Modelling of a novel hot-wire thermal flow sensor with neural nets under different operating conditions

Thermal flow sensors with a wide dynamic range are widely applied in practical fluid flow measurements to yield local velocity information and also for volume flow-rate measurements. The importance of such flow sensors inspired the authors’ investigations into wide-velocity range thermal sensors and the outcome of this work is summarized in this paper. The present novel sensor is mechanically the same as the hot-wire anemometer, but it is excited by discrete, widely separated, square waves of electrical current rather than a continuous current. The nominal output of the new sensor is a function of the time constant of the heated wire and thus also of the velocity of flow. The time constant decreases as the flow velocity increases, while the heat transfer increases. In this paper, the results obtained suggest that our measurements for flow velocity and volume flow rate are in very good agreement with the theoretical results for the present thermal flow sensor. A neural network has been trained with the output data for the flow 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 curve of the single wire thermal flow sensor under different operating temperatures. The sensor described here was developed for slowly changing unidirectional flows, and uses one wire of 12.5 μm diameter. It is excited at 30 Hz frequency and its usable flow velocity range is 0.01–25 m/s. This yields an effective operating range and corresponds to a bandwidth of 1–2500.