Temperature analysis of coherent anti-stokes raman spectra using a neural network approach

H.J.L. Van Der Steen, J.D. Black

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Abstract

A neural network trained with clustered data has been applied to the extraction of temperature from vibrational Coherent Anti-Stokes Roman (CARS) spectra of nitrogen. CARS is a non-intrusive thermometry technique applied in practical combustors in industry. The advantages of clustering of training data over training with unprocessed calculated spectra is described. The method is applied to CARS data from an isothermal furnace and a liquid kerosene fuelled aero-engine combustor sector rig. Resulting temperatures have been compared with values extracted from the data using conventional least squares fitting and, where possible, mean temperatures measured by pyrometer and blackbody cavity probe. The main advantage of the neural network method is speed, with the potential for online temperature extraction at the spectral acquisition rate of 10 Hz using standard PC hardware. © 1997 Springer-Verlag London Limited.
Original languageEnglish
Pages (from-to)248 - 257
Number of pages10
JournalNeural Computing and Applications
Volume5
Issue number4
DOIs
Publication statusPublished - 1997
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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