CN114897897A - Evaluation method of horizontal jet type flame stability - Google Patents
Evaluation method of horizontal jet type flame stability Download PDFInfo
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Abstract
The invention provides a simple and reliable evaluation method for flame stability of a horizontal jet flow type, which is based on the fluctuation characteristics of flame, utilizes a probability statistical method and carries out probability processing on a real-time flame continuous video image to establish a comprehensive evaluation system based on a horizontal dimensionless fluctuation parameter delta and a vertical dimensionless fluctuation parameter gamma, and can evaluate the flame stability and the change trend thereof in real time and accurately. The method can be used for judging the stability of the horizontal jet combustion in a laboratory, and can also guide the parameter adjustment of various horizontal industrial furnaces and heating devices in the combustion process, thereby reducing the fuel consumption rate, increasing the efficiency and saving the energy by stabilizing the flame.
Description
Technical Field
The invention relates to the field of flame stability evaluation, in particular to a method for evaluating horizontal jet type flame stability.
Background
Flame stability means that the flame can be held in a certain position and volume under the specified combustion conditions, and neither flashback nor extinguishes. Since unstable combustion may cause a decrease in the thermal efficiency of the combustion apparatus, and may cause a backfire, a misfire, and the like, causing various safety accidents and economic losses, monitoring and evaluation of combustion stability is important for the combustion apparatus. In particular to horizontal jet type flame, which is widely applied to a plurality of production and living scenes such as aircraft engines, small cooking ranges, heating furnaces, steam boilers, combustors and the like.
The temperature, the reaction speed, the interaction between the oxidant and the reactant of the horizontal jet type flame and the like have strong correlation with the change of the flame form, and macroscopically reflect the fluctuation characteristics of the flame. In recent years, methods for evaluating the stability of flame images mostly focus on distinguishing a large number of flame gray level images, and a fuzzy theory, a rough set theory, a level set theory, a neural network theory and the like are used for reference. For example, a neural network is adopted to detect flame combustion stability, correlation of each characteristic value is fully considered based on a large amount of operation data, a classification method of a support vector machine is adopted to classify and predict flame signals, or a concept of transfer learning is added, and various characteristics are extracted by a deep learning algorithm. However, these methods have large calculation amount, slow calculation speed and harsh general application conditions, are not suitable for the real-time stability evaluation of flame, and are difficult to be popularized to wider fields.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for evaluating the flame stability of a horizontal jet flow type, which comprises the following steps:
correspondingly converting the obtained multiple continuous flame images into multiple binary images;
acquiring a flame probability cloud picture according to the flame occurrence probability P (x, y) of the same pixel position in the plurality of binary images;
calculating a horizontal dimensionless fluctuation parameter delta and a vertical dimensionless fluctuation parameter gamma according to the flame probability cloud chart, wherein,
， 
，L f is the horizontal projection length of the flame fluctuation region, L is the horizontal projection length from the flame starting point to the flame end, H f The vertical projection length of a flame fluctuation area is H, the vertical projection length from a flame starting point to a flame tail end is H, the flame fluctuation area is an area, P (x, y) is more than or equal to 0.1 and less than or equal to 0.9, and the flame tail end is an intersection point of a flame trace and a probability contour line, wherein P (x, y) is 0.5 in the flame fluctuation area;
and judging the flame stability according to the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma.
Preferably, the flame stabilization is judged according to the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma
The method comprises the following steps:
judging the horizontal stability of the flame according to the horizontal dimensionless fluctuation parameter delta and a formula (1),
judging the vertical stability of the flame according to the vertical dimensionless fluctuation parameter gamma and a formula (2),
if the horizontal stability of the flame and the vertical stability of the flame are both in a stable region, the flame has stability;
the flame has instability if at least one of a horizontal stability of the flame and a vertical stability of the flame is in an unstable range.
Preferably, the judging the flame stability according to the horizontal dimensionless fluctuation parameter δ and the vertical dimensionless fluctuation parameter γ includes:
judging the horizontal stability of the flame according to the horizontal dimensionless fluctuation parameter delta and a formula (3),
judging the vertical stability of the flame according to the vertical dimensionless fluctuation parameter gamma and a formula (4),
if the horizontal stability of the flame and the vertical stability of the flame are both in a stable region, the flame has stability;
the flame tends to be unstable if at least one of the horizontal stability of the flame and the vertical stability of the flame is in a tendency instability interval;
a flame has extreme instability if at least one of the horizontal stability of the flame and the vertical stability of the flame is in an extremely unstable region.
