A Lagrangian framework is proposed to address liquid film and atomization modeling in large-eddy simulations (LESs) of aeronautical air-blast injectors. The Lagrangian liquid film model of O’Rourke and Amsden (“A Spray/Wall Interaction Submodel for the KIVA-3 Wall Film Model,” SAE International TP 2000-01-0271, Warrendale, PA, 2000) is improved by introducing a subgrid contact angle to better predict the film height and the resulting film dynamics. Next, the phenomenological the primary atomization model for prefilming air-blast injectors (PAMELA) proposed by Chaussonnet et al. (“A New Phenomenological Model to Predict Drop Size Distribution in Large-Eddy Simulations of Airblast Atomizers,” International Journal of Multiphase Flow, Vol. 80, April 2016, pp. 29–42) for primary atomization at the prefilmer edge is enhanced to deal with complex geometries. This model is able to predict the droplet-size probability density function from the prefilmer height and flow conditions. The original formulation relied on correlations valid for flat plates to determine the gas boundary-layer thickness, and it required a gas velocity at film height to be set by the user. These two points make its use difficult for complex configurations where there is no simple correlation for the gas boundary-layer thickness and the gas velocity at film height cannot be a priori estimated. An embedded methodology, named the automatic PAMELA, is therefore proposed in this work to automatically determine these two quantities in the simulation. For each cell of the prefilmer edge where atomization occurs, the gas boundary-layer thickness is estimated by analyzing the local velocity profile thanks to Lagrangian probes; and the gas velocity is computed from the local film height by assuming a logarithmic velocity profile. Finally, the film and primary atomization models are coupled to a secondary atomization model, and they are assessed on an industrial air-blast aeronautical injector. The average droplet velocity profiles and Sauter mean diameters are compared against experimental phase Doppler particle analyzer measurements, and they demonstrate the ability of the proposed framework to perform Lagrangian LESs of liquid injection in complex geometries.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.