Cavitating Flow Pattern Characterization in Square Section Injectors by Means of CFD

Miguel G. Coussirat, Flavio H. Moll, Alfred Fontanals García

Abstract


Cavitation in pressure atomizers strongly affects the liquid/spray jet behavior at their outlets. The liquid atomization at the outlet jet is promoted when the cavitation stage is fully-developed into the atomizer nozzle. The type of atomization induced by cavitation allows developing more efficient atomizers when this cavitation stage is controlled. Cavitating flow is related to turbulent and multi-phase flows with mass transfer between the liquid and gaseous phases, and it is affected by several factors related to the physical properties of the fluid and the characteristics of the fluid flow. Due to the high-speed flow and small spatial-time scales involved, cavitation studies in injectors by experiments is very expensive. On the other hand, despite that several codes for numerical modeling of cavitating flows have been developed, this kind of flow modeling is still a big challenge, because cavitating flows should not be modeled as typical turbulent flows. There is a close relation between the cavitation inception/developing condition and the turbulence level in the flow leading to a non-standard turbulence state. When the Reynolds Averaged Simulation (RAS) of turbulence is used, previous works showed that in general its influence over the obtained results is bigger than the influence of the selected mass transfer model for a suitable cavitating flow modeling. It is also demonstrated that this selection could be competitive compared to more sophisticated options, e.g., Large Eddy Simulations (LES), when only a general flow structure and the mean/global values are required. In this work more evidence related with the capability of the RAS turbulence models are presented, and the obtained conclusions could be useful to improve injectors design by numerical modeling because the detection of the incipient/quite developed cavitation flow conditions is captured accurately using low computational resources.

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