Preferably, the converting the acquired multiple continuous flame images into binary images respectively includes:
acquiring the optimal number n of continuous flame images required to be extracted for judging the stability of the horizontal jet type flame;
extracting n continuous flame images from an input video file;
carrying out gray processing on the flame image to obtain a gray value of each pixel in the flame image;
obtaining an optimal threshold value T according to the gray value, and obtaining each flame image according to a formula (5)Assignment of pixel locationsf(x, y) converting the gray value into binary value to obtain binary image of the flame image,
preferably, the acquisition of the optimal number n of successive flame images to be extracted for determining the flame stability of the horizontal jet type includes
The method comprises the following steps:
counting characteristic parameters of horizontal jet type flames of a preset number of flame images, wherein the characteristic parameters comprise L, H, L f 、H f 、k、k f δ and γ, said k being the flame lift slope, k f Raising the slope for the fluctuation region;
through L, H, L f 、H f 、k、k f Delta, and gamma, and a determination L, H, L of the statistical invariance curve of the flame f 、H f 、k、k f Statistical independence of, delta and gamma, L, H, L f 、H f 、k、k f The amounts of independence corresponding to δ and γ are shown in equation (6),
a j ≤n i ≤b j （6），
wherein, a j And b j Is a constant number, n i Are respectively L, H, L f 、H f 、k、k f The number of the continuous flame images corresponding to δ and γ, j being a natural number from 1 to 8;
obtaining the optimal number n of continuous flame images required to be extracted for judging the stability of the horizontal jet type flame according to the formula (7),
max{a j }≤n≤min{b j } （7）。
preferably, n is more than 400 and less than or equal to 500.
Preferably, the obtaining of the flame probability cloud picture according to the flame occurrence probability P (x, y) of the same pixel position in the plurality of binary images includes:
according to the formula
Calculating the probability of flame occurrence, wherein P (x, y) is the position of the pixel point (x, y)
I is the number of the flame images, and n is the number of the collected continuous flame images;
and drawing a flame probability cloud picture according to the corresponding relation between the flame occurrence probability and the gray level of the preset pixel points (x, y).
Preferably, the obtaining the optimal threshold T according to the gray-level value includes:
counting the accumulated frequency of each gray value in the continuous flame images;
and setting the gray value corresponding to the minimum value in the valley bottom area between the two peaks in the accumulated frequency as an optimal threshold value T.
The beneficial effect of this application is as follows:
the invention provides a simple and reliable evaluation method for flame stability of a horizontal jet flow type, which is based on the fluctuation characteristics of flame, utilizes a probability statistical method and carries out probability processing on a real-time flame continuous video image to establish a comprehensive evaluation system based on a horizontal dimensionless fluctuation parameter delta and a vertical dimensionless fluctuation parameter gamma, and can evaluate the flame stability and the change trend thereof in real time and accurately. The method can be used for judging the stability of the horizontal jet combustion in a laboratory, and can also guide the parameter adjustment of various horizontal industrial furnaces and heating devices in the combustion process, thereby reducing the fuel consumption rate, increasing the efficiency and saving the energy by stabilizing the flame.
Drawings
For a clearer explanation of the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for evaluating flame stability of a horizontal jet type according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a probability cloud of flames according to an embodiment of the present invention;
FIG. 3 is an enlarged schematic view of a probability cloud of flames according to an embodiment of the present invention;
FIG. 4 shows a series of 6 flame images collected under the experimental condition 1;
FIG. 5 shows a series of 6 flame images collected under test condition 6;
FIG. 6 is a series of 6 flame images collected under the experimental condition P4;
FIG. 7 is a schematic diagram of an embodiment of the present inventionLA statistical invariance curve of (a);
FIG. 8 is a schematic diagram of an embodiment of the present inventionHA statistical invariance curve of (a);
FIG. 9 is a schematic diagram of an embodiment of the present inventionL f A statistical invariance curve of (a);
FIG. 10 is a schematic diagram of an embodiment of the present inventionH f A statistical invariance curve of (a);
FIG. 11 is a graph illustrating the overall flame rise slope according to an embodiment of the present inventionkAnd flame fluctuation zone lift slopek f A statistical invariance curve of (a);
FIG. 12 is a horizontal dimensionless fluctuation parameter provided by an embodiment of the present inventionδAnd vertical dimensionless fluctuation parameterγStatistical invariance curve of (a).
