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• W4377018108 abstract "The variation of relevant parameters during the spraying process will affect the droplet deposition behavior (DDB) on the plant leaf surface. Hence, a comprehensive analysis and modeling are the basis for model-based control design and performance optimization of spraying efficiency. This paper adopted the method of computational fluid dynamics (CFD) to conduct real-time visualization analysis and numerical modeling establishment of the DDB from the micro and macro perspectives, aiming to obtain the quantitative deposition laws of relevant parameters in the deposition process. The micro results showed that the droplet had different degrees of deposition, bouncing, and diffusion at different blade inclination angles (IAs) and contact angles (CAs). The system’s total pressure presented a consistent trend, while there was a sharp increase of about 2–3 times at the moment of impact on the leaf surface. Numerically, the droplet diffusion speed was basically at 5–7 mm/ms. And it presented an exponential function between the diffusion time and the diffusion velocity of the droplets, whose coefficient of determination (R2) equaled 0.73. The DDB of macro analysis clearly explained the change laws of droplet size, residence time, shear stress, and spreading speed. And the mathematical models of the droplet deposition thickness (DDT) on the target surface about time, spraying speed, liquid density, and fluid viscosity were established, which showed a trend of string functions. The growth rate of DDT reached a maximum of 13331% at 40 ms, which could be considered as an economic spraying time. The study achieved a comprehensive dynamic visualization and model-building of relevant spraying parameters and provided an actual reference for the spraying deposition control design and the parameters selection." @default.
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• W4377018108 date "2023-07-01" @default.
• W4377018108 modified "2023-10-15" @default.
• W4377018108 title "Modeling and analysis of droplet deposition behavior: From the micro and macro perspectives" @default.
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• W4377018108 doi "https://doi.org/10.1016/j.compag.2023.107896" @default.
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