Mean Droplet Size Prediction of Twin Swirl Airblast Nozzle at Elevated Operating Conditions

Jiaming Miao, Bo Wang (), Guangming Ren and Xiaohua Gan
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Jiaming Miao: Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Bo Wang: Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Guangming Ren: Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Xiaohua Gan: Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, China

Energies, 2024, vol. 17, issue 20, 1-18

Abstract: This study introduces a novel predictive model for atomization droplet size, developed using comprehensive data collected under elevated temperature and pressure conditions using a twin swirl airblast nozzle. The model, grounded in flow instability theory, has been meticulously parameterized using the Particle Swarm Optimization (PSO) algorithm. Through rigorous analysis, including analysis of variance (ANOVA), the model has demonstrated robust reliability and precision, with a maximum relative error of 19.3% and an average relative error of 6.8%. Compared to the classical atomization model by Rizkalla and Lefebvre, this model leverages theoretical insights and incorporates a range of interacting variables, enhancing its applicability and accuracy. Spearman correlation analysis reveals that air pressure and the air pressure drop ratio significantly negatively impact droplet size, whereas the fuel–air ratio (FAR) shows a positive correlation. Experimental validation at ambient conditions shows that the model is applicable with a reliability threshold of We 1 / Re 1 ≥ 0.13 and highlights the predominance of the pressure swirl mechanism over aerodynamic atomization at higher fuel flow rates ( q > 1.25 kg/h). This research effectively bridges theoretical and practical perspectives, offering critical insights for the optimization of airblast nozzle design.

Keywords: twin swirl airblast nozzle; Sauter mean diameter; atomization; prediction model; elevated conditions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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