Dynamic surface tension at liquid sheet breakup governs spray droplet size: A unified model for reduced drift and improved targeting

文献类型： 外文期刊

第一作者： Huang, Zhan

作者： Huang, Zhan;Wang, Zhichong;Wang, Changling;He, Xiongkui;Huang, Zhan;Huang, Zhan;Wang, Zhichong;Wang, Changling;He, Xiongkui;Huang, Zhan;Wang, Zhichong;Wang, Changling;He, Xiongkui;Wang, Zhichong;Wang, Changling;He, Xiongkui

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关键词： Sprays; Droplet; Adjuvants; Breakup time; PIV; Dynamic surface tension

期刊名称：ENVIRONMENTAL RESEARCH （ 影响因子：7.7； 五年影响因子：7.7 ）

ISSN： 0013-9351

年卷期： 2025 年 285 卷

页码：

收录情况： SCI

摘要： Precise control of spray droplet size is critical for optimizing pesticide application, as oversized droplets cause runoff and soil contamination, while undersized droplets lead to atmospheric drift and off-target deposition. These inefficiencies pose significant risks to environmental and human health. However, predictive models for droplet size often fail to consistently account for the effects of spray adjuvants, creating a critical gap in managing spray behavior. Existing models show significant inconsistencies between predictions for plain water and adjuvant-containing solutions, hindering reliable optimization. To resolve this, we developed a unified droplet size prediction model by introducing a more physically relevant parameter: the dynamic surface tension (DST) measured at the precise moment of liquid sheet breakup (T-LSB). We innovatively employed particle image velocimetry (PIV) to determine T-LSB for various nozzles, pressures, and adjuvant concentrations. These breakup times (0.8-3.4 ms) were then used to ascertain the corresponding DST for inclusion in the model. The resulting unified model successfully reconciled the predictive constants for water and adjuvant solutions, reducing the deviation by 89 % compared to previous fixed-time models. The new model demonstrated high predictive accuracy (R-2 > 0.99) and reduced the root mean square error of droplet size predictions by 13.0 %. This work provides a robust tool for selecting optimal adjuvant and operational parameters, ultimately enabling more targeted pesticide application. By improving prediction accuracy, our model offers a practical pathway to reduce spray drift, minimize chemical runoff, and enhance the environmental sustainability of modern agriculture.

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