Predicting drift from field spraying by means of a 3D computational fluid dynamics model

Predicting drift from field spraying by means of a 3D computational fluid dynamics model In order to investigate and understand drift from field sprayers, a steady state computational fluid dynamics (CFD) model was developed. The model was developed in 3D in order to increase the understanding of the causes of drift: a deviation in the wind direction cannot be captured by a 2D approach, the wake behind a wind screen is not symmetrical, the effects of a changed nozzle orientation may not be symmetrical. The model's accuracy was validated with field experiments carried out according to the international standard ISO 22866. A field sprayer with a spray boom width of 27 m and 54 nozzles (Hardi ISO F110-03 at 3 bar) was driving at 2.22 m/s over a flat pasture. During the experiments the wind direction was perpendicular to the tractor track. The model explained the variation in drift replicates during each single field experiment through varying boom height (0.3–0.7 m), wind velocity (1.3–2.5 m/s), wind deviation (−18° to +18°) from the direction perpendicular to the tractor track and injection velocity of the droplets (17–27 m/s). Boom movements had the highest impact on the variations in drift values (deviations in drift deposits of 25%), followed by variation in wind velocity (deviations in drift deposits of 3%) and injection velocity of the droplets (deviations in drift deposits of 2.5%). Wind deviation from the direction perpendicular to the tractor track had a reducing effect on the drift values (deviations in drift deposits of 2%). Small variations in driving speed had little influence on drift values. Near drift (<5 m) is predicted well by the model but the increased complexity compromised the predictions at greater distances. The model will be further developed in order to improve far drift prediction. Dynamic simulations will be performed and the model for turbulent dispersion will be optimized. The model did not require calibration. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computers and Electronics in Agriculture Elsevier

Predicting drift from field spraying by means of a 3D computational fluid dynamics model

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Publisher
Elsevier
Copyright
Copyright © 2007 Elsevier B.V.
ISSN
0168-1699
eISSN
1872-7107
D.O.I.
10.1016/j.compag.2007.01.009
Publisher site
See Article on Publisher Site

Abstract

In order to investigate and understand drift from field sprayers, a steady state computational fluid dynamics (CFD) model was developed. The model was developed in 3D in order to increase the understanding of the causes of drift: a deviation in the wind direction cannot be captured by a 2D approach, the wake behind a wind screen is not symmetrical, the effects of a changed nozzle orientation may not be symmetrical. The model's accuracy was validated with field experiments carried out according to the international standard ISO 22866. A field sprayer with a spray boom width of 27 m and 54 nozzles (Hardi ISO F110-03 at 3 bar) was driving at 2.22 m/s over a flat pasture. During the experiments the wind direction was perpendicular to the tractor track. The model explained the variation in drift replicates during each single field experiment through varying boom height (0.3–0.7 m), wind velocity (1.3–2.5 m/s), wind deviation (−18° to +18°) from the direction perpendicular to the tractor track and injection velocity of the droplets (17–27 m/s). Boom movements had the highest impact on the variations in drift values (deviations in drift deposits of 25%), followed by variation in wind velocity (deviations in drift deposits of 3%) and injection velocity of the droplets (deviations in drift deposits of 2.5%). Wind deviation from the direction perpendicular to the tractor track had a reducing effect on the drift values (deviations in drift deposits of 2%). Small variations in driving speed had little influence on drift values. Near drift (<5 m) is predicted well by the model but the increased complexity compromised the predictions at greater distances. The model will be further developed in order to improve far drift prediction. Dynamic simulations will be performed and the model for turbulent dispersion will be optimized. The model did not require calibration.

Journal

Computers and Electronics in AgricultureElsevier

Published: Apr 1, 2007

References

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