Miao, J.; Wang, B.; Ren, G.; Gan, X. Mean Droplet Size Prediction of Twin Swirl Airblast Nozzle at Elevated Operating Conditions. Preprints2024, 2024091498. https://doi.org/10.20944/preprints202409.1498.v1
APA Style
Miao, J., Wang, B., Ren, G., & Gan, X. (2024). Mean Droplet Size Prediction of Twin Swirl Airblast Nozzle at Elevated Operating Conditions. Preprints. https://doi.org/10.20944/preprints202409.1498.v1
Chicago/Turabian Style
Miao, J., Guangming Ren and Xiaohua Gan. 2024 "Mean Droplet Size Prediction of Twin Swirl Airblast Nozzle at Elevated Operating Conditions" Preprints. https://doi.org/10.20944/preprints202409.1498.v1
Abstract
This study introduces a novel predictive model for atomization droplet size, developed through 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 ≥0.13 and highlight 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
