Flame temperature imaging measurement method fusing electrical imaging and single-path absorption spectrum

Abstract

The invention provides a flame temperature imaging measurement method fusing electrical imaging and a single-path absorption spectrum. The flame temperature imaging measurement method mainly comprises three steps of parameter initialization, histogram mapping relation construction and temperature distribution reconstruction. The method comprises the following steps: firstly, in parameter initialization, calculating a temperature histogram by utilizing a single-path laser absorption spectrum, reconstructing a dielectric constant on a laser path by utilizing electrical imaging, and establishing a dielectric constant histogram; secondly, in histogram mapping relation construction, a mapping model from dielectric constant histograms to temperature histograms is constructed through a neural network, the dielectric constant histograms corresponding to multiple straight lines penetrating through the imaging area are obtained, and the dielectric constant histograms are substituted into the mapping model to obtain the corresponding temperature histograms; and finally, in temperature distribution reconstruction, temperature distribution in the whole area is solved according to the temperature histograms on all the straight lines. According to the invention, the single-path laser absorption spectrum and the electrical imaging are fused, the temperature distribution of the whole area is obtained, and the method has important application value for temperature measurement of an extreme environment.

Description

Technical Field

[0001] The present invention relates to a flame temperature imaging measurement method integrating electrical imaging and single-path absorption spectroscopy, and belongs to the field of electrical imaging. The two-dimensional dielectric constant reconstruction information of the electrical imaging technology is fully integrated with the single-angle laser absorption spectrum information to construct a mapping model on the same path, thereby obtaining the temperature information corresponding to multiple straight lines of the laser absorption spectrum of the entire area passing through the imaging area, and realizing the reconstruction of the two-dimensional temperature field. Background Art

[0002] Combustion is the main form of energy conversion. The safety, efficiency and cleanliness of the combustion process are crucial to ensure the normal operation of major fields such as electricity, energy, transportation, aerospace, etc. Laser absorption spectroscopy (LAS) has the advantages of no need for preprocessing, fast response speed, accurate data, simultaneous detection of multiple parameters, and strong environmental adaptability. However, it requires a certain number of optical path layouts for measuring the combustion temperature field. When the projection layout is limited, the combustion temperature field measurement cannot be achieved, and only the temperature measurement in the corresponding optical path can be achieved. Electrical tomography (ET) technology, as a visual monitoring method for the distribution of electrical parameters of complex flow systems, is widely used in flow pattern identification, oil pipeline working condition detection, flame electrical parameter distribution visualization monitoring and many other aspects. It has the characteristics of no radiation, non-invasiveness, fast response, low cost, no moving parts, reliable operation, and no need for optical windows.

[0003] Electrical imaging technology is a non-contact measurement method developed based on the electrostatic field model. When the electrical parameters of the medium in the measured field change, the admittance between the two electrodes of the sensor will change. By measuring the boundary admittance value and combining it with the image reconstruction algorithm, the distribution of electrical parameters of the medium in the field can be obtained. The electrical parameters of the medium include dielectric constant and conductivity. When focusing on the dielectric constant characteristics of the medium, electrical imaging is called electrical capacitance tomography (ECT); when focusing on the conductivity characteristics of the medium, electrical imaging is called electrical resistance tomography (ERT).

[0004] In the combustion field, the fuel produces a large number of neutral particles, free radicals and electrons under the action of chemical ionization and thermal ionization, which makes the electrical parameters of the flame change significantly compared with the air. Therefore, electrical imaging technology can be used to visualize the flame distribution. In addition, the electrical parameters of the flame are related to the combustion field temperature, particle type, particle concentration and other parameters. Therefore, electrical imaging can monitor the dielectric constant and conductivity distribution in the flame and realize the imaging of electrical parameters; according to the theoretical model or simplified model of electrical parameters and temperature, ET can realize direct reconstruction of temperature; however, there are too many unknown parameters in the theoretical model of temperature measurement and it is not mature in practical application. It needs to be integrated with other temperature measurement methods to construct a model of dielectric constant and temperature.

