Existing non-contact flame temperature measuring methods depend on complex, bulky and expensive optical instruments, which make it difficult for portable applications and high-density distributed networking monitoring. Here, we demonstrate a flame temperature imaging method based on a perovskite single photodetector. High-quality perovskite film epitaxy grows on the SiO2/Si substrate to fabricate the photodetector. Duo to the Si/MAPbBr3 heterojunction, the light detection wavelength is extended from 400 nm to 900 nm. Then, a perovskite single photodetector spectrometer has been developed using the deep-learning method for spectroscopic measurement of flame temperature. In the temperature test experiment, the spectral line of doping element K+ has been selected to measure the flame temperature. The photoresponsivity function of the wavelength was learned based on a commercial standard blackbody source. The spectral line of element K+ has been reconstructed using the photocurrents matrix by the regression solving photoresponsivity function. As a validation experiment, the “NUC” pattern is realized by scanning the perovskite single-pixel photodetector. Finally, the flame temperature of adulterated element K+ has been imaged with the error of 5%. It provides a way to develop high precision, portable, low-cost flame temperature imaging technology.
Resolution is an important index for evaluating the reconstruction performance of temperature distributions in a combustion environment, and a higher resolution is necessary to obtain more precise combustion diagnoses. Tunable diode laser absorption tomography (TDLAT) has proven to be a powerful combustion diagnosis method for efficient detection. However, restricted by the line-of-sight (LOS) measurement, the reconstruction resolution of TDLAT was dependent on the size of the detection data, which made it difficult to obtain sufficient data for extreme environmental measurements. This severely limits the development of TDLAT in combustion diagnosis. To overcome this limitation, we proposed a super-resolution reconstruction method based on the super-resolution residual U-Net (SRResUNet) to improve the reconstruction resolution using a software method that could take full advantage of residual networks and U-Net to extract the deep features from the limited data of TDLAT to reconstruct the temperature distribution efficiently. A simulation study was conducted to investigate how the parameters would affect the performance of the super-resolution model and to optimize the reconstruction. The results show that our SRResUNet model can effectively improve the accuracy of reconstruction with super-resolution, with good antinoise performance, with the errors of 2-, 4-, and 8-times super-resolution reconstructions of approximately 5.3, 7.4, and 9.7%, respectively. The successful demonstration of SRResUNet in this work indicates the possible applications of other deep learning methods, such as enhanced super-resolution generative adversarial networks (ESRGANs) for limited-data TDLAT.
We demonstrate a perovskite single-phototransistor visible-light spectrometer based on a deep-learning method. The size of the spectrometer is set to the scale of the phototransistor. A photoresponsivity matrix for the deep-learning system is learned from the characteristic parameters of the visible-light wavelength, gate voltage, and power densities of a commercial standard blackbody source. Unknown spectra are reconstructed using the corresponding photocurrent vectors. As a confirmatory experiment, a 532-nm laser and multipeak broadband spectrum are successfully reconstructed using our perovskite single-phototransistor spectrometer. The resolution is improved to 1 nm by increasing the number of sampling points from 80 to 400. In addition, a way to further improve the resolution is provided by increasing the number of sampling points, characteristic parameters, and training datasets. Furthermore, artificial intelligence technology may open pathways for on-chip visible-light spectroscopy.
To study the temperature distribution of the methane combustion process, the methane and air premixing model was simulated using fluent fluid simulation software, and the distribution clouds of temperature and H2O molecules were given. And the combustion region at 5 cm high was selected to study the relationship between the temperature and the mass fraction of H2O. Meanwhile, according to the principle of temperature measurement by TDLAS technology, the gas temperature was simulated in SIMULINK, the absorption line of H2O molecules was selected as the temperature measurement spectral line, and the spectral absorption model was established using the HITRAN database, and the flow chart of the simulation platform construction was given, including the light source module, gas chamber module, and data detection module. Under certain conditions, the temperature simulation data were obtained by giving 15 groups of H2O mass fractions. The results showed that the temperature measured by the TDLAS system was consistent with the temperature simulated by Fluent software, and the error range was within 5%.
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