CFD modeling of an electrostatic spraying system to optimise pesticide spray efficiency and reduced drift

Authors: HERKINS, MATTHEW - The Ohio State University; ZHAO, LINGYING - The Ohio State University; Zhu, Heping; Jeon, Hongyoung

Submitted to: Biosystems Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/24/2025
Publication Date: 1/2/2026
Citation: Herkins, M., Zhao, L., Zhu, H., Jeon, H. 2026. CFD modeling of an electrostatic spraying system to optimise pesticide spray efficiency and reduced drift. Biosystems Engineering. 263. Article 104378. https://doi.org/10.1016/j.biosystemseng.2025.104378.
DOI: https://doi.org/10.1016/j.biosystemseng.2025.104378

Interpretive Summary: Electrostatic spraying technology enhances spray efficiency by charging droplets to improve their attraction to crop canopies, reducing spray drift. However, optimizing sprayer configurations experimentally is resource-intensive and complex due to numerous influencing parameters. This study introduces a computational fluid dynamics (CFD) model as an efficient alternative to experimental optimization. The model predicts droplet charge, canopy deposition, and downwind drift for an electrostatic spraying system. Validated in a wind tunnel at wind speeds of 0 and 2.24 m/s, using five hollow-cone nozzles and a 50 mm electrode at 20 kV DC, the model showed reasonable accuracy. It predicted canopy deposition with average relative errors of 40.8% at 0 m/s and 58.8% at 2.24 m/s. For airborne drift at 2.24 m/s, it achieved an average relative error of 50.1% for deposits at heights of 0.70 m or less. These results highlight CFD modeling as a valuable tool for optimizing electrostatic sprayer configurations, improving efficiency, and minimizing drift across diverse environmental conditions and applications.

Technical Abstract: Electrostatic spraying technology charges spray droplets to increase their attraction to crop canopies to increase spray efficiency and reduce spray drift. However, determining the best configuration for an electrostatic pesticide sprayer is a complicated matter as many parameters could impact spray efficiency. Experimentally optimizing spray configurations is a resource intensive and time-consuming task. Computational modeling is an excellent alternative optimization method to overcome the disadvantages of an experimental approach. This study developed a computational fluid dynamics (CFD) model to predict the charge imparted on spray droplets and the canopy deposition and downwind drift of charged droplets. The model was validated against canopy deposition, ground deposition, and airborne drift measurement data collected in a wind tunnel at wind speeds of 0 and 2.24 m s-1 using five hollow-cone nozzles and a 50-mm diameter electrode held at an applied voltage of 20 kV DC. Results showed that the model could predict the average canopy deposition from an electrostatic nozzle at specific locations within the canopy with average relative errors of 40.8% and 58.8% at wind speeds of 0 and 2.24 m s-1, respectively. At a wind speed of 2.24 m s-1, the model was able to predict the airborne drift deposits at heights of 0.70 m or less with an average relative error of 50.1% for the validated cases. These findings demonstrate that CFD modeling is a promising method for optimizing electrostatic spraying system configurations to maximize spray efficiency and minimize drift across a range of applications with varied environmental conditions.