[0005] Laser absorption spectroscopy is a technology based on line-of-sight measurement. It uses the absorption of lasers in a specific wavelength band by gas and calculates the gas absorption rate based on the Beer-Lambert law. The light intensity absorption rate in the optical path is strongly coupled with parameters such as the temperature and component concentration of the absorbing molecules. The parameters are decoupled through methods such as dual-line colorimetry and multi-spectral measurement to achieve quantitative measurement of multiple parameters in the combustion field. The basic principle is that when a laser beam passes through the gas flow field to be measured, the laser light intensity will attenuate due to the absorption of the molecules. The attenuation value is related to parameters such as the temperature, component concentration, pressure of the absorbing molecules and the path length of the laser. By scanning the wavelength of the incident laser within a certain range, the absorption spectrum of the molecule to be measured can be obtained, and then the parameters on the laser path can be solved. According to the absorption spectrum data on a single path, the mean value of the combustion field parameters along the laser path can be solved, but the spatial distribution of the parameters cannot be reflected. To this end, the laser absorption spectroscopy technology is combined with the tomography technology to obtain the absorption spectrum data of multiple angles and multiple optical paths, and the distribution of the combustion field parameters can be solved through the tomography algorithm. In 2014, Lan Lijuan et al. published an article entitled "Theoretical and Experimental Study on Tunable Diode Laser Absorption Spectroscopy to Measuring Gas Temperature in Vacuum Environment" in "Acta Physica Sinica", Vol. 63, No. 8, pp. 101-109. They used tunable diode laser absorption spectroscopy (TDLAS) technology to measure the vibrational-rotational temperature of gas molecules in a vacuum environment. When the gas absorption rate of laser is greater than 2%, the TDLAS technology can be used to measure the vibrational-rotational temperature of gas molecules in a vacuum environment. It has a very high response speed and is highly consistent with the gas temperature and constant temperature bath temperature measured by a first-class standard platinum resistance. The error between the two does not exceed ±0.2°C when the constant temperature bath temperature is stable. In 2015, Liu C et al. published an article entitled "Development of a fan-beam TDLAS-based tomographic sensor for rapid imaging of temperature and gas concentration" in Optics Express, Vol. 23, No. 17, pp. 22494-22511. They designed a five-angle fixed fan-beam TDLAS sensor, which reduced the distance between two adjacent detectors, thereby increasing the spatial resolution to 0.78 cm. They collected 60 absorption lines of two water molecules on the measurement light path at a frame rate of 83.3 frames / s, and monitored the temperature distribution of McKenna plane flames and swirl dynamic flames of different shapes. Experiments showed that the reconstructed temperature and H2O concentration distributions were consistent with the expected distributions.In the current single-path laser absorption spectroscopy system (such as patent: Gas temperature probability density distribution fitting and reconstruction method based on single optical path multi-spectrum, application number: CN201710469033.4), multiple absorption spectra can be used to reconstruct the gas temperature probability density distribution. Compared with the traditional LAS that can only obtain temperature mean information, this method can obtain more information, but cannot obtain the position information of the temperature value on the path. It requires temperature histograms with more projection angles to reconstruct the temperature corresponding to each grid in the entire area.

[0006] However, when laser absorption spectroscopy is used to perform two-dimensional temperature field imaging, a complete optical window needs to be opened. Single-channel LAS only needs to open two holes in the opposite position of the combustion chamber to measure the average temperature on the path, but it cannot obtain the temperature information of each grid on the path, let alone reconstruct the two-dimensional temperature distribution of the entire field.

[0007] Electrical imaging technology uses a reconstruction algorithm to reconstruct the two-dimensional distribution of the dielectric constant and conductivity in the measured area based on the change in the dielectric constant in the measured area, which will cause the change in the impedance value between the electrode pairs on the sensor. Because it is a non-invasive measurement, it has the advantages of fast response speed, low imaging cost, and no need to open windows. It has been applied to the measurement of electrical parameters in the combustion field. In 2000, RCWaterfall published "Imaging Combustion Using Electrical Capacitance Tomography" at the IEE SeminarAdvanced Sensors and Instrumentation Systems for Combustion Processes. It is used to observe the position of the jet flame in the combustion tank. It can be installed in an actual burner and has a time resolution of several milliseconds, which is sufficient to monitor and control jet engines. In 2008, Lis S et al. published an article entitled “Preliminary study on ECT imaging of flames in porous media” in the 9th issue of Volume 19 of Measurement Science and Technology. They analyzed the influence of flame ionization effect on dielectric constant and used ECT technology to study flame distribution in porous media and image flames. They were able to track the movement of flames and detect multiple flames. The color distribution of flame images corresponded well to the combustion intensity and could reflect the shape, position and gas flow changes of the flame. In 2010, Gut Z. and Wolanski P. published an article titled “Flame Imaging Using 3D Electrical Capacitance Tomography” in Combustion Science and Technology, Vol. 182, No. 11-12, pp. 1580-1585. They fabricated a three-plane ECT sensor with 18 electrodes (6 electrodes and 3 rows of electrodes per plane) to obtain more realistic flame reconstruction images.In 2013, Jia Y et al. published a paper titled "Theoretical Analysis of the Dielectric Characteristics of Plasma Flame and Imaging plasma flame Using Electrical Capacitance Tomography" at the 2013 IEEE International Conference on Imaging Systems and Techniques. Based on the analysis of plasma flame ionization phenomena, polarization modes, and the displacement polarization mechanism of bound electrons and free electrons, they proposed a theoretical model of the dielectric constant of plasma flames. Combined with the mathematical model of ECT sensors and multi-point calibration measurement data, they obtained the distribution of the dielectric constant of plasma flames in the measurement area, providing new methods and valuable data for ECT research on plasma combustion. In 2021, Hu D et al. published an article titled "Revised Calderon Method of Annular ECT for Imaging Flashback Flame of a Bluff-Body Burner" in Volume 70 of IEEE Transactions on Instrumentation and Measurement. They used an eight-electrode circular sensor to visually monitor the flashback process under different combustion conditions in a blunt-body burner, and used reconstructed grayscale values ​​to quantitatively characterize the flashback intensity and flashback time, providing a new means for the study of the mechanism of combustion stability. In 2024, Jin S et al. published an article titled "Estimation of Temperature for Premixed Flame by Relative Permittivity and Conductivity" in Volume 73 of IEEE Transactions on Instrumentation and Measurement. They proposed a premixed flame temperature estimation method based on relative permittivity and conductivity, established a mechanism model between the relative permittivity, conductivity and temperature of the premixed flame, and realized the temperature estimation of the premixed flame.

