Most of the existing non-contact flame temperature measurement methods rely on the ideal thermal-optical excitation model, which has a great influence on temperature measurement accuracy. Therefore, based on element doping and energy spectrum analysis, this study proposes a novel twodimensional (2-D) estimation method for flame temperature and emissivity distribution. The element doping method and laser-induced breakdown spectroscopy (LIBS) are introduced into the temperature field test. The external doped element whose spectral radiation characteristics are easy to be analyzed, is regarded as the measured particles to describe the flame temperature distribution from the side. And LIBS is used to analyze and select the doped element, and further determine the effective working wavelength of the optical camera. Besides, the relationship between spectral radiance and emissivity (L -ε) of doped samples is obtained by the emissivity calibration experiment. Then, the 2-D temperature and emissivity distributions can be estimated. Infrared thermograph is used to verify the accuracy of temperature measurement, the measurement error between calculated and standard values is not more than 5%. The experimental results of the oxygen-ethanol combustion flame show that this method can be well applied to the similar temperature measurement.INDEX TERMS Temperature measurement, Spectral emissivity, Element doping, Spectral analysis.
In this study, based
on the existing high-temperature measurement
and calibration equipment, calibration experiments using the spectral
emissivity of intrinsic element particles in the field were designed
to achieve the accurate measurement of a temperature field. Laser-induced
breakdown spectroscopy was used to select the corresponding elements,
and the element doping method was used to approximate the real temperature
field. After calibrating the camera, the temperature distribution
and spectral emissivity distribution of the flame were calculated.
The range of calculated values was determined to be well-consistent
with data collected using an infrared thermal imager, which verified
the accuracy of the experiment.
In this study, an improved flame edge detector based
on convolutional
neural network (CNN) was proposed. The proposed method can generate
edge graphs and extract edge graphs relatively effectively. Our network
architecture was based on VGG16 primarily, the last two max-pooling
operators and all full connection layers of the VGG16 network were
deleted, and the rest was taken as the basic network. The images output
by the five convolution layers were upsampled to the size of the input
images and finally fused to the edge image. Error calculation and
back propagation of the fusion image and label image are carried out
to form a weakly supervised model. Using the open datasets BSDS500
to train the network, the ODS F-measure can reach 0.810. Various experiments
were carried out on different flame and fire images, including butane–air
flame, oxygen–ethanol flame, energetic material flame, and
oxygen–acetylene premixed jet flame, and the infrared thermogram
was also verified by our method. The results demonstrate the effectiveness
and robustness of the proposed algorithm.
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