extrapolation of calibration curve of hot-wire spirometer using a novel neural network based approach

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%