A method for identifying a soot formation threshold in a premixed stable stagnation flame

Abstract

The application discloses a method for identifying a soot formation critical point in a premixed stable stagnation flame. The method is verified by taking ethylene premixed stable stagnation flame as an example, and the soot formation critical point is identified by using the method. The method collects a large number of flame pictures under each equivalent ratio by a non-invasive optical diagnosis system, and the images are processed by using python. The method provides an error index for judging the influence degree of the number of pictures on the result, quantifies the soot formation process, gives the definition of the soot critical point, and provides an innovative identification index. It is verified by experiments that the method can accurately capture a series of changes of the soot formation region of the premixed stable stagnation flame, and then identify the soot formation critical point.

Description

Technical Field

[0001] This invention belongs to the field of combustion technology, specifically relating to a method for identifying the critical point of soot formation in a premixed, stabilized, stalled flame. Background Technology

[0002] The soot produced by the incomplete combustion of hydrocarbon fuels not only harms the environment but also affects human health and normal daily life and production activities. Therefore, understanding and controlling the formation process of soot is particularly important for suppressing soot formation.

[0003] A premixed stabilized flame refers to a flame formed by premixing fuel and oxidizer in a certain proportion before the reaction, isolating the mixture from the outside air with a high flow rate of nitrogen, and placing a stabilizing plate above the burner nozzle in a position that allows for full contact with the flame. Compared to a premixed flame, this flame has stronger flame rigidity, better stability, and a well-defined physical boundary, making it ideal for studying the process of soot formation, especially how soot transforms from the gaseous phase to the solid phase.

[0004] As the equivalence ratio increases, the flame transitions from oxygen-rich combustion to fuel-rich combustion. The premixed stabilized stagnation flame gradually changes from a soot-free blue flame to a high-concentration soot orange flame. Correspondingly, this change in flame color represents the transformation of the chemiluminescence component in the combustion zone from free radical luminescence to luminescence coupled with the radiation from free radicals and soot. The condition at which the orange flame first appears is considered the macroscopic soot formation critical point.

[0005] Previous methods for determining the critical point of soot formation were mostly based on visual methods, which were subject to significant subjective interference and could lead to biased results. Summary of the Invention

[0006] The purpose of this invention is to provide a method for accurately identifying the critical point of soot formation in a premixed, stabilized flame using a non-invasive optical diagnostic system. This method effectively reduces the interference of noise in the image on the results, quantifies the soot formation process, and defines the critical point of soot formation. This method can accurately capture the critical point of soot formation, which is of great significance for studying the soot formation process.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for identifying the soot formation critical point in a premixed, stabilized, stalled flame, the method comprising:

[0009] Step 1: Use a camera to capture continuously changing flame images under different flame conditions, 100 images for each condition. Extract the flame region from 25, 50, 75, and 100 flame images for each condition and calculate the average image.

[0010] Step 2: Count the number of points in the average image with different image processing volumes for each working condition where the red signal value is greater than the blue signal value, and represent these points on a black background image according to the difference value to form a difference image;

[0011] Step 3: Plot a scatter plot with the equivalence ratio of each working condition as the x-axis and the number of pixels with red signal values ​​greater than blue signal values ​​in the average image of different image processing volumes for each working condition as the y-axis.

[0012] Step 4: Perform error statistics on the ordinate of the mean images with different image processing volumes in Step 3 under the same working condition, and select the mean image when the error is less than 5%.

[0013] Step 6: Perform linear interpolation on the scatter plot of the mean image selected in Step 4 and calculate the second derivative. Define the condition where LPN is not 0 but its second derivative is 0 for the first time as the critical point for soot generation.

[0014] Preferably, the camera in step 1 is a high-definition digital SLR camera or a CCD camera.

[0015] Prior to this, the shooting parameters in step 1 were all set to fixed values ​​during the experiment.

[0016] Prior to this, the camera in step 1 is in a fixed position during the experiment, and there are no other external light sources or other airflow disturbances around it.

[0017] Prior to this, the different flame conditions in step 1 are achieved by changing only a single parameter, while keeping other conditions constant, for example, by increasing only the equivalence ratio.

[0018] Prioritize changing the flame conditions in step 1, wait 2 minutes for the gas in the pipeline to stabilize, and then take pictures.

[0019] Prior to this, in step 1, the intercepted flame area must be above the burner nozzle, below the stagnation plate, and include all parts of the flame.

