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Calibration of Triple-Wire Probes Using an Artificial Neural Network

[+] Author Affiliations
Noorallah Rostamy, Soheil Akbari, David Sumner, Donald J. Bergstrom

University of Saskatchewan, Saskatoon, SK, Canada

Paper No. FEDSM-ICNMM2010-31198, pp. 217-224; 8 pages
  • ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels
  • ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting: Volume 2, Fora
  • Montreal, Quebec, Canada, August 1–5, 2010
  • Conference Sponsors: Fluids Engineering Division
  • ISBN: 978-0-7918-4949-1 | eISBN: 978-0-7918-3880-8
  • Copyright © 2010 by ASME


Hot-wire anemometry is an established technique for velocity measurements in turbulent flows. Calibration of hot-wire probes is challenging due to the nonlinear relationship between the probe output voltage and the velocity, and the sensitivity to the temperature difference between the heated wire and the ambient flow. A triple-wire probe contains three mutually orthogonal wires that permit the three components of the local instantaneous velocity vector to be measured simultaneously. Calibration data reduction methods for multi-wire probes, based on variable-angle calibration techniques, may include curve-fits and direct-interpolation schemes. In the present study, a novel calibration data reduction method for a triple-wire probe is reported which uses an artificial neural network. Such a method has been successfully applied by other researchers for the calibration of seven-hole pressure probes. For the triple-wire probe, the neural network is used to produce a calibration relation between the three probe output voltages and the three components of the local velocity vector. Variable-angle calibration data were obtained for a triple-wire probe for velocity magnitudes from 5 to 40 m/s, yaw angles from −35° to +35° , and roll angles from 0° to 345° . A three-layer perceptron feed-forward network, using a Levenberg-Marquardt training algorithm, was applied to the calibration data, to map the mean voltages to the mean velocity components. The network was tested using an independent data set. The present results yielded standard errors of approximately ±0.38 m/s, ±0.25 m/s and ±0.26 m/s in the magnitudes of the streamwise, vertical, and cross-flow velocity components, respectively. The results showed that the present neural network model is not significantly sensitive to the size of the calibration data set, suggesting it may be a more convenient calibration data reduction method compared to other methods.

Copyright © 2010 by ASME



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