiple flames are afterburner flames.

[0024] In some embodiments, the radially distributed multiple flames are rocket engine flames.

[0025] In some embodiments, the radially distributed multiple flames are multi-injection port combustor flames.

[0026] The beneficial effects of the present invention are:

[0027] (1) The present invention discloses a high-speed image analysis method for the interaction structure between multiple jet flames. By processing high-speed video of a jet flame group, the method can dynamically capture the flow contours of each jet flame and the two-dimensional structure of the interaction interval in real time, revealing phenomena such as mutual interference and merging between flames, helping researchers to better understand the behavior of flames under different working conditions and optimize the control strategy during the combustion process.

[0028] (2) The present invention discloses a high-speed image analysis method for the interaction structure between multiple jet flames. Based on the Moreira-Santos algorithm of K-means clustering, the outer contour of the flame point cloud is identified, and the complex curved structure of the flame edge is effectively reproduced. Especially in the interaction area of ​​multiple jet flames, clear spatial distribution information of the interaction between flames is provided, which helps researchers to understand the influence of multiple flame interactions on combustion performance more intuitively.

[0029] (3) The present invention discloses a high-speed image analysis method for the interaction structure between multiple jet flames, which provides a technical tool for flame interaction research and has high recognition efficiency. It can be widely used in the flame structure analysis of complex combustion systems such as afterburners, rocket engines, and multi-jet combustion chambers.

[0030] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific embodiments or the description of the prior art. Obviously, some of the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 It is a schematic flow chart of the analysis method shown in the present invention;

[0033] Figure 2 1 is a schematic diagram of the combustion chamber structure shown in a preferred embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of a high-speed image of a multi-jet flame methyl self-luminescence according to a preferred embodiment of the present invention;

[0035] Figure 4 1 is a schematic diagram of analysis area selection according to a preferred embodiment of the present invention;

[0036] Figure 5 Schematic diagram of the initial segmentation of the analysis area shown in a preferred embodiment of the present invention;

[0037] Figure 6 1 is a schematic diagram of Gaussian filtering according to a preferred embodiment of the present invention;

[0038] Figure 7 It is a schematic diagram of the two-dimensional structure extraction of the interaction area between multiple flames shown in a preferred embodiment of the present invention.

[0039] Among them, the labels of the components are:

[0040] 1. Combustion chamber air inlet; 2. Combustion chamber fuel inlet; 3. Bluff body structure; 4. Combustion chamber outlet; 5. Motion vector of characteristic corner points; 6. Flame outline; 7. Flame interaction structure. DETAILED DESCRIPTION

[0041] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0042] Unless otherwise specified, the technical solutions described in the present invention are all conventional solutions in the field; the reagents or materials described are all from commercial channels unless otherwise specified.

[0043] Please refer to the present invention, which discloses a high-speed image analysis method for the interaction structure between multiple jet flames. This method uses digital image processing methods to process the flame methyl or hydroxyl self-luminous images to extract the flow contours of each jet flame and the two-dimensional structure of the interaction interval. The method includes the following steps:

[0044] S1. Image acquisition: This embodiment is based on the blunt body flame in the afterburner. Figure 2 This is a schematic diagram of the combustion chamber structure of an embodiment. The bluff body and jet nozzles are integrated. Four rows of jet nozzles are evenly distributed on both sides of the bluff body, with the bluff body's height axis as the centerline. The jet nozzle aperture is 1.5mm and the jet nozzle spacing is 8mm. The fuel and air from each row of jet nozzles mix and burn at the bluff body's trailing edge, with the four flames interacting with each other. In this embodiment, propane fuel is used. Multi-flame perspective video is captured using a high-speed camera combined with a methyl filter. Each frame of the image is obtained using an image interface program. The image resolution is 1280×360 and the sampling rate is 5000Hz. Figure 3 It is a schematic diagram of a multi-flame image.

[0045] S2. Analysis area selection: First, select the analysis area of ​​interest for the image. Manual selection of the appropriate area is allowed. All pixels outside the area of ​​interest will be cropped. It should be noted that the area upstream of the fuel injection port should be excluded as much as possible to reduce the impact of pixel changes in the upstream area on the analysis results. Figure 4 is a schematic diagram of the selected analysis area.

[0046] S3. Initial Segmentation of the Analysis Area: In this example, there are four rows of fuel injection nozzles, and four flames are represented in the image. Within the selected analysis area, a preliminary rectangular analysis region is constructed around each injection nozzle. Specifically, using the centerlines of the two injection nozzles as a reference, a rectangular region is segmented around the upper and lower centerlines and upstream and downstream boundaries of each injection nozzle to define each analysis flame. This separates the combustion areas of the four flames, ensuring that the initial outline of each flame can be accurately identified. This helps subsequent image processing algorithms better locate the flame boundaries and avoid blurring the outlines caused by interweaving flames.