[0008] To measure the temperature of the combustion field using electrical imaging technology, it is necessary to establish a mapping model of dielectric constant, conductivity and temperature. However, this model requires the known parameters such as the type, concentration and collision frequency of particles in the combustion field. These parameters are difficult to obtain, and it is not necessarily reasonable to simplify the model by simulation and other means. Therefore, the use of ECT for combustion field temperature monitoring needs to consider integrating with other temperature measurement methods to establish a mapping model of dielectric constant, conductivity and temperature. In 2016, Chen Q et al. published an article entitled "Direct Measurements of Permittivity of Plasma-Assisted Combustion Using Electrical Capacitance Tomography" in IEEE Transactions on Plasma Science, Vol. 44, No. 12, pp. 3009-3016. They used thermocouples to measure the single-point flame temperature, and Langmuir probes to measure the electron density and electron temperature, and obtained the theoretical value of the flame dielectric constant, which was compared and verified with the ECT measurement results. This method uses an intrusive measurement method to obtain the temperature and electrical parameters of the combustion field. Although it interferes with the flow field and the spatial resolution of the measurement results is low, it still provides a new idea for the electrical imaging technology to monitor the temperature parameters of the combustion field. In 2018, Liu J et al. published an article entitled "Internal structure visualization of flow and flame by process tomography and PLIF datafusion" in the Journal of Thermal Science, Vol. 27, No. 1, pp. 64-73. They used electrical imaging and laser induced fluorescence technology to monitor the flame combustion process simultaneously. The high dielectric constant part in the ECT reconstructed image can characterize the interface between the inner cone and the outer shell of the flame. This interface has the strongest reaction and the highest dielectric constant. The picture captured by OH-PLIF shows the distribution of OH radicals in the flame on the same sheet. The two images show good consistency in shape, showing a strong correlation between the dielectric constant and OH radicals in the flame, proving the feasibility of using ECT to measure flames.In 2020, Hu D et al. published an article entitled "Estimation of Combustion Temperature Field from the Electrical Admittivity Distribution Obtained by Electrical Tomography" in IEEE Transactions on Instrumentation and Measurement, Volume 69, Issue 9, Pages 6271-6280. In order to solve the complex problem of the mapping model of dielectric constant and conductivity to temperature, ion probes and thermocouples were used to measure the electrical parameters and temperature at different positions of the alcohol lamp flame. They proposed to use polynomial, exponential and extreme gradient boosting (XGBoost) regression modeling methods to fit the mapping model and use it in the combustion field temperature estimation. The conductivity and dielectric constant distribution at different heights of the Bunsen burner flame were reconstructed. However, thermocouples and ion probes will destroy the flow field and will cause severe interference to the dielectric constant and conductivity distribution in the sensitive field. Therefore, the above method can only perform offline calibration of electrical imaging, and cannot monitor the same combustion state of the same flame. In 2024, Tian Y et al. published a paper titled "Dynamic Cross-Sectional Temperature Imaging From LAS Labeled Electrical Tomography" in Volume 73 of IEEE Transactions on Instrumentation and Measurement, reconstructing the dielectric constant and conductivity distribution of the Bunsen burner flame under various fuel-rich combustion states. LAS is used to reconstruct the temperature distribution in the same cross section, and the combustion state of the flame is changed by changing the equivalence ratio of fuel to air. The random forest regression model is used to construct a mapping of electrical parameters to temperature under different combustion conditions as a temperature measurement model, realizing the electrical imaging technology to monitor the temperature distribution of the combustion field under different combustion conditions. However, this method requires the use of a two-dimensional LAS temperature measurement system, which requires the opening of a complete optical window and many optical paths.

[0009] Combining the advantages of LAS and ECT technologies in combustion field measurement, a single-channel LAS signal is used as a temperature reference to establish a mapping relationship from dielectric constant to temperature, and then the temperature of the combustion field is reconstructed; especially under some conditions that are not conducive to the use of optical measurement methods, such as the blocking of light signals by solid particles in harsh environments and the difficulty in opening optical windows, the two-dimensional temperature distribution of the combustion field can be obtained, which has important engineering application prospects.

