Liquid flow and breakage behaviors of two liquid jets impacting on the wire mesh with different impinging angles

Sun, Zhong-Bo; Liao, Haiyan; Wang, Lihua; Jiang, Lan; Zhang, Xin; Wang, Baoju; Chu, Guang‐Wen; Luo, Yong

doi:10.1016/j.cej.2022.140036

Cited by 8 publications

(2 citation statements)

References 41 publications

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“…In addition, superhydrophobic surfaces are more likely to cause droplet penetration or rebound as a whole than ordinary surfaces. Sun et al. (2023) used high-speed cameras to study the liquid flow and rupture behavior of two liquid jets after impact on a stainless steel mesh.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, superhydrophobic surfaces are more likely to cause droplet penetration or rebound as a whole than ordinary surfaces. Sun et al (2023) used high-speed cameras to study the liquid flow and rupture behavior of two liquid jets after impact on a stainless steel mesh. Kooij et al (2019) investigated the effects of droplet properties and mesh impact velocity on the maximum spreading diameter and water droplet penetration mass after droplet impact on the mesh using experiments and simulation.…”

Section: Introductionmentioning

confidence: 99%

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Research on a UAV spray system combined with grid atomized droplets

Xue,

Tian,

Yang

et al. 2024

Front. Plant Sci.

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BackgroundsUAVs for crop protection hold significant potential for application in mountainous orchard areas in China. However, certain issues pertaining to UAV spraying need to be addressed for further technological advancement, aimed at enhancing crop protection efficiency and reducing pesticide usage. These challenges include the potential for droplet drift, limited capacity for pesticide solution. Consequently, efforts are required to overcome these limitations and optimize UAV spraying technology.MethodsIn order to balance high deposition and low drift in plant protection UAV spraying, this study proposes a plant protection UAV spraying method. In order to study the operational effects of this spraying method, this study conducted a UAV spray and grid impact test to investigate the effects of different operational parameters on droplet deposition and drift. Meanwhile, a spray model was constructed using machine learning techniques to predict the spraying effect of this method.Results and discussionThis study investigated the droplet deposition rate and downwind drift rate on three types of citrus trees: traditional densely planted trees, dwarf trees, and hedged trees, considering different particle sizes and UAV flight altitudes. Analyzing the effect of increasing the grid on droplet coverage and deposition density for different tree forms. The findings demonstrated a significantly improved droplet deposition rate on dwarf and hedged citrus trees compared to traditional densely planted trees and adopting a fixed-height grid increased droplet coverage and deposition density for both the densely planted and trellised citrus trees, but had the opposite effect on dwarfed citrus trees. When using the grid system. Among the factors examined, the height of the sampling point exhibited the greatest influence on the droplet deposition rate, whereas UAV flight height and droplet particle size had no significant impact. The distance in relation to wind direction had the most substantial effect on droplet drift rate. In terms of predicting droplet drift rate, the BP neural network performed inadequately with a coefficient of determination of 0.88. Conversely, REGRESS, ELM, and RBFNN yielded similar and notably superior results with a coefficient of determination greater than 0.95. Notably, ELM demonstrated the smallest root mean square error.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%