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

Journal Article · · Applied Energy
 [1];  [2];  [3];  [4];  [2];  [2]
  1. Univ. of California, Merced, CA (United States); Shanghai Jiao Tong Univ. (China)
  2. Univ. of California, Merced, CA (United States)
  3. Technical Univ. of Denmark, Lyngby (Denmark)
  4. National Physical Laboratory (NPL), Teddington (United Kingdom

Inversion of temperature and species concentration distributions from radiometric measurements involves solving nonlinear, ill-posed and high-dimensional problems. Machine Learning methods offer solving such highly nonlinear problems, offering an alternative way to deal with complex and dynamic systems with good flexibility. Here, 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.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1581370
Journal Information:
Applied Energy, Vol. 252, Issue C; ISSN 0306-2619
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 35 works
Citation information provided by
Web of Science

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