[0010] Based on the above background, the present invention proposes a flame temperature imaging measurement method that integrates electrical imaging and single-channel absorption spectroscopy, uses a single-channel LAS to obtain the light intensity change on the path, and uses the SART algorithm to obtain the temperature histogram, uses electrical imaging and LAS on the same path to obtain the one-dimensional dielectric constant distribution, and obtains the dielectric constant histogram according to the selected dielectric constant maximum and minimum values; uses a neural network to construct a mapping from the dielectric constant histogram to the temperature histogram, sets the existence of several straight lines, determines the linear equation of each straight line, obtains the dielectric constant distribution of each straight line according to the linear equation and obtains the corresponding dielectric constant histogram, substitutes it into the mapping relationship to obtain the temperature histogram corresponding to all straight lines; finally uses the SART algorithm to solve and obtain the temperature distribution in the entire area. The advantage of this method is that it makes full use of the dielectric constant information, fuses it with the histogram information of the single-channel LAS to construct a mapping, and expands it to several straight lines, so that the temperature field information of the measured flame can be calculated, and only two optical holes need to be opened at most, and the system has a simple structure and low cost. Summary of the invention

[0011] 1. Technical issues to be resolved

[0012] The purpose of the present invention is to propose a flame temperature imaging measurement method that integrates electrical imaging and single-path absorption spectroscopy. The method makes full use of the two-dimensional dielectric constant reconstruction information of electrical imaging technology and fuses it with the single-angle laser absorption spectrum information to construct a mapping relationship on the same straight line path, thereby obtaining the temperature information corresponding to the laser absorption spectrum straight line of the entire area and realizing two-dimensional temperature distribution imaging.

[0013] (II) Technical solution

[0014] The present invention is a flame temperature imaging measurement method integrating electrical imaging and single-path absorption spectroscopy, which mainly comprises the following steps:

[0015] Step 1: Obtain the temperature histogram on the path through a single-channel laser absorption spectroscopy system; the system includes a laser generator and controller, the flame gas to be measured, and a photoelectric detector. A laser of a specific wavelength is used to pass through the center of the flame. The target gas absorbs the laser, causing the light intensity to attenuate. The detector receives the attenuation of the light intensity signal. The laser beam v(t) after high-frequency modulation can be expressed as:

[0016]

[0017] in, represents the central wave number before modulation, a d Modulation depth, f is the modulation frequency of the high-frequency sinusoidal signal. The laser light intensity I0(t) after high-frequency modulation can be expressed as:

[0018]

[0019] in, is the average light intensity of low-frequency scanning, b is the laser intensity modulation coefficient, It is the harmonic component of each modulation frequency of the laser. According to the Beer-Lambert law, the central wave number is located at ν0 [cm -1 After the laser passes through the gas to be measured, its light intensity is at wave number ν [cm -1 The absorption rate α(ν) at ] is:

[0020]

[0021] Substituting formula (1) and (2) into formula (3), the intensity of the laser beam after passing through the target gas is obtained as:

[0022]

[0023] Where τ[v(t)] is the transmission coefficient of the laser.

[0024] For the case of small absorption of the target gas, the transmission coefficient can be approximated as:

[0025]

[0026] Expand formula (5) into Fourier series form:

[0027]

[0028] Expand the Fourier coefficients:

[0029]

[0030] For a single-channel measurement system, M temperature values ​​T are discretely selected. m , m=1,2,…,M, the corresponding absorption distance is L m , m=1,2,…,M; calculate K absorption spectra β per unit path length k (v), k = 1, 2, ..., K; corresponding to the Fourier coefficients satisfy:

[0031]

[0032] The first harmonic and second harmonic components are extracted from the transmitted light intensity signal using a lock-in amplifier and a low-pass filter, and the normalized second harmonic signal S can be obtained. 2f / 1f for:

[0033]

[0034] Among them, R 2f and R 1f are the amplitudes of the second harmonic and first harmonic signals, respectively, X 1f and Y 1f are the X and Y components of the first harmonic, X 2f and Y 2f are the X and Y components of the second harmonic.

[0035] For a single-channel LAS measurement system, M discrete gas temperature values ​​are selected and the normalized second harmonic signal R of the i-th pair of gas temperature values ​​per unit path length is calculated. 2f / 1f,i From equation (9), we can get that the normalized second harmonic signal of a single channel and the normalized second harmonic signal absorbed by M discrete gas temperature value units satisfy:

[0036]

[0037] According to formula (10), the path length corresponding to the selected M discrete gas temperature values ​​can be obtained and a temperature histogram can be established.

[0038] Step 2: Calculate the dielectric constant value on the same path as the single-channel LAS and establish a dielectric constant histogram; solve the dielectric constant distribution through the obtained capacitance value, and the sensitive field of electrical imaging is:

[0039]

[0040] Among them, z = x + iy is a complex number representing the coordinates (x, y), γ(z) = σ(z) + iωε(z) and are the complex conductivity and corresponding potential at point z, respectively.