Fig. 13 is a schematic diagram of an optimal threshold T according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
Aiming at the defects in the prior art, the scheme provides an evaluation method of horizontal jet type flame stability. Referring to fig. 1, a flow chart of a method for evaluating flame stability of a horizontal jet type according to an embodiment of the present invention is shown. As can be seen from fig. 1, the method comprises the following steps:
step S10: and correspondingly converting the obtained multiple continuous flame images into multiple binary images. In this embodiment, a conventional 25-frame/sec CCD or CMOS camera may be used as the flame video capture device, and the captured continuous flame video may be extracted from the continuous flame image by using mathematical calculation software such as MATLAB. The extracted continuous flame image can be subjected to gray processing and binary processing to obtain a binary image, the binary image only has black and white visual effects, and each pixel point correspondingly stores two kinds of assignments of 0 or 1.
Step S20: and acquiring a flame probability cloud picture according to the flame occurrence probability P (x, y) of the same pixel position in the plurality of binary images.
In this embodiment, the pixel position with the white visual effect and assigned as 1 may be defined as having flame, and conversely, the pixel position with the black visual effect and assigned as 0 may be defined as having no flame, so that the probability of flame occurring at the same pixel position in a plurality of binary images may be counted. Referring to fig. 2 and fig. 3, a schematic diagram of a flame probability cloud chart according to an embodiment of the present invention and an enlarged schematic diagram of a flame probability cloud chart according to an embodiment of the present invention are shown. As can be seen from fig. 2 and 3, the flame probability cloud can be used as a visual representation of the distribution of the flame occurrence probability, and the pixel positions having the same flame occurrence probability have the same gray scale or chromaticity.
Step S30: calculating a horizontal dimensionless fluctuation parameter delta and a vertical dimensionless fluctuation parameter gamma according to the flame probability cloud chart, wherein,
， 
，L f is the horizontal projection length of the flame fluctuation region, L is the horizontal projection length from the flame starting point to the flame end, H f Is the length of the vertical projection of the flame fluctuation region, H is the flameThe vertical projection length from the starting point to the flame end, the flame fluctuation area is an area which is more than or equal to 0.1 and less than or equal to P (x, y) and less than or equal to 0.9, the flame end is the intersection point of a flame trace and a probability contour line with the P (x, y) of 0.5 in the flame fluctuation area, the flame starting point is usually the central position of a nozzle of the flame generating device, in order to determine the flame trace from the flame starting point to the flame end, a vertical normal line is firstly drawn at different positions of four or more than 50 percent occurrence probability contour lines at the bottom of the flame until the probability contour line with the probability of 50 percent of the other side of the flame, and then the central points of the line segments are connected to form the flame trace, wherein the schematic diagram is shown in FIG. 2. The flame fluctuation of the region with the flame occurrence probability lower than 0.1 is large, so that if the region is brought into a flame fluctuation region, the flame fluctuation interval is obviously overlarge, and the flame stability judgment accuracy is easily reduced; the region with the flame occurrence probability higher than 0.9 has a small flame fluctuation, and therefore, if the region is included in the flame fluctuation region, the flame fluctuation region is significantly reduced, which is also not favorable for the determination of flame stability.
Step S40: and judging the flame stability according to the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma. Flame stability means that the flame can be held in a certain position and volume under the specified combustion conditions, and neither flashback nor extinguishes. The flame temperature, reaction speed, propagation speed, interaction between the oxidant and the reactant, etc. all have important influence on the flame form, and the flame form is one of the important manifestations of flame stability, and when the flame form is changed violently, the stability trend of the flame will be changed accordingly. According to the method, the integral form of the flame can be represented through the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma, when the form of the flame changes, the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma also change, and the association between the form of the flame and the stability of the flame is established through the quantized horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma, so that the stability of the flame is quantized.
In general, when the flame is in a steady state, the parameter δ of the horizontal dimensionless fluctuation and the parameter δ of the vertical dimensionless fluctuationThe parameter gamma is also in the respective stable region, when the flame stability is changed, the flame shape is changed, correspondingly, the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma are also separated from the respective stable region and enter the unstable region. The stability of the flame can be judged according to the range of the interval where the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma are located. For example, flame propagation velocity is greater than the jet velocity, which results in a dynamic balance between flame propagation velocity and jet velocity. At this time, the level of horizontal fluctuation of the flame front surface is reduced, L f Amplification ratio (L-L) f And/2) obviously reduced, namely the horizontal dimensionless fluctuation parameter delta is obviously reduced, and the flame deviates from a stable interval, thereby indicating that the flame is converted from a stable state to a state tending to be unstable. The variation trends of the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma are basically consistent, and when the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma are too large or too small, the flame tends to be unstable.
