Extrapolation of Calibration Curve of Hot-Wire Spirometer Using a Novel Neural Network Based Approach

mohammad ali ardekani, vahid reza nafisi, foad farhani

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Abstract


Hot-wire spirometer uses a constant temperature anemometer (CTA) for its operation. The working principle of CTA, used for the measurement of fluid velocity and flow turbulence, is based on convective heat transfer from a hot-wire sensor to a fluid being measured. The calibration curve of a CTA is nonlinear and cannot be easily extrapolated beyond its calibration range. Therefore, a method for extrapolation of calibration curve will be of great practical application. In this paper, a novel approach based on the conventional neural network method has been proposed to extrapolate CTA calibration curve for measurement range of 0.7-30 m/sec. Results show that using this approach, the standard deviation for the extrapolation of the CTA calibration curve beyond its upper limit is about -0.5%, which is acceptable in most cases. Moreover, the application of this approach for the extrapolation of the CTA calibration curve below its lower limit produces standard deviation of about 4.5%, which is acceptable in spirometery applications. Finally the standard deviation on the whole measurement range (0.7-30 m/s) is about 1.5%

Keywords


Hot-wire Spirometer; Constant temperature anemometer (CTA); Curve fitting; Neural network method; Self organize map (SOM)

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