[0041] Assume that any analytical function in the region is v(z), then:

[0042]

[0043] Because there are:

[0044]

[0045] Combining formula (13) and formula (12), we have:

[0046]

[0047] According to the divergence theorem, the integral of the dielectric constant changing in space can be linked to the boundary voltage and current measurement. get:

[0048]

[0049] The mapping of voltage to current density on the boundary of the measured field, namely the Dirichlet-to-Neumann mapping, is:

[0050]

[0051] in, represents the mapping from voltage to current density when there is a dielectric constant distribution γ0(z) in the sensitive field Ω, It represents the mapping from voltage to current density when there is a dielectric constant distribution of (γ0+Δγ)(z) in the sensitive field Ω, then:

[0052]

[0053] Subtract equations (18) and (17), and the Calderon method assumes that the potential distribution is approximately unchanged when the dielectric constant change Δγ(z) is small, that is, Then we have:

[0054]

[0055] because Satisfies Laplace's equation set up is a special solution, and let k=k1+ik2,z=x+iy. So we have:

[0056]

[0057] Wherein, t(k) is the scattering transform, which satisfies the form of Fourier transform.

[0058] Compute the scattering transform:

[0059]

[0060] in, P is the number of measuring electrodes, θ i represents the center of the electrode plate of the ith electrode, and A is the area of ​​the electrode plate.

[0061] The change in dielectric constant Δε(z) can be obtained by performing a two-dimensional inverse Fourier transform on t(k):

[0062]

[0063] The Calderon method is used to calculate the dielectric constant distribution on the path. Formula (22) is expressed as:

[0064]

[0065] Where R is the radius of the numerical integration region.

[0066] Use formula (23) to obtain the dielectric constant distribution on the path and find the maximum dielectric constant ε max and the minimum value ε min , and ε max and ε min Divide into N values, namely ε n ,n=1,2,…,N;Find the grid on the path corresponding to the single LAS, and put the dielectric constant value of each grid into ε n , n=1,2,…,N, establish the dielectric constant histogram on the path.

[0067] Step 3: Use a neural network to establish a mapping relationship from the dielectric constant histogram to the temperature histogram on the same path, and obtain the temperature histogram matrix of Q straight lines passing through the imaging area; the fully connected neural network is a multi-layer perceptron structure, which includes three input layers, hidden layers, and output layers. The number of hidden layers in this method is set to 1. The neural network input is the N values ​​of the dielectric constant histogram, and the output is the M values ​​of the temperature histogram.

[0068] The number of neurons in the hidden layer is selected according to the empirical formula:

[0069]

[0070] Among them, n Neurons is the number of neurons, N is the number of indicators in the input layer, that is, the number of values ​​in the dielectric constant histogram, M is the number of indicators in the output layer, that is, the number of values ​​in the temperature histogram, and c is an empirical constant, usually a number in the range of [1,10].

[0071] The output of the jth node in the hidden layer is:

[0072]

[0073] Among them, h j is the output value, ω ij is the connection weight between the i-th node in the input layer and the j-th node in the hidden layer, x i is the input variable of the i-th node in the input layer, β j is the threshold of the jth node in the hidden layer, and f(·) is the activation function.

[0074] The output of the mth node in the output layer is:

[0075]

[0076] Among them, y out is the output value, η jm is the connection weight between the jth node in the hidden layer and the mth node in the output layer, σ m is the threshold of the mth node in the output layer.

[0077] The imaging area is discretized into D grids, and a rectangular coordinate system is established with the center of the imaging area as the origin. The equations of the Q straight lines passing through the imaging area are:

[0078] xcosθ Q×1 +ysinθ Q×1 =ρ Q×1 (27)

[0079] Among them, θ Q×1 is the set of angles between the normal and the horizontal axis of each of the Q selected straight lines, ρ Q×1 is the set of distances from the origin to each of the Q selected straight lines.

[0080] Use formula (27) to determine the grid through which the straight line equation passes, and use formula (23) to calculate the dielectric constant value of each straight line passing through the grid, and put it into the dielectric constant value ε n , n = 1, 2, ..., N, so that each straight line establishes a dielectric constant histogram on the corresponding path, that is, Q dielectric constant histograms. Substitute the Q dielectric constant histograms into the fully connected neural network mapping model of formulas (25) and (26) to obtain Q temperature histograms corresponding to each straight line.

[0081] Step 4: Two-dimensional temperature distribution imaging; establish the sensitivity matrix W according to the layout of the selected Q straight lines Q×D , where the element w in the i-th row and j-th column is i,j is the length of the i-th path through the j-th grid. Using the 0-1 binary matrix Y D×M Characterize the temperature distribution of the target gas field, matrix Y D×M Each row of represents a grid. There is an element 1 in the row, and the rest of the elements are 0. The temperature value at the location is obtained according to the column where the element 1 is located, that is:

[0082]

[0083] in, represents the temperature value of D grids, and the superscript R indicates the reconstructed distribution;

[0084] Histogram matrix L Q×MIt represents the path lengths occupied by the M gas states on the Q straight lines. The Q straight lines satisfying formula (10) are written in matrix form as follows:

[0085] S Q×K =L Q×M ·R M×K (29)

[0086] Among them, the normalized second harmonic of the M gas parameters in formula (10) is 2f / 1f,i Arrange row by row to form the harmonic basis matrix R M×K , K is the number of data points of the normalized second harmonic signal, and the matrix of the normalized second harmonic signal arranged row by row is S Q×K .

