Abstract
By means of the heat and mass transfer analysis based on the new similarity analysis method, it is found that only the wall temperature gradient and mass flow rate parameter are no-given variables respectively, for prediction of heat and mass transfer of the film boiling. The wall temperature gradient is proportional to heat transfer, and will decrease with increasing the wall superheated grade, and increase with increasing the bulk subcooled grade. Additionally, the wall temperature gradient is steeper with higher liquid bulk subcooled grade and with lower wall superheated grade. The curve-fit equation for evaluation of the wall temperature gradient provided in this chapter agrees very well with the related rigorous numerical solutions, and useful for a reliable prediction of heat transfer of the laminar film boiling of water. From the numerical results, it is seen that vapor film thickness will increase with increasing wall superheated grade or with decreasing the water bulk subcooled grade, and in the iterative calculation it is a key work to correctly determine the suitable value. The solutions of the governing equations are converged in very rigorous values of vapor film thickness. The interfacial velocity component will increase with increasing the wall superheated grade except the case for very low liquid bulk subcooled grade, and will decrease with increasing the liquid bulk subcooled grade. The boiling mass flow rate is proportional to the induced mass flow rate parameter. The mass flow rate parameter will increase with increasing the wall superheated grade, decrease obviously with increasing the liquid subcooled grade, and decrease slower and slower with increasing the liquid subcooled grade. The mass flow rate parameter is formulated according to the numerical solutions, and then, prediction equation for boiling mass transfer is created for reliable evaluation.KeywordsMass Flow RateWater BulkLocal Nusselt NumberAverage Heat Transfer CoefficientLaminar FilmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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