Modeling of the Response of a Hot-Wire Anemometer with Neural Nets under Various Air Densities

Abstract

The sensors, which use the convective heat transfer at hot wires in order to measure the flow rate of gases, are well known. Hot-Wire Anemometry (HWA), which is operated in either constant-current mode or in constant temperature mode, represents the most popular methods to measure the velocity and the flow rate of the fluid flow. Generally, the hot-wire sensors are calibrated against the flow velocity under atmospheric pressure conditions. To calibrate hot-wire sensors under different air densities; a special calibration test rig is needed. In the present paper, calibrations are shown to yield the same hot-wire response curves for density locations in the range of 1 to 7 kg/m3 and its usable mass flow rate range (rU) is 0.1 to 25 kg/m²s. Also, a neural network has been trained with the output data for the hot-wire sensor and tested on our measurements. It was observed that the quality of the results depends on the number of hidden neurons. The predicted values are close to the real ones which indicate the neural net model gives a good approximation for the calibration curves of the hot-wire anemometer under different flow densities. The hot-wire sensor that used in the present study has 5 mm diameter and 1.25 mm length so its aspect ratio is 250