[0020] Prior to this, in step 1, the method for calculating the image mean can be defined using the following formula:

[0021]

[0022] Where B represents the blue signal value, G represents the green signal value, and R represents the red signal value. B, G, and R represent the blue, green, and red signal values ​​of the mean image, respectively. K is the number of images used to calculate the mean, and i is the image number.

[0023] Prior to this, in step 2, the number of pixels with red signal values ​​greater than blue signal values ​​is defined as:

[0024]

[0025] Where N and M represent the number of pixels in the horizontal and vertical directions of the mean image, respectively. i and j represent the x and y coordinates of the pixels in the mean image, respectively.

[0026] Prior to step 2, the black background image and the mean image are the same size, and the positions of all displayed pixels are the same as the positions of pixels in the mean image.

[0027] Preferably, in step 4, the error index is defined as:

[0028]

[0029] Where m represents the number of images used to calculate the average image for a given working condition, and φ i This indicates different operating conditions.

[0030] Prior to step 5, the flame characteristics and patterns before and after the soot formation critical point are as follows: When no soot is generated, the flame contains only blue chemiluminescent areas. In the captured flame area images, the red signal value of all points is less than the blue signal value, which is represented as no luminescent points in the difference image. Near the soot formation critical point, the flame contains both blue and weak orange luminescent areas. In the captured flame images, points with red signal values ​​greater than blue signal values ​​gradually appear, and their number increases with the equivalence ratio. In the difference image, blue luminescent points gradually appear and begin to gradually transform into red luminescent points. When significant soot generation occurs, both blue and orange luminescent areas exist simultaneously in the flame. In the captured flame area, points with red signal values ​​greater than or even significantly greater than blue signal values ​​appear, which is represented as numerous luminescent points, mostly red, in the difference image.

[0031] Prioritized, in step 5, the critical point for soot generation is defined as the inflection point from slow LPN growth to rapid LPN growth. Based on the error index, the image with the larger number of processed images (with an error index less than 5%) is used to determine the soot critical point. The critical point for soot generation is defined as the first occurrence of a non-zero LPN but a zero second derivative.

[0032] Compared with the prior art, the present invention has the following advantages:

[0033] (1) The method of the present invention quantifies the generation of soot based on the R and B values ​​in the image, and judges the soot critical point of the premixed stable stagnant flame by numerical analysis, thus eliminating the bias caused by subjective judgment.

[0034] (2) The method of the present invention points out that the average image of multiple images under the same working condition can effectively reduce the influence of noise, and provides an index on the influence of the number of images on the result, which has guiding significance for balancing the amount of calculation and accuracy.

[0035] The present invention will now be further described with reference to the accompanying drawings. Attached Figure Description

[0036] Figure 1 Images show the mean values ​​and R / B difference values ​​of premixed stable stagnation flames at different equivalence ratios, where subscript 1 represents the mean value image and subscript 2 represents the R / B difference image.

[0037] Figure 2 (a) shows the LPN values ​​under different equivalence ratios, and (b) shows the effect of different number of image processing images on the results under different equivalence ratios.

[0038] Figure 3 A method for determining the critical point of carbon soot formation in a premixed, stable, stagnant flame. Detailed Implementation

[0039] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0040] The present invention will be further described using ethylene premixed stabilized flame as an example.

[0041] A method for identifying the critical point of soot formation in a premixed, stabilized, stalled flame comprises the following steps:

[0042] Step 1: In a dark environment, use a high-definition digital SLR camera with fixed shooting parameters at a fixed position to take pictures of flames at different equivalent ratios. Take 100 pictures at a fixed frequency within a short period of time for each equivalent ratio.

[0043] Step 2: Using Python, process all images captured under each working condition. For images under the same working condition, calculate the mean image and compare the R and B values ​​of all pixels in the mean image to obtain an R / B difference cloud map. The result is as follows: Figure 1 As shown;

[0044] Step 3: Plot a scatter plot with the equivalence ratio of each working condition as the x-axis and the number of pixels with red signal values ​​greater than blue signal values ​​in the average number of images for each working condition as the y-axis. The result is as follows: Figure 2 As shown in (a);

[0045] Step 4: Perform error statistics on the ordinate of the mean images for the same working condition with different numbers of images from Step 3. Compare the three image processing quantities (25:50, 50:75, 75:100) to illustrate the impact of the number of images processed on the results. Define that when the error index is less than 5%, the number of images has no impact on the results. The results are as follows... Figure 2 As shown in (b);

[0046] Step 5: Based on the results of Step 4, select the image with the larger number of processed images from the two images with an error index of less than 5% as the basis. The mean image obtained from this is used to determine the critical point of carbon soot generation in a uniformly mixed, stable, and stagnant flame.

