Experimental study of droplet behavior in a swirl flow field induced by two kinds of guiding vanes and inlet structure optimization

Shi, Shiying; Sun, Huan-qiang; Lin, Liming; Zhang, Chengfu; Zhang, Jian

doi:10.1007/s42241-021-0001-4

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Researchers have undertaken several experimental and numerical simulation investigations on nozzle construction parameters in order to achieve good hydraulic performance. Shi et al 9 used the range method and variance method to design an orthogonal experiment with the atomization cone angle and droplet diameter as test indexes and determined the best parameter combination of the nozzle atomization performance. Fang et al 10 determine which nozzle structural features have the greatest influence on air jet velocity using orthogonal testing and computational fluid dynamics (CFD) numerical modeling approaches.…”

Section: Introductionmentioning

confidence: 99%

The effect of nozzle construction factors on hydraulic performance and prediction

Jiang,

Yue,

Liu

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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To investigate the connection between nozzle jet performance and structural characteristics (contraction angle θ, outlet diameter d, ratio of straight segment to outlet diameter L2/d), impact force studies were performed on nine nozzles with varied structures using a self-developed water jet experimental platform, with target distances of 20mm, 100 mm, 200 mm, and 300 mm with jet pressures of 0.1 MPa, 0.2 MPa, and 0.3 MPa. The impact force of a nozzle water jet grows dramatically as the outlet diameter increases. When the pressure of the water jet remains constant, the impact force increases as the target distance increases. The maximum water jet impact force is 6.1 KG when the d is 11 mm. The BP neural network, the PSO and the GA-BP neural networks were utilized to forecast and assess the nozzle impact force at a target distance of 300 mm, respectively. The results reveal that, when compared to the PSO and the BP neural network, the GA-BP neural network projected values are more consistent with the measured values, with a lower average error rate and greater predictive capacity.

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Section: Introductionmentioning

confidence: 99%