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The performance characteristics and the near wake of a model wind turbine were investigated experimentally. The model tested is a three-bladed horizontal axis type wind turbine with an upstream rotor of 0.90 m diameter. The performance measurements were conducted at various yaw angles, a freestream speed of about 10 m s 1 , and the tip speed ratio was varied from 0.5 to 12. The time-averaged streamwise velocity field in the near wake of the turbine was measured at different tip speed ratios and downstream locations. As expected, it was found that power and thrust coefficients decrease with increasing yaw angle. The power loss is about 3% when the yaw angle is less than 10 ı and increases to more than 30% when the yaw angle is greater than 30 ı . The velocity distribution in the near wake was found to be strongly influenced by the tip speed ratio and the yaw angle. At the optimum tip speed ratio, the axial velocity was almost uniform within the midsection of the rotor wake, whereas two strong peaks are observed for high tip speed ratios when the yaw angle is 0 ı . As the yaw angle increases, the wake width was found to be reduced and skewed towards the yawed direction. With increasing downstream distance, the wake velocity field was observed to depend on the tip speed ratio and more pronounced at high tip speed ratio.

Bubble formation phenomena in a two-phase gas/liquid system occur in many industries that involve boiling; such as desalination stations, nuclear reactors, chemical plants, and fluid piping transportation and processes. Bubble formation phenomena cause problems, such as a decrease in equipment efficiency, vibration, noise, and solid surface erosion. Applications of the acoustic emission (AE) technique for monitoring bubble formation and burst stages in boiling processes are marginal in terms of extension in comparison to other applications of the AE technique. The use of the AE technique in this experimental investigation covers the frequency range between 100 and 1000 kHz, showing that the AE sensor can detect acoustic emissions from an occurrence of bubble formation. Statistically, it was found that the best AE parameter indicator for bubble formation was AE-RMS (root mean square).

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