ISSN:
1433-3058
Keywords:
Neural networks
;
CARS
;
Spectroscopy
;
Combustors
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mathematics
Notes:
Abstract A neural network trained with clustered data has been applied to the extraction of temperature from vibrational Coherent Anti-Stokes Raman (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 aeroengine 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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01424230
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