[0047] Step 6: Perform linear interpolation on the LPN scatter plot of the average of 100 processed images and calculate the second derivative. Define the condition where the second derivative is 0 for the first time (LPN is not 0) as the critical point for soot generation. The results are as follows: Figure 3 As shown;

[0048] This invention discloses a method for identifying the critical point of soot formation in premixed, stabilized, and stalled flames. The method uses a non-invasive optical diagnostic system to acquire multiple flame images at different equivalence ratios. For each equivalence ratio, the average image is calculated, and a calculation region is defined. Based on an error index of less than 5%, the critical point of soot formation is determined. This invention quantifies the soot formation trend, accurately captures the process of soot formation from nothing to something, and identifies the critical point of soot formation through an innovative method.

[0049] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying the critical point of soot formation in a stable, stalled flame, characterized in that: Step 1: Use a camera to capture continuously changing flame images under different flame conditions, N images for each condition, and extract the flame region from these N images and calculate the average image. Step 2: Count the number of points in the average image of different image processing volumes for each working condition where the red signal value is greater than the blue signal value, and represent these points on the black background image according to the difference to form a difference image; Step 3: Plot a scatter plot with the equivalence ratio of each working condition as the x-axis and the number of pixels with red signal values ​​greater than blue signal values ​​in the average image of different image processing volumes for each working condition as the y-axis; in Step 3, the number of pixels with red signal values ​​greater than blue signal values ​​is defined as: ； Where N and M represent the number of pixels in the horizontal direction and the number of pixels in the vertical direction of the mean image, respectively; i and j represent the x-coordinate and y-coordinate of the pixels in the mean image, respectively. Step 4: Perform error statistics on the ordinate of the mean images with different image processing volumes in Step 3 under the same working condition, and select the mean image when the error is less than 5%. Step 5: Perform linear interpolation on the scatter plot of the mean image selected in Step 4 and calculate the second derivative. The first occurrence of a condition where LPN is not 0 but its second derivative is 0 is the critical point for soot generation.

2. The method for identifying the critical point of soot formation in a premixed, stabilized, stalled flame according to claim 1, characterized in that, The camera used in step 1 is a high-definition digital SLR camera or a CCD camera.

3. The method for identifying the critical point of soot formation in a premixed, stabilized, stalled flame according to claim 1, characterized in that, In step 1, the camera was in a fixed position during the experiment, and there were no other external light sources around it. The shooting parameters were all set to fixed values ​​during the experiment.

4. The method for identifying the critical point of soot formation in a premixed, stabilized, stalled flame according to claim 1, characterized in that, In step 1, a high-definition digital SLR camera with fixed shooting parameters is used at a fixed position in a dark environment to take pictures of flames at different equivalent ratios. For each equivalent ratio, N pictures are taken at a fixed frequency within a short period of time. In step 1, only the equivalent ratio is changed, while other conditions remain the same.

5. The method for identifying the critical point of soot formation in a premixed, stabilized, stalled flame according to claim 1, characterized in that, The value of N is greater than 50. The larger the value of N, the better the result.

6. The method for identifying the critical point of soot formation in a premixed, stabilized, stalled flame according to claim 1, characterized in that, In step 2, the captured flame area must be higher than the burner nozzle, lower than the stagnation plate, and include all parts of the flame; the formula for calculating the average value of the image is: ； Where B represents the blue signal value, G represents the green signal value, and R represents the red signal value; These represent the blue, green, and red signal values ​​of the mean image, respectively; K is the number of images used to calculate the mean, and i is the image number.

7. The method for identifying the soot formation critical point in a premixed, stabilized, stalled flame according to claim 1, characterized in that, In step 2, the black background image and the mean image are the same size, and the positions of all displayed pixels are the same as those of the pixels in the mean image.

8. The method for identifying the soot formation critical point in a premixed, stabilized, stalled flame according to claim 1, characterized in that, In step 4, the error index is defined as: ； Where m represents the number of images used to calculate the average image for a given working condition. Different operating conditions are indicated by "step"; step indicates the step size used when calculating errors.

9. The method for identifying the critical point of soot formation in a premixed, stabilized, stalled flame according to claim 1, characterized in that, In step 5, the critical point of soot generation is defined as the turning point from slow growth of LPN to rapid growth of LPN. Based on the error index, the result of processing more images than the two types of images with an error index of less than 5% is used to determine the critical point of soot generation. The condition where the second derivative is 0 for the first time and LPN is not 0 is identified as the critical point of soot generation.

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