The air flow and the fuel flow of the horizontal jet type flame are both in the horizontal direction, the change of the mobility trend in the horizontal direction firstly reflects on the fluctuation area at the tail end of the flame, and the vertical projection height of the flame fluctuation area is obviously changed firstly due to the influence of air buoyancy, so that the change is reflected on a vertical dimensionless fluctuation parameter gamma. Since the macroscopic features of the horizontal jet type flame stabilization are firstly reflected on the vertical fluctuation of the flame end, the sensitivity of the vertical dimensionless fluctuation parameter gamma is higher than that of the horizontal dimensionless fluctuation parameter delta, but the error caused by the flame probability statistics is larger due to the large fluctuation. Parameter L related to parameter delta due to level dimensionless fluctuation f Both L and L are aligned with the velocity direction of the fuel and air flow, and therefore, the measurement is more accurate, but the sensitivity is relatively weak. Therefore, the scheme has higher accuracy and reliability in determining the stability of the flame through the combination of the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma.
The invention provides a simple and reliable evaluation method for flame stability of a horizontal jet flow type, which is based on the fluctuation characteristics of flame, utilizes a probability statistical method and carries out probability processing on a real-time flame continuous video image to establish a comprehensive evaluation system based on a horizontal dimensionless fluctuation parameter delta and a vertical dimensionless fluctuation parameter gamma, and can evaluate the flame stability and the change trend thereof in real time and accurately. The method can be used for judging the stability of the horizontal jet combustion in a laboratory, and can also guide the parameter adjustment of various horizontal industrial furnaces and heating devices in the combustion process, thereby reducing the fuel consumption rate, increasing the efficiency and saving the energy by stabilizing the flame.
In a preferred embodiment of the present application, step S40 further includes the following steps:
step S411: judging the horizontal stability of the flame according to the horizontal dimensionless fluctuation parameter delta and a formula (1),
step S412: judging the vertical stability of the flame according to the vertical dimensionless fluctuation parameter gamma and a formula (2),
step S413: if the horizontal stability of the flame and the vertical stability of the flame are both in a stable region, the flame has stability;
step S414: the flame has instability if at least one of a horizontal stability of the flame and a vertical stability of the flame is in an unstable range.
Among the applications of horizontal jet type flames, diesel fuel is most widely used. In the embodiment, diesel oil is used as fuel, a stable interval and an unstable interval corresponding to a horizontal dimensionless fluctuation parameter delta and a vertical dimensionless fluctuation parameter gamma are preset, and the stable states of flame in the horizontal direction and the vertical direction can be judged through the corresponding intervals shown in the formula (1) and the formula (2). When both are in the stable region, the flame can be judged to be in a stable state at present; when one of the two is in an unstable state, the flame is judged to be in an unstable state by indicating that the flame has an unstable factor in the horizontal direction or the vertical direction. When the flame is detected to be in an unstable state, an early warning can be sent out, so that relevant personnel can ensure the continuous and stable output of the flame by adjusting relevant working condition parameters.
Table 1: the stability test result of the horizontal jet flame using diesel oil as fuel is as follows:
as can be seen from Table 1, the stability results obtained by the evaluation method are consistent with the actual stability results under the conditions of different environmental pressures, air volumes, air volume flow rates and equivalence ratios. Referring to table 1, fig. 4 and fig. 5, 6 continuous flame images collected under the experimental condition 1 and 6 continuous flame images collected under the experimental condition 6 are shown, respectively. As can be seen from fig. 4 and 5, the flame under the experimental condition 1 is in a stable state, and the flame under the experimental condition 6 is in an unstable state, which is consistent with the result determined by the present evaluation method. Only 6 consecutive images of each of the experimental condition 1 and the experimental condition 6 are taken as an example, and the rest conditions are not shown in an excessive way. In addition, the method can be popularized to any fuel and horizontal jet flame of a combustor by changing the preset judgment intervals of the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma, and is not limited to the application range of taking diesel as fuel.
In order to further improve the accuracy of stability judgment, in other embodiments of the present application, the judgment intervals of the horizontal dimensionless fluctuation parameter δ and the vertical dimensionless fluctuation parameter γ may be further refined, so as to pre-judge the stability trend of the flame, and reserve more sufficient time for controlling the stability of the flame. Specifically, step S40 further includes the following steps:
step S421: judging the horizontal stability of the flame according to the horizontal dimensionless fluctuation parameter delta and a formula (3),
step S422: judging the vertical stability of the flame according to the vertical dimensionless fluctuation parameter gamma and a formula (4),
step S423: if the horizontal stability of the flame and the vertical stability of the flame are both in a stable region, the flame has stability;
step S424: the flame tends to be unstable if at least one of the horizontal stability of the flame and the vertical stability of the flame is in a tendency instability interval;
step S425: a flame has extreme instability if at least one of the horizontal stability of the flame and the vertical stability of the flame is in an extremely unstable region.
