Usable work of macro-scale cavities in liquids

It is shown that the generation of cavities in a liquid can produce usable work, which is illustrated by the stretching of a string. This work is done during the expansion of the cavity, and not with its collapse. Basic equations are presented for the movement of a device moved by the so called cavity events. A theoretical solution is also proposed, which uses polynomial functions relating the so called "excess of pressure" in the cavity and time. Evaluations of the force generated during the expansion of the cavity showed a mean peak value of about 58 N for the moving container, while measurements with the container fixed to a support showed a peak value of 476 N, considered somewhat overestimated, because high frequency oscillations seem to superpose the mean behavior. Simultaneous phenomena occurring during the cavity events are also described. Series of pictures of the experiments are presented.

Two-Phase Frictional Pressure Drop and Flow Boiling Heat Transfer for R245fa in a 2.32-mm Tube

Experimental two-phase frictional pressure drop and flow boiling heat transfer results are presented for a horizontal 2.32-mm ID stainless-steel tube using R245fa as working fluid. The frictional pressure drop data was obtained under adiabatic and diabatic conditions. Experiments were performed for mass velocities ranging from 100 to 700 kg m−2 s−1 , heat flux from 0 to 55 kW m−2 , exit saturation temperatures of 31 and 41◦C, and vapor qualities from 0.10 to 0.99. Pressures drop gradients and heat transfer coefficients ranging from 1 to 70 kPa m−1 and from 1 to 7 kW m−2 K−1 were measured. It was found that the heat transfer coefficient is a strong function of the heat flux, mass velocity, and vapor quality. Five frictional pressure drop predictive methods were compared against the experimental database. The Cioncolini et al. (2009) method was found to work the best. Six flow boiling heat transfer predictive methods were also compared against the present database. Liu and Winterton (1991), Zhang et al. (2004), and Saitoh et al. (2007) were ranked as the best methods. They predicted the experimental flow boiling heat transfer data with an average error around 19%.