This work presents a computational study of atomization in an air-assisted coaxial atomizer utilizing a benchmark configuration, the Sydney Needle Spray, to examine both dense and dilute spray regimes under realistic gas-phase turbulence. A geometric volume-of-fluid method, coupled with large-eddy simulation and adaptive mesh refinement, resolves the interface and surrounding gas flow with high fidelity, and both phases are validated against experiment, providing a robust basis for simulation credibility. With a reasonably good agreement in the overall trend of droplet size distribution relative to the experiment, this study further evaluates the impact of numerical tolerances, showing that the strict settings commonly used in fundamental studies do not necessarily improve accuracy in terms of characteristic droplet size for large-scale spray simulations. Furthermore, the analysis identifies radial velocity fluctuations within the liquid column as an indicator of the onset of liquid-column instability. A thinner boundary layer at the air-pipe exit intensifies interfacial instabilities, leading to enhanced radial velocity fluctuations within the liquid column and improved atomization performance. Finally, the simulations elucidate the atomization sequence: the liquid column first regularizes the core gas flow, interfacial waves then develop and shed liquid sheets, which subsequently fragment into ligaments and droplets, producing intermittent mass-flow-rate signals downstream. Frequency-spectrum analysis of the liquid mass flow rate further reveals subharmonic frequency components corresponding to the merging of successive liquid sheets. The results establish a validated, high-fidelity framework for air-assisted sprays and offer guidance for large-scale spray simulations and an accurate description of the primary breakup processes.
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Po-Han Chen
Alberto Ceschin
Mohammad Raghib Shakeel
International Journal of Spray and Combustion Dynamics
King Abdullah University of Science and Technology
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Chen et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d896166c1944d70ce0747a — DOI: https://doi.org/10.1177/17568277261433381