کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1533997 | 1512577 | 2015 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Simultaneous measurement of 2-dimensional H2O concentration and temperature distribution in premixed methane/air flame using TDLAS-based tomography technology
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی مواد
مواد الکترونیکی، نوری و مغناطیسی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
An innovative tomographic method using tunable diode laser absorption spectroscopy (TDLAS) and algebraic reconstruction technique (ART) is presented in this paper for detecting two-dimensional distribution of H2O concentration and temperature in a premixed flame. The collimated laser beam emitted from a low cost diode laser module was delicately split into 24 sub-beams passing through the flame from different angles and the acquired laser absorption signals were used to retrieve flame temperature and H2O concentration simultaneously. The efficiency of the proposed reconstruction system and the effect of measurement noise were numerically evaluated. The temperature and H2O concentration in flat methane/air premixed flames under three different equivalence ratios were experimentally measured and reconstruction results were compared with model calculations. Numerical assessments indicate that the TDLAS tomographic system is capable for temperature and H2O concentration profiles detecting even the noise strength reaches 3% of absorption signal. Experimental results under different combustion conditions are well demonstrated along the vertical direction and the distribution profiles are in good agreement with model calculation. The proposed method exhibits great potential for 2-D or 3-D combustion diagnostics including non-uniform flames.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Optics Communications - Volume 346, 1 July 2015, Pages 53-63
Journal: Optics Communications - Volume 346, 1 July 2015, Pages 53-63
نویسندگان
Fei Wang, Qi Wu, Qunxing Huang, Haidan Zhang, Jianhua Yan, Kefa Cen,