| 光谱学与光谱分析 |
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| Reconstruction Research for Gas Concentration and Temperature of Flame Based on Algebraic Reconstruction Technique |
| XIA Hui-hui, LIU Jian-guo, XU Zhen-yu, KAN Rui-feng, HE Ya-bai, ZHANG Guang-le, CHEN Jiu-ying |
| Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China |
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Abstract We specify water vapor among combustion products as the target gas based on tunable diode absorption spectroscopy in this paper. The direct absorption signals of water vapor after being processed can be used to calculate the gas concentration distributions and temperature distributions of the combustion region of methane and air flat flame furnace via algebraic reconstruction technique (ART). In the numerical simulation, reconstruction region is a grid of five by five, we assume a temperature and water vapor concentration distribution of 25 grid, then simulate different direction laser rays which cross the combustion region, generating projection of each ray, by ART reconstruction algorithm, it turns out that the temperature and water vapor distribution reconstruction error is less than 1%. In the experiment, we chose a distributed-feedback laser to scan the target gas H2O 7 153.722，7 153.748 and 7 154.354 cm-1 as absorbtion line pair to measure temperature of the flame，we consider the former two line as one absorbtion line. By Stages multi-directional scanning, the authors abtain 16 different regions distributin of temperature and gas concentration of furnace when we collecte 30 different angle data by spectral data processing, reconstruction algorithm, two absorbtion line ratio method for temperature sensing, finding the temperature and water concentration are higher in the center than in the edge, it turns out that the reconstruction algorithm is good enough to achieve the distributions of gas concentration and temperature of the combustion region.
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Received: 2014-07-03
Accepted: 2014-11-15
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Corresponding Authors:
XIA Hui-hui
E-mail: hhxia@aiofm.ac.cn
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