Table 2: experimental results for different operating parameters at 0.05MPa atmospheric pressure:
in this embodiment, the unstable state is an initial stage of an unstable state and is still an unstable state in nature, the extremely unstable state is a final stage of an unstable state, and the extremely unstable state in which the flame is sustained may cause a risk of blow-out. As can be seen from Table 2, the stability results obtained by the evaluation method are consistent with the actual stability results under different working conditions. Referring to table 2 and fig. 6, 6 consecutive flame images collected under the experimental condition P4 are shown. As can be seen from FIG. 6, the flame under the experimental condition P4 is in an extremely unstable state, which is consistent with the theoretical judgment result of the method. Only 6 consecutive images of the experimental condition P4 are taken as an example here, and the rest of the conditions are not shown here too much.
In this embodiment, step S10 further includes the following steps:
step S101: acquiring the optimal number n of continuous flame images required to be extracted for judging the stability of the horizontal jet type flame;
step S102: extracting n continuous flame images from an input video file;
step S103: carrying out gray processing on the flame image to obtain a gray value of each pixel in the flame image;
step S104: obtaining an optimal threshold value T according to the gray value, and assigning a value to each pixel position in the flame image according to a formula (5)f(x, y) converting the gray value into binary value to obtain binary image of the flame image,
in the invention, the statistical invariance research is carried out on the minimum frame number of 100-1000 involved in the probability processing method of the continuous images. Whether the flame images of 100 continuous frames or up to 1000 continuous frames are subjected to statistical processing, the overall flame appearance can be well characterized. But the flame edge results are not good for fewer consecutive image frame numbers relative to the statistical effect for a larger number of frames. Because the position of 50% defined by the method is the tail end of the flame, a smoother and uniform profile cannot be obtained under the condition of less continuous image frame number, and the error is slightly larger. Particularly, in the area close to the flame end, because the influence of buoyancy is large, the fluctuation degree of the flame is larger than that of a continuous flame area, and the fluctuation amplitude of the flame cannot be completely presented by probability statistics of flame continuous images with the number of less than 300 frames.
When the number of the flame images is less than 300 frames (particularly 100 frames), the flame fluctuation area is obviously in a zigzag shape, the zigzag shape of the flame fluctuation area gradually becomes gentle as the number of the statistical frames increases to 300 frames, and the statistical property is clear in an error range. First, we address two of the most important parameters in the global basic shape of flames: l and H were subjected to statistical invariant analysis, as shown in particular in fig. 7 and 8. When the statistic is less than 200 frames, H and L are both small due to the insufficient number of the statistical flame images. After more than 600 frames, we found a significant drop in the value of L with a large increase in the amount of error, at a statistical number of 800 to 1000 frames. At the same time, the value of H also rises with a large error increment. As a rule, the flame should remain stable at all times, as should the independence curve of the flame statistics. However, the burning test of the plateau environment is usually performed in an artificial low-pressure chamber, and the plateau burning test in the application also utilizes a low-pressure chamber to replace the pressure of the plateau environment. Although the air pressure in the low-pressure chamber is maintained at +/-100 Pa of the set pressure by the suction action of a series of vacuum pumps, the flame lifting height is obviously increased after the statistics of 800 frames because the amount of combustion smoke inside the low-pressure chamber for a long time still affects the gas in the chamber due to the sealing property of the box body (although a smoke exhaust port is arranged). Therefore, we can divide the curve of the whole flame invariance statistics into three parts according to the variation trends of L and H: (1) insufficient statistical regions (the number of statistical images is less than or equal to 200); (2) a reasonable statistical region (n is more than or equal to 300 and less than or equal to 600); (3) an interference statistical region (n is more than 600 and less than or equal to 1000).
After the fluctuation region is enlarged, it can be more obviously seen that the jaggy is very obvious on the outline of each probability when the statistic is small, and besides the uncertainty of measurement is increased, the jaggy is caused by too few statistical samples, and both L and H are high. With particular reference to FIGS. 9 and 10, L is different than L f The calculated value of (c) enters a reasonable statistical region after the statistics of frame 400. This is because for the horizontal spray jet type flame, the buoyancy lifting effect at the end of the flame leads to a more obvious fluctuation amplitude than that of the continuous flame area, and finally leads to a smaller sampling amount and cannot completely reflect the horizontal fluctuation condition. For H f The reason for the interference statistic phenomenon after the statistic of 800 frames is the same as that of H, and is also due to the regulation principle of the low-pressure chamber and the overflow of high-temperature flue gasThe effects are not described in detail here.
