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Performance of a Heller cooling tower under wind conditions has been investigated by measuring wind velocity and its direction around the tower and inlet and outlet water flow rates and temperatures. Results show that air suction at the tower prevents flow separation at its periphery. The tower front cooling sectors experience better airflow distribution compared to sectors parallel to wind direction, which improves their thermal performance by about 20% compared to still-air conditions. Airflow pattern around the tower at different distances shows that wind tangential velocity at corner sectors is four times the velocity at the reference point, decreasing air pressure and tower suction.

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Hot-wire spirometer is a kind of constant temperature anemometer (CTA). 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 CTA calibration curve will be of great practical application. In this paper, a novel approach based on the conventional neural network and self-organizing map (SOM) method has been proposed to extrapolate CTA calibration curve for measurement of velocity in the range 0.7-30 m/seconds. Results show that, using this approach for the extrapolation of the CTA calibration curve beyond its upper limit, the standard deviation is about –0.5%, which is acceptable in most cases. Moreover, 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 spirometry applications. Finally, the standard deviation on the whole measurement range (0.7-30 m/s) is about 1.5%.