[0047] S4. Gaussian filtering: Preprocess the image of the selected analysis area and smooth the image using the Gaussian filtering algorithm. Gaussian filtering can effectively reduce noise in the image, especially during high-speed video recording. Due to the limitations of shooting conditions, the flame image may be affected by noise. Gaussian filtering can smooth subtle changes in the flame edge and prevent noise interference from affecting subsequent analysis results. The Gaussian function formula is as follows:

[0048] ;

[0049] in, is the Gaussian function at position The weight of is the standard deviation, It is the coordinate offset with the center of the kernel as the reference point, indicating the distance from the center of the filter.

[0050] S5. Feature corner detection: Use the GoodFeatureToTrack algorithm to detect feature corners. This algorithm analyzes the grayscale value changes of the image and finds points with significant direction changes in the local area. Specifically, it mainly relies on the gradient matrix of the image. and its eigenvalues, and the corner points in the image are determined by the minimum eigenvalue, such as (2) and (3). This algorithm can effectively identify the corner features in the image, which can represent the key feature points in the flame flow process. In this step, the feature points in the current frame image are first detected, and a reasonable threshold is set to ensure that enough feature points can be detected. Specifically, the feature points should be distributed in the key area of ​​the flame, especially the edge area of ​​the flame, to ensure that the flow of the flame can be accurately identified in subsequent tracking.

[0051] ;

[0052] in, is the Gaussian window function, and The images are and Directional grayscale gradient.

[0053] ;

[0054] if If is large, it means that the gray value of the point has significant changes in both directions and is a corner point. If the value is smaller, the point may be an edge or a flat area.

[0055] S6. Feature Corner Point Supplementation: Similarly, feature corner point detection is performed on the flame image of the subsequent frame to obtain a feature corner point set. By comparing it with the feature corner point set of the flame image of the previous frame, feature corner points that are farther away from the previous frame's corner points are identified. These points mostly represent new flame structures transferred from upstream areas during the flame flow process. These new feature corner points are saved in the corner point set of the current frame.

[0056] S7. Optical flow calculation: Use the Lucas-Kanade pyramid iteration algorithm to match the feature corner points in two adjacent frames and track the displacement of each feature corner point in the consecutive frames. This algorithm is based on the optical flow equation (4) and can effectively track the motion trajectory of the feature points and obtain the motion vector set of each feature point in the flame. By tracking the feature corner points, the flow direction, speed, and position changes of the flame can be recorded.

[0057] ;

[0058] in, It is the grayscale change of the image in the time dimension, that is, the difference between two frames. and is the optical flow field to be estimated, i.e. the pixels in and Direction displacement.

[0059] S8. Flame Region Identification: Analyze the acquired motion vector set and determine and classify the flame structure based on motion direction and speed. The flame region to which each characteristic corner point belongs is determined. For initial identification, the determination is based on the rectangular space within which the point lies. For subsequent identification, the determination is based on the flame source to which the upstream point of the vector belongs. This way, the characteristic corner points in the current field form a point cloud representing different flame sources.

[0060] S9. Flame outline recognition: Due to the narrow and long structure of flames, the Moreira-Santos algorithm based on K-means clustering is used to identify the concave hull outline of each flame point cloud. K-means clustering divides data points into k clusters, and each data point is assigned to the cluster center closest to it, as shown in (5). The Moreira-Santos algorithm introduces additional constraints to ensure that the identified cluster center is more consistent with the actual boundary of the flame. During the flow of flames, the edges are usually irregular and exhibit complex deformations. This algorithm can effectively extract the outer contour shape of the flame and reproduce the flow trend of the flame.

[0061] ;

[0062] in, It is the objective function, which is used to measure the quality of the clustering results. is the number of clusters. It is clusters. It is data points. It is The center point of a cluster.

[0063] S10. Adjacent Flame Interaction Extraction: By performing a bitwise AND operation on each flame point cloud, we can obtain the interaction regions between adjacent flames. These regions are typically the areas of flame interference during the combustion process and can reflect the actual interaction between multiple jet flames during blunt body combustion.

[0064] This embodiment uses a digital image processing algorithm to effectively analyze the image of a blunt-body multi-flame methyl self-luminous flame with an afterburner as the background, and can effectively extract the two-dimensional structure of each flame interaction interval visually, which is conducive to further analysis and research on the mechanism of interaction between flames and its impact on combustion.

