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Applied Physics B

, 123:83 | Cite as

Non-uniform temperature and species concentration measurements in a laminar flame using multi-band infrared absorption spectroscopy

  • Liu Hao Ma
  • Lok Yin Lau
  • Wei RenEmail author
Article

Abstract

We report in situ measurements of non-uniform temperature, H2O and CO2 concentration distributions in a premixed methane–air laminar flame using tunable diode laser absorption spectroscopy (TDLAS). A mid-infrared, continuous-wave, room-temperature interband cascade laser (ICL) at 4183 nm was used for the sensitive detection of CO2 at high temperature.The H2O absorption lines were exploited by one distributed feedback (DFB) diode laser at 1343 nm and one ICL at 2482 nm to achieve multi-band absorption measurements with high species concentration sensitivity, high temperature sensitivity, and immunity to variations in ambient conditions. A novel profile-fitting function was proposed to characterize the non-uniform temperature and species concentrations along the line-of-sight in the flame by detecting six absorption lines of CO2 and H2O simultaneously. The flame temperature distribution was measured at different heights above the burner (5–20 mm), and compared with the thermocouple measurement with heat-transfer correction. Our TDLAS measured temperature of the central flame was in excellent agreement (<1.5% difference) with the thermocouple data.The TDLAS results were also compared with the CFD simulations using a detailed chemical kinetics mechanism (GRI 3.0) and considering the heat loss to the surroundings.The current CFD simulation overpredicted the flame temperature in the gradient region, but was in excellent agreement with the measured temperature and species concentration in the core of the flame.

Keywords

Flame Temperature Laminar Flame Thermocouple Measurement Tunable Diode Laser Absorption Spectroscopy Multiplicative Algebraic Reconstruction Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research is supported by National Natural Science Foundation of China (NSFC) (11502222) and CUHK Direct Grant for Research.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Mechanical and Automation EngineeringThe Chinese University of Hong KongNew TerritoriesHong Kong
  2. 2.Shenzhen Research Institute, The Chinese University of Hong KongNew TerritoriesHong Kong

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