Experimental study on the release of droplets via bubble bursting under different liquid phase conditions

Chen, Hao; Gu, Haifeng; Lv, Huanwen; Yu, Xiang; Wang, Junlong; Luo, Hanyu

doi:10.1016/j.pnucene.2020.103617

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…In recent years, some researchers have studied the droplets generated after the bubble burst under different experimental conditions [28][29][30]. From their research, the bubblebursting atomisation method possesses very high potential in generating NAs and applicability to MQL machining.…”

Section: Introductionmentioning

confidence: 99%

Optimisation on the Performance of Bubble-Bursting Atomisation for Minimum Quantity Lubrication with Vegetable Oil Using Computational Fluid Dynamics Simulation

2022

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Sustainable and green machining technologies have become a welcomed topic in the manufacturing industries. One of the emerging sustainable technologies is minimum quantity lubrication (MQL). In this study, the optimisation and study of the bubble-bursting atomisation system applied to MQL machining is carried out through the computational fluid dynamics (CFD) simulation approach. Vegetable oil is selected as the cooling lubricant in this study. The performance of the bubble-bursting atomisation system is improved by alternating air inlet velocity and the gap distance between the inlets of bubble production. A velocity of 0.1 ms−1 is suitable for the air at the inlets for the bubble production, whereas 10 ms−1 is suitable for the velocity of the air at the inlet, where the droplets of vegetable oil are guided to the nozzle. Besides that, a 50 mm gap distance between the air inlets for the production of bubbles is able to avoid the occurrence of bubble coalescence. Under these conditions, optimal bubble sizes of 2–3 mm can be achieved, with a higher probability of nano-sized droplets being present in these ranges. Furthermore, a higher rate and smaller size of vegetable oil droplets escaping the atomisation chamber and reaching the machining zone will be generated. Thus, the performance of the MQL machining can be improved.

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Section: Introductionmentioning

confidence: 99%