Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements

Tao Ren*, Michael F. Modest, Alexander Fateev, Gavin Sutton, Weijie Zhao, Florin Rusu

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Inversion of temperature and species concentration distributions from radiometric measurements involves solving nonlinear, ill-posed and high-dimensional problems. Machine Learning approaches allow solving such highly nonlinear problems, offering an alternative way to deal with complex and dynamic systems with good flexibility. In this study, we present a machine learning approach for retrieving temperatures and species concentrations from spectral infrared emission measurements in combustion systems. The training spectra for the machine learning model were synthesized through calculations from HITEMP 2010 for gas mixtures of CO2, H2O, and CO. The method was tested for different line-of-sight temperature and concentration distributions, different gas path lengths and different spectral intervals. Experimental validation was carried out by measuring spectral emission from a Hencken flat flame burner with a Fourier-transform infrared spectrometer with different spectral resolutions. The temperature fields above the burner for combustion with equivalence ratios of ϕ = 1, ϕ = 0.8, and ϕ = 1.4 were retrieved and were in excellent agreement with temperatures deduced from Rayleigh scattering thermometry.
Original languageEnglish
Article number113448
JournalApplied Energy
Volume252
Number of pages15
ISSN0306-2619
DOIs
Publication statusPublished - 2019

Keywords

  • Chemistry
  • Concentration
  • Inverse radiation
  • Machine learning
  • Neural network
  • Temperature

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