[0087] The definition of the sensitivity matrix is:

[0088] L Q×M =W Q×D ·Y D×M (30)

[0089] Since the histograms on multiple straight lines passing through the imaging area satisfy that the sum of their lengths is equal to the total length of the straight lines, the constraint is satisfied:

[0090] L Q×M ·e M×1 =W Q×D ·e D×1 (31)

[0091] Among them, the column vector e M×1 and e D×1 are all unit vectors.

[0092] Combining equations (29), (30) and (31), we can obtain the reconstructed temperature field model:

[0093] W Q×D ·Y D×M ·(R M×K e M×1 )=(S Q×K W Q×D ·e D×1 ) (32)

[0094] Using formula (32) for matrix Y D×M An iterative solution is performed and a two-dimensional temperature distribution image is reconstructed according to formula (28).

[0095] (III) Beneficial effects

[0096] The beneficial effect of the present invention is to propose a flame temperature imaging measurement method that integrates electrical imaging and single-channel absorption spectroscopy. Electrical imaging does not require an optical window to monitor the flame, but can only obtain the distribution of the dielectric constant, but cannot obtain temperature information; and the single-channel laser absorption spectrum cannot reconstruct the two-dimensional temperature field, and can only obtain the average temperature on the path. The present invention integrates electrical imaging and single-channel laser absorption spectroscopy, uses an electrical imaging system and a laser optical path, obtains a dielectric constant histogram and a single-channel laser absorption spectrum temperature histogram, constructs a mapping model for the histogram on the same path, and obtains the temperature histogram of multiple straight lines passing through the imaging area, and finally realizes the reconstruction of the two-dimensional temperature field. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Attached Figure 1 :The specific implementation diagram of this method

[0098] Attached Figure 2 : Schematic diagram of the reconstruction system structure, which consists of the following parts: laser control and generation module (101), capacitor array sensor (102), imaging area (103), photodetector (104), laser absorption spectrum data acquisition module (105), electrical tomography acquisition module (106) and host computer (107)

[0099] Attached Figure 3 : is the flame distribution of the experiment, which is the object of temperature field reconstruction. The area between the two dotted lines is the electrical imaging measurement position, and the middle position between the two dotted lines is the measurement position of the laser absorption spectrum.

[0100] Attached Figure 4 : Temperature distribution reconstructed under 45×45 grid division DETAILED DESCRIPTION

[0101] Reference Figure 1 is a specific implementation diagram of this method, and Appendix 2 is a schematic diagram of the imaging system. Figure 3 The flame distribution of the experiment is shown in the following figure. Figure 4 To reconstruct the temperature field distribution, the specific steps combined with an example are as follows:

[0102] Step 1: Obtain the temperature histogram on the path through a single LAS; Figure 2 The single-channel LAS system shown in the figure includes a laser generator and controller, the flame area to be measured and the photoelectric detector, as well as a data acquisition module and a host computer. -1 and 7444.4cm -1The lasers of the two spectral lines pass through the center of the flame. The wave number model is a 1kHz scanning sawtooth wave superimposed with a 200kHz sine modulation, and the modulation depth is 0.1. The lasers of a specific band pass through the center of the flame and are absorbed by the target gas, causing the light intensity to attenuate. The detector receives the attenuation of the light intensity signal and transmits it to the host computer through the data acquisition module.

[0103] Discretely select 6 temperature values ​​T1, T2, T3, T4, T5, T6, and the corresponding absorption distances are L1, L2, L3, L4, L5, L6. Calculate the absorption spectra β1(v) and β2(v) of the two uniform gas parameters per unit path length. For a single-channel LAS measurement system, select 6 discrete gas temperature values ​​and calculate the normalized second harmonic signal R of the i-th pair of gas temperature values ​​per unit path length. 2f / 1f,i The single-channel, normalized second harmonic signal and the normalized second harmonic signal absorbed by 6 discrete gas temperature value units satisfy:

[0104]

[0105] According to formula (1), the path lengths corresponding to the six selected gas temperature values ​​are obtained and a temperature histogram is established.

[0106] Step 2: Obtain the dielectric constant value on the same path as the single-path LAS and establish a dielectric constant histogram; Figure 2 In the electrical imaging system shown in the figure, when reconstructing the dielectric constant distribution, the two-dimensional field to be measured is evenly divided into 45×45 grids. The sensitive field is circular, with a total of 1517 grids and 45 grids passed through on the path. The dielectric constant distribution is calculated using Calderon:

[0107]

[0108] Use formula (2) to obtain the dielectric constant distribution on the path and find the maximum dielectric constant ε max and the minimum value ε min , and ε max and ε min Divide into 6 values, namely ε1, ε2, ε3, ε4, ε5, ε6. Find 45 grids on the path corresponding to the single-path LAS, put the dielectric constant value of each grid into ε1, ε2, ε3, ε4, ε5, ε6, and establish the dielectric constant histogram on the path.