For a horizontal jet flame, we usually need to characterize its degree of upward bending, and k is the flame lift slope, i.e. the slope of the straight line between the flame start (the central position of the flame nozzle) and the flame end, which can be used to represent the lift characteristics of the flame, is an important parameter for characterizing the degree of upward bending of the flame. But specifically for the slope k of the fluctuation region f Can be used for further characterizing the upward bending characteristic of the flame end, the slope k f Is the slope of a straight line from a position on the flame trace where P (x, y) is 90% to a position where P (x, y) is 10%. Their statistical invariance specific trends are shown in fig. 11. First, for the overall trend, k is gradually reduced to be gentle in the region, and k is gradually reduced f Gradually increases and then becomes gentle, and finally, the total body still accords with the dividing principle of the three statistical regions from the interference statistical region. However, it should be noted that k and k are the same f Are respectively connected with L, H, L f And H f Has direct correlation, so that k and k are integrated f The most reasonable interval of statistical invariance of the two parameters is only 400 to 500 frames. In summary, a statistical amount of a minimum of 300 continuous flame images is acceptable for the morphology of global flames. For the dimensionless expression of the parameters and the flame lifting characteristics of the fluctuation area, at least 400 continuous flame images are needed to ensure the statistic invariance. Therefore, if in real-time flame monitoring, the appropriate statistical duration can be selected for the purpose according to requirements and economy.
Flame fluctuations are indicative of flame stability to some extent, and are characterized herein by δ and γ. Their statistical invariance curves are shown in fig. 12. In the whole, the two dimensionless parameters can represent the stability of the flame, and the comprehensive judgment statistical invariance interval is 400-500 frames. Therefore, in the preferred embodiment of the present application, the preferred range of the extracted continuous flame images is 400-500, i.e. 400 < n ≦ 500.
However, in detail, at a statistical number of 200, the value of δ has been reduced to around 0.23, but the value of the final δ statistical invariance is about 0.26, and 0.23 is sufficient to determine that the flame is stable and causes a false determination. Whereas γ has a value of about 0.34 higher than the invariance value of 0.325 at a statistical image number of 200, with no risk of judgment error.
In the interference statistical area above the statistical quantity of 800 frames, the delta value does not reflect the change condition of the flame completely in time, the change of the gamma value is very sensitive, the gamma value is obviously reduced to about 0.42, the error bar is obviously increased, and the explanation of the interference statistical area is printed. In contrast, γ does better characterize flame stability because the definition of γ is based on the height of flame fluctuation. It is more reliable to use the lifting characteristic of a horizontal jet spray flame having a tip to express the stability of the flame.
Therefore, in summary, for the horizontal jet combustion flames of different forms or fuels, the method for obtaining the optimal number n of continuous flame images required to be extracted for judging the stability of the horizontal jet flame is as follows:
step S1011: counting characteristic parameters of horizontal jet type flames of a preset number of flame images, wherein the characteristic parameters comprise L, H, L f 、H f 、k、k f δ and γ, said k being the flame lift slope, k f For the rising slope of the fluctuation region, in this embodiment, the minimum value of the preset number may be set to 100 sheets, the maximum value is theoretically equal to the number of seconds of the video × the shooting frame rate, and the maximum value of this embodiment may be set to 1000 sheets.
Step S1012: through L, H, L f 、H f 、k、k f Statistical invariant curves for flame delta and gamma (see fig. 7-12), decision L, H, L f 、H f 、k、k f Statistical independence of, delta and gamma, L, H, L f 、H f 、k、k f The number of irrelevancy corresponding to δ and γ is shown in equation (6), a j ≤n i ≤b j (6) Wherein a is j And b j Is a constant number, n i Are respectively L, H, L f 、H f 、k、k f The number of the continuous flame images corresponding to δ and γ, j being a natural number from 1 to 8;
step S1013: obtaining the requirements for determining the flame stability of the horizontal jet type according to the formula (7)The optimal number of consecutive flame images n, max { a) extracted j }≤n≤min{b j H 7, i.e. the minimum value of n isa i Is determined to be the maximum value of (c),nmaximum value ofb i Minimum value of (c).
The optimal threshold T may be obtained by a maximum inter-class variance method, a double peak method, a minimum error method, and other methods, and in this embodiment, the method for obtaining the optimal threshold T according to the gray value is as follows:
step S1031: and counting the accumulated frequency of each gray value in the continuous flame images.
