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Evaluation of Noisy Coherent Anti-Stokes Raman Spectra by Evolutionary Algorithms

  • U. Linnemann
  • P. Roosen
  • H.-J. Koß
Part of the Heat and Mass Transfer book series (HMT)

Abstract

Coherent Anti-Stokes Raman Spectroscopy (CARS) is frequently the method of choice for non-intrusive temperature measurements in combustion systems. The temperature determination requires a comparison of measured spectra with theoretically calculated ones. Conventional gradient-based least squares fitting of experimental data with a library of theoretical spectra usually leads to sensible results, as long as the spectrum shapes behave well and the noise level is low.

The investigation of a 1 MW coal dust burner yielded only very noisy data that showed the limits of applicability of the gradient-based approach. Therefore in this paper a novel fitting approach based on evolutionary algorithms for such spectra is presented. The applied algorithm is explained. Temperature evaluation results, both conventionally and evolutionarily determined, are given.

Keywords

Theoretical Spectrum Lorentz Width Line Width Parameter Coal Dust Burner Spontaneous Raman Spectroscopy 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • U. Linnemann
    • 1
  • P. Roosen
    • 1
  • H.-J. Koß
    • 1
  1. 1.RWTH AachenLehrstuhl für Technische ThermodynamikAachenGermany

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