[0065] In summary, the present invention discloses a high-speed image analysis method for the interaction structure between multiple jet flames. By processing high-speed videos of jet flame groups, the flow contours of each jet flame and the two-dimensional structure of the interaction interval can be captured in real time and dynamically, revealing phenomena such as mutual interference and merging between flames, helping researchers to better understand the behavior of flames under different working conditions and optimize the control strategy in the combustion process; the method is based on the Moreira-Santos algorithm of K-means clustering, which identifies the outer contour of the flame point cloud and effectively reproduces the complex curved structure of the flame edge, especially in the interaction area of ​​multiple jet flames, providing clear spatial distribution information of the interaction between flames, helping researchers to more intuitively understand the influence of multiple flame interactions on combustion performance; the method provides a technical tool for flame interaction research, and has high recognition efficiency, and can be widely used in flame structure analysis of complex combustion systems such as afterburners, rocket engines and multi-injection nozzle combustion chambers.

[0066] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0067] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A high-speed image analysis method for the interaction structure between multiple jet flames, characterized in that: The following steps are involved: S1. Image acquisition: outside the fuel injection port of the multi-injection port combustion chamber, a high-speed camera is used to capture a high-speed video of the multi-jet flame self-luminescence, and the high-speed video is processed by an image interface program to obtain a plurality of single-frame images, wherein the flame self-luminescence includes methyl self-luminescence and hydroxyl self-luminescence; S2. Analysis area selection: selecting an analysis area for a single frame image, excluding the area upstream of the fuel injection port during the selection process; S3. Initial division of the analysis region: Based on the spatial position of each fuel injection port, the analysis region is initially divided. The center lines of the two injection ports are used as references, and the upper and lower center lines and the upper and downstream boundaries of a particular injection port are used to form an initial rectangular analysis region. S4, Gaussian filtering: applying Gaussian filtering to the initial rectangular analysis area to smooth the image and reduce image noise; S5. Feature corner point detection: Perform feature corner point detection on the current frame image after Gaussian filtering to obtain a special corner point set that can represent the key feature points in the flame flow process; S6, feature corner point supplement: repeat the operation of step S5 to process the next frame of image, obtain a feature corner point set, and compare it with the special corner point set of the previous frame image, identify the feature corner points that are far away from the feature corner points of the previous frame, and save these feature corner points to the corner point set of the current frame; S7, optical flow field calculation: Match the feature corner points in two adjacent frames, track the displacement of each feature corner point in consecutive frames, and obtain the motion vector set of each feature point in the flame; S8, flame region identification: Analyze the acquired motion vector set, determine and classify the flame structure according to the motion direction and speed, determine the flame region to which each characteristic corner point belongs, and make the characteristic corner points in the current field form flame point clouds of different flame sources; S9, flame outer contour recognition: performing concave hull outer contour recognition on each flame point cloud to extract the outer contour shape of the flame; S10. Extraction of interaction between adjacent flames: Perform bitwise AND operation on each flame point cloud to obtain the interaction area between adjacent flames, and finally obtain the interaction structure between multiple jet flames.

2. The high-speed image analysis method for the interaction structure between multiple jet flames according to claim 1, characterized in that: In step S5, the goodFeatureToTrack algorithm is used to detect feature corners of the current frame image after Gaussian filtering.

3. The high-speed image analysis method for the interaction structure between multiple jet flames according to claim 1, characterized in that: In step S7, the Lucas-Kanade pyramid iterative algorithm is used to match the feature corner points in two adjacent image frames.

4. The high-speed image analysis method for the interaction structure between multiple jet flames according to claim 1, characterized in that: In step S8, when determining the flame area to which each characteristic corner point belongs, if it is the first determination, the determination is made according to the rectangular space where the characteristic corner point is located; If it is a subsequent judgment, the judgment is made based on the flame source to which the upstream characteristic corner point of the vector belongs.

5. The high-speed image analysis method for the interaction structure between multiple jet flames according to claim 1, characterized in that: In step S9, the Moreira-Santos algorithm based on K-means clustering is used to recognize the outer contour of the flame.

6. An application of the high-speed image analysis method for the interaction structure between multiple jet flames according to any one of claims 1 to 5, characterized in that: The high-speed image analysis method is applied to extract and analyze the interaction structure between radially distributed multiple flames.

7. The use according to claim 6, characterized in that The radially distributed multiple flames are afterburner flames.

8. The use according to claim 6, characterized in that The radially distributed multiple flames are rocket engine flames.

9. The use according to claim 6, characterized in that The radially distributed multiple flames are multi-injection port combustion chamber flames.

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