Step S1032: and setting the gray value corresponding to the minimum value in the valley bottom area between the two peaks in the accumulated frequency as an optimal threshold value T.
Referring to fig. 13, a diagram of an optimal threshold T is shown. As can be seen from fig. 13, the gray level histogram is plotted with the abscissa as the gray level value and the integration frequency as the ordinate, it can be seen that the gray level histogram has two distinct peaks (the integration frequencies are 7000 and 3500, respectively), and the gray level value corresponding to the minimum value in the valley bottom region between the two peaks (see the partially enlarged view in fig. 13) is the optimal threshold T. The optimal threshold value T is obtained by adopting a double-peak method, and the method is simple and easy to obtain and has high accuracy.
In addition, in a preferred embodiment of the present disclosure, step S20 further includes the following steps:
step S201: according to the formula
Calculating the probability of flame occurrence, wherein P (x, y) is the pixel point (x, y)
The flame occurrence probability at the position, i is the number of the flame images, n is the number of the collected continuous flame images, and f (x, y) is 1 or 0;
step S202: and drawing a flame probability cloud picture according to the corresponding relation between the flame occurrence probability and the gray level of the preset pixel points (x, y).
In this embodiment, the flame occurrence probability is divided into 10 levels, that is, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1, and different flame occurrence probabilities may correspond to different grayscales according to a preset manner, in this embodiment, the higher the flame occurrence probability is, the larger the grayscale value in the flame probability cloud map is (as shown in fig. 3). In other embodiments of the present application, a flame probability cloud chart may also be drawn according to a preset correspondence between the flame occurrence probability and the chromaticity. For example, a region with a flame occurrence probability of 1 may be correspondingly drawn as red, a region with a flame occurrence probability of 0.1 may be correspondingly drawn as blue, and other regions may be drawn as a transition color between red and blue.
In order to make those skilled in the art better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
Claims (8)
1. A method for evaluating flame stability of a horizontal jet type, comprising the steps of:
correspondingly converting the obtained multiple continuous flame images into multiple binary images;
acquiring a flame probability cloud picture according to the flame occurrence probability P (x, y) of the same pixel position in the plurality of binary images;
calculating a horizontal dimensionless fluctuation parameter delta and a vertical dimensionless fluctuation parameter gamma according to the flame probability cloud chart, wherein,
，
，L f is the horizontal projection length of the flame fluctuation region, L is the horizontal projection length from the flame starting point to the flame end, H f Is the vertical projection length of the flame fluctuation area, and H is the vertical projection length from the flame starting point to the flame end point, and the flame fluctuation areaThe area is a region with the P (x, y) being more than or equal to 0.1 and less than or equal to 0.9, and the tail end of the flame is the intersection point of a flame trace and a probability contour line with the P (x, y) being 0.5 in the flame fluctuation region;
and judging the flame stability according to the horizontal dimensionless fluctuation parameter delta and the vertical dimensionless fluctuation parameter gamma.
2. The method for evaluating flame stability of a horizontal jet type according to claim 1, wherein the judging of flame stability from the horizontal dimensionless fluctuation parameter δ and the vertical dimensionless fluctuation parameter γ includes:
judging the horizontal stability of the flame according to the horizontal dimensionless fluctuation parameter delta and a formula (1),
judging the vertical stability of the flame according to the vertical dimensionless fluctuation parameter gamma and a formula (2),
if the horizontal stability of the flame and the vertical stability of the flame are both in a stable region, the flame has stability;
the flame has instability if at least one of a horizontal stability of the flame and a vertical stability of the flame is in an unstable range.
3. The method for evaluating flame stability of a horizontal jet type according to claim 1, wherein the judging of flame stability from the horizontal dimensionless fluctuation parameter δ and the vertical dimensionless fluctuation parameter γ includes:
judging the horizontal stability of the flame according to the horizontal dimensionless fluctuation parameter delta and a formula (3),
judging the vertical stability of the flame according to the vertical dimensionless fluctuation parameter gamma and a formula (4),
if the horizontal stability of the flame and the vertical stability of the flame are both in a stable region, the flame has stability;
the flame tends to be unstable if at least one of the horizontal stability of the flame and the vertical stability of the flame is in a tendency instability interval;
a flame has extreme instability if at least one of the horizontal stability of the flame and the vertical stability of the flame is in an extremely unstable region.