[0109] Step 3: Use a neural network to establish a mapping relationship from the dielectric constant histogram to the temperature histogram on the same path, and obtain the temperature histogram matrix on the 189 straight lines passing through the imaging area; the fully connected neural network is a multi-layer perceptron structure, which contains three layers of input layer, hidden layer and output layer. The number of hidden layers in this method is set to 1, and the number of neurons is 9. Use a fully connected neural network to realize the forward transfer from the dielectric constant histogram to the temperature histogram.

[0110] The imaging area is discretized into 1517 grids, and a rectangular coordinate system is established with the center of the imaging area as the origin. The equations of the 189 straight lines passing through the imaging area are:

[0111] xcosθ 189×1 +ysinθ 189×1 =ρ 189×1 (3)

[0112] Among them, θ Q×1 is the set of angles between the normal and the horizontal axis of each of the 189 selected lines, ρ 189×1 It is the set of distances from the origin to each of the 189 selected lines.

[0113] Use formula (3) to find the grid through which the straight line equation passes, and use formula (2) to calculate the dielectric constant value of each straight line passing through the grid, and put it into the dielectric constant values ​​ε1, ε2, ε3, ε4, ε5, ε6, so as to establish the dielectric constant histogram on the corresponding path, that is, 189 dielectric constant histograms. Substitute the 189 dielectric constant histograms into the fully connected neural network mapping model to obtain 189 temperature histograms corresponding to each straight line.

[0114] Step 4: Two-dimensional temperature distribution imaging; establishing the sensitivity matrix W according to the layout of the selected 189 straight lines 189×1517 , where the element w in the i-th row and j-th column is i,j is the length of the i-th path through the j-th grid. Using the 0-1 binary matrix Y D×M Characterize the temperature distribution of the target gas field, matrix Y 1517×6 Each row of represents a grid. There is an element 1 in the row, and the rest of the elements are 0. The temperature value at that location is obtained according to the column where the element 1 is located:

[0115]

[0116] in, It represents the temperature values ​​of 1517 grids, and the superscript R indicates the reconstructed distribution;

[0117] Histogram matrix L 189×6It represents the path lengths occupied by the six temperature states on the 189 straight lines. The 189 straight lines satisfying formula (1) are written in matrix form as follows:

[0118] S 189×1180 =L 189×6 ·R 6×1180 (5)

[0119] Among them, 520 and 660 sampling points near the peak are selected for the two spectral lines respectively, and the two spectral lines form the measurement data matrix S on 189 optical paths. 189×1180 The six gas temperature parameters in formula (1) are normalized to the second harmonic R 2f / 1f,i Arrange row by row to form the harmonic basis matrix R 6×1180 .

[0120] The definition of the sensitivity matrix is:

[0121] L 189×6 =W 189×1517 ·Y 1517×6 (6)

[0122] Since the histogram of the 189 straight lines passing through the imaging area satisfies that the sum of their lengths is equal to the total length of the straight lines, the constraint is satisfied:

[0123] L 189×6 ·e 6×1 =W 189×1517 ·e 1517×1 (7)

[0124] Among them, the column vector e 6×1 and e 1517×1 are all unit vectors.

[0125] Combining formulas (5), (6) and (7), we can get the reconstructed temperature field model:

[0126] W 189×1517 ·Y 1517×6 ·(R 6×1180 e 6×1 )=(S 189×1180 W 189×1517 ·e 1517×1 ) (8)

[0127] Using formula (8) to transform the matrix Y 1517×6 Solve and reconstruct the two-dimensional temperature distribution image according to formula (4).

Claims

1. A flame temperature imaging measurement method integrating electrical imaging and single-channel absorption spectroscopy, characterized in that: The imaging system includes a circular cross-section electrical imaging sensor, a single-channel tunable laser generation module, a photoelectric detector, a data acquisition and processing module, etc., wherein the circular capacitance tomography sensor has P electrodes with an area of ​​A, an AC voltage is applied to the electrodes, and the other electrodes are grounded or kept at the same potential as the ground, so as to obtain the capacitance value change in the sensor; the laser passes through the central area of ​​the electrical imaging sensor, and the detector obtains the flame absorption spectrum; N temperature values ​​are selected and the temperature histogram is calculated; the dielectric constant distribution on the path corresponding to the laser absorption spectrum is reconstructed by electrical imaging, M dielectric constant values ​​are constructed and the dielectric constant histogram on the path is obtained; a mapping model from the dielectric constant histogram to the temperature histogram is established by using a neural network, Q straight lines passing through the imaging area are selected, the dielectric constant distribution corresponding to each straight line is reconstructed by using electrical imaging, the dielectric constant histogram of each straight line passing through the grid is obtained, and the temperature histogram of the grid on each straight line is obtained through the mapping model; a sensitivity matrix is ​​established according to the Q straight lines passing through the imaging area, and the temperature histogram and the sensitivity matrix are used to calculate and realize a two-dimensional temperature distribution image in the entire area.

