Hot wire probe calibration using artificial neural network

Abstract

Hot wire anemometers (HWAs) are used as research tools in fluid mechanics. In HWA measurements, a hot wire probe calibration stage must be carried out before the experiments, in order to determine the relation between the probe voltage and fluid velocity at the nozzle exit, because of variation in the ambient conditions. In this study, a hot wire probe calibration system is used to calibrate a one-dimensional hot wire probe by means of an AN-1005 HWA, which operates as a constant temperature anemometer (CTA). Experimental results have been used to train an artificial neural network (ANN) in order to produce a new calibration curve for new conditions, because calibration is time consuming and difficult. The network has yielded an R² value of 0.999, and very small root-mean-squared values, which indicate that predicted values are close to the experimental ones, and the ANN model gives a good approximation for the calibration curve under different conditions. In addition, formulations have been prepared according to a hidden number of neurons studied. Since the necessary formulae have been given, anyone could use these variables to obtain predictions from the ANN as if the hot wire probe has been operated.