4. The method for evaluating flame stability of a horizontal jet type according to claim 1, wherein the converting the acquired plurality of continuous flame images into binary images respectively comprises:
acquiring the optimal number n of continuous flame images required to be extracted for judging the stability of the horizontal jet type flame;
extracting n continuous flame images from an input video file;
carrying out gray processing on the flame image to obtain a gray value of each pixel in the flame image;
obtaining an optimal threshold value T according to the gray value, and assigning each pixel position in the flame image according to a formula (5)
Value off(x, y) converting the gray value into binary value to obtain binary image of the flame image,
5. the method for evaluating flame stability of a horizontal jet type according to claim 4, wherein the obtaining of the optimal number n of continuous flame images to be extracted for determining flame stability of a horizontal jet type comprises the steps of:
counting characteristic parameters of horizontal jet type flames of a preset number of flame images, wherein the characteristic parameters comprise L, H, L f 、H f 、k、k f δ and γ, said k being the flame lift slope, k f Raising the slope for the fluctuation region;
through L, H, L f 、H f 、k、k f Delta, and gamma, and a determination L, H, L of the statistical invariance curve of the flame f 、H f 、k、k f Statistical independence of, delta and gamma, L, H, L f 、H f 、k、k f The amounts of independence corresponding to δ and γ are shown in equation (6),
a j ≤n i ≤b j （6），
wherein, a j And b j Is a constant number, n i Are respectively L, H, L f 、H f 、k、k f The number of the continuous flame images corresponding to δ and γ, j being a natural number from 1 to 8;
obtaining the optimal number n of continuous flame images required to be extracted for judging the stability of the horizontal jet type flame according to the formula (7),
max{a j }≤n≤min{b j } （7）。
6. the method of claim 4, wherein 400 < n.ltoreq.500.
7. The method for evaluating flame stability of a horizontal jet type according to claim 4, wherein obtaining a flame probability cloud according to the flame occurrence probability P (x, y) of the same pixel position in the plurality of binary images comprises:
according to the formula
Calculating the probability of flame occurrence, wherein P (x, y) is the position of the pixel point (x, y)
I is the flame image number;
and drawing a flame probability cloud picture according to the corresponding relation between the flame occurrence probability and the gray level of the preset pixel points (x, y).
8. The method of evaluating flame stability of a horizontal jet type according to claim 4, wherein obtaining an optimal threshold value T from the gray-level values comprises:
counting the accumulated frequency of each gray value in the continuous flame images;
and setting the gray value corresponding to the minimum value in the valley region between the two peaks in the accumulated frequency as an optimal threshold value T.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101750152A (en) * | 2009-12-17 | 2010-06-23 | 昆明理工大学 | Method for representing and diagnosing combustion instability |
| CN102609727A (en) * | 2012-03-06 | 2012-07-25 | 中国人民解放军理工大学工程兵工程学院 | Fire flame detection method based on dimensionless feature extraction |
| CN103077519A (en) * | 2012-12-31 | 2013-05-01 | 天津大学 | Method for automatically monitoring flame combustion stability |
| CN111489342A (en) * | 2020-04-09 | 2020-08-04 | 西安星舟天启智能装备有限责任公司 | Video-based flame detection method and system and readable storage medium |
-
2022
- 2022-07-13 CN CN202210817613.9A patent/CN114897897B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101750152A (en) * | 2009-12-17 | 2010-06-23 | 昆明理工大学 | Method for representing and diagnosing combustion instability |
| CN102609727A (en) * | 2012-03-06 | 2012-07-25 | 中国人民解放军理工大学工程兵工程学院 | Fire flame detection method based on dimensionless feature extraction |
| CN103077519A (en) * | 2012-12-31 | 2013-05-01 | 天津大学 | Method for automatically monitoring flame combustion stability |
| CN111489342A (en) * | 2020-04-09 | 2020-08-04 | 西安星舟天启智能装备有限责任公司 | Video-based flame detection method and system and readable storage medium |
Non-Patent Citations (6)
| Title |
|---|
| KAI XIE等: "Study on statistical invariance of probability processing and fluctuation characteristics of consecutive images of horizontal spray flame under low-pressure", 《CASE STUDIES IN THERMAL ENGINEERING》 * |
| KAI XIE等: "Theoretical analysis and experimental investigation of flame trajectory of horizontal subsonic jet diesel spray under sub-atmospheric pressure", 《FUEL》 * |
| 祖春光等: "基于火焰图像的燃烧稳定性诊断和分析", 《湖北电力》 * |
| 谢凯等: "不同环境温度下水平喷雾燃烧火焰形态实验研究", 《工业加热》 * |
| 谢凯等: "低压环境下水平射流喷雾燃烧火焰形态实验研究", 《工业加热》 * |
| 陈书谦等: "基于神经网络的火焰燃烧稳定性算法研究", 《南京师大学报(自然科学版)》 * |
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