2. The flame temperature imaging measurement method integrating electrical imaging and single-path absorption spectroscopy according to claim 1 is characterized in that: An optical path is used to obtain the mapping of the dielectric constant histogram to the temperature histogram. The electrical imaging technology is used to obtain the temperature histogram of Q straight lines passing through the imaging area. Combined with the grid division corresponding to all straight lines and the sensitivity matrix constructed by the straight lines, the two-dimensional temperature distribution image in the entire area is calculated. The specific steps include the following: Step 1: Obtain the temperature histogram on the path through a single-channel laser absorption spectroscopy system; laser light of a specific wavelength passes through the center of the flame and is absorbed by the target gas, resulting in light intensity attenuation. The light intensity of the high-frequency modulated laser beam after passing through the target gas is: Where, τ[v(t)] is the transmission coefficient of the laser, represents the central wave number before modulation, a d Modulation depth, f is the modulation frequency of the high-frequency sinusoidal signal, α(ν) is the light intensity at wave number ν [cm -1 ]; For a single-channel laser absorption spectroscopy system, M temperature values ​​T are discretely selected. m , m=1,2,…,M, the corresponding absorption distance is L m , m=1,2,…,M; calculate K absorption spectra β per unit path length k (v), k = 1, 2, ..., K; extract the first harmonic and second harmonic components from the transmitted light intensity signal to obtain a normalized second harmonic signal; simulate the normalized second harmonic signal of the selected i-th gas temperature value on the unit path length; the single-path, normalized second harmonic signal and the normalized second harmonic signal of the unit absorption of M discrete gas temperature values ​​satisfy: S 1×K =L 1×M ·R M×K (2) Among them, R M×K The normalized second harmonics of M gas parameters are arranged row by row to form a harmonic basis matrix, K is the number of data points of the normalized second harmonic signal, S 1×K A matrix arranged row by row for the normalized second harmonic signal; According to formula (2), the path length corresponding to the selected M discrete gas temperature values ​​is obtained, thereby establishing a temperature histogram; Step 2: Obtain the dielectric constant distribution on the same path as the single-path laser absorption spectrum and establish a dielectric constant histogram; use the Calderon method to calculate the dielectric constant distribution on the path and invert the dielectric constant distribution on the path: Where R is the radius of the numerical integration area, (x, y) is the coordinate value on the path, P is the number of measuring electrodes, and A is the area of ​​the electrode plate. θ i represents the plate center of the i-th electrode; The dielectric constant distribution on the path is obtained by formula (3), and the maximum dielectric constant ε is found. max and the minimum value ε min , and ε max and ε min Divide into N values, namely ε n ,n=1,2,…,N;Find the grid on the path corresponding to the single-path LAS, and put the dielectric constant value of each grid into ε n , n=1,2,…,N, a dielectric constant histogram on the path is established; Step 3: Use a neural network to establish a mapping relationship from a dielectric constant histogram to a temperature histogram on the same path, and obtain a temperature histogram matrix of Q straight lines passing through the imaging area; the fully connected neural network is a multi-layer perceptron structure, which includes three input layers, a hidden layer, and an output layer; the neural network input is N values ​​of the dielectric constant histogram, and the output is M values ​​of the temperature histogram; The imaging area is discretized into D grids, and a rectangular coordinate system is established with the center of the imaging area as the origin. The equations of the Q straight lines passing through the imaging area satisfy: x cosθ Q×1 +y sinθ Q×1 =ρ Q×1 (4) Among them, θ Q×1 is the set of angles between the normal and the horizontal axis of each of the Q selected straight lines, ρ Q×1 is the set of distances from the origin to each of the Q selected straight lines; Use formula (4) to find the grid that each straight line passes through, use formula (3) to calculate the dielectric constant value of each straight line passing through the grid, and put it into the dielectric constant value ε n , n=1,2,…,N, establish a dielectric constant histogram on the path corresponding to each straight line, that is, Q dielectric constant histograms; substitute the Q dielectric constant histograms into the fully connected neural network mapping model to obtain the straight line temperature histogram matrix corresponding to the Q lines; Step 4: Two-dimensional temperature distribution imaging; establish the sensitivity matrix W according to the layout of the selected Q straight lines Q×D , where the element w in the i-th row and j-th column is i,j is the length of the i-th path through the j-th grid; the matrix Y D×M is the temperature distribution of the target gas, Y D×M Each row of represents a grid. There is an element 1 in the row, and the rest of the elements are 0. The temperature value at that location is obtained according to the column where the element 1 is located: in, represents the temperature value of D grids, and the superscript R indicates the reconstructed distribution; The histograms on multiple straight lines passing through the imaging area satisfy that the sum of their lengths is equal to the total length of the straight lines, and the reconstructed temperature field model is: W Q×D ·Y D×M ·(R M×K e M×1 )=(L Q×M ·R M×K W Q×D ·e D×1 ) (6) Among them, e M×1 and e D×1 are all unit vectors, W is the sensitivity matrix; For the matrix Y of formula (6) D×M An iterative solution is performed, and then the two-dimensional temperature distribution image is reconstructed according to formula (5).

Images