A Lagrangian stochastic model for aerial spray transport above an oak forest

Wang, Yansen; Miller, David R.; Anderson, Dean E.; McManus, Michael L.

doi:10.1016/0168-1923(95)02221-i

Cited by 10 publications

(3 citation statements)

References 34 publications

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“…The ''Langevin class'' heavy particle trajectory model used in many of the preceding simulations is not new, having been used by Wilson et al (1981) to simulate bead dispersion experiments, and more recently by Wang et al (1995) in the treatment of aerial sprays. Current findings confirm the usefulness of this simple scheme for the calculation of an ensemble of heavy particle trajectories released from ''high'' sources (large h/z 0 ) in the unperturbed atmospheric surface layer.…”

Section: Discussionmentioning

confidence: 99%

“…Thomson's well-mixed constraint (wmc); results of MacInnes and Bracco (1992), who examined several models not satisfying the wmc, usefully illustrate the seriousness of errors that can result. To give a few examples of these recent Langevin treatments of spray dispersion, Wang et al (1995) adopted Thomson's twodimensional well-mixed tracer dispersion model for a Gaussian velocity pdf [Eqs. 7], and used Eq.…”

Section: E Langevin Class Of Particle Trajectory Modelsmentioning

confidence: 99%

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Trajectory Models for Heavy Particles in Atmospheric Turbulence: Comparison with Observations

Wilson¹

2000

J. Appl. Meteor.

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The simplest ''random flight'' models for the paths of heavy particles in turbulence have been tested against previous observations of the deposition of glass beads from an elevated source in the atmospheric surface layer. For the bead sizes examined (diameter 50-100 m), for which the ratio of particle inertial timescale to turbulence timescale p /⌫ L K 1, it was found sufficient to adapt, as others earlier have done, a well-mixed first-order Lagrangian stochastic (''Langevin'') model of fluid element trajectories, simply by superposing a gravitational settling velocity w g and reducing the velocity autocorrelation timescale along the heavy particle trajectory (⌫ p) relative to the fluid-Lagrangian timescale (⌫ L). That is to say, unless details of the particle distribution very close to ground (where p /⌫ L is not small) are of interest, no advantage other than conceptual clarity can be found in the more faithful approach of explicitly modeling particle acceleration by means of the particle equation of motion. With the timescale reduction parameter ␤ ϳ 2, the Langevin model estimated the location and width of the bead deposit swath very well and fixed the peak deposit density to within (at worst) about 100% error (in very stable stratification), but more generally to within about 20%. In the case where trajectories intersected a tall crop canopy, uncertainties in the treatment of deposition proved more significant than nuances of the trajectory algorithm.

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Section: Discussionmentioning

confidence: 99%

Section: E Langevin Class Of Particle Trajectory Modelsmentioning

confidence: 99%

Trajectory Models for Heavy Particles in Atmospheric Turbulence: Comparison with Observations

Wilson¹

2000

J. Appl. Meteor.

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“…Deposition is optimized when the probability of a droplet to collide with the target is maximal considering the whole droplet size and velocity distributions. Physical transport of 10 droplets (Wang et al, 1995;Walklate, 1987) and spray drift potential (Holterman et al, 1997;Lebeau et al, 2011;Teske et al, 2002) have been investigated and modeled intensively based on spray characteristics (droplet size and velocity distributions) and environmental conditions (release height, meteorological conditions, etc.) to improve deposition.…”

Section: Introductionmentioning

confidence: 99%

Measurements of reference ISO nozzles by high-speed imaging

Cock

Massinon

Nuyttens³

et al. 2016

Crop Protection

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Agricultural spray characteristics determine the efficiency of a pesticide application because size and velocity affect droplet trajectory and impact behavior.At present, the relevance of different characterization techniques remains controversial since discrepancies may be significant between measurements performed in different laboratories.A digital image acquisition technique and analysis algorithm is proposed for droplet size and velocimetry measurements as an alternative to well-established techniques such as the Phase Doppler Particle Analyzer (PDPA) or laser diffraction spectrometry (LDS). The algorithm requires double exposed shadow images acquired in a back-lighted arrangement with a Particle Image Velocimetry (PIV) camera and a pulsed light emitting diode (LED). Spatial illumination heterogeneities are corrected by subtracting from each image a mean background acquired on several images without any particle. The algorithm accuracy is ensured by the rejection of out-of-focus particles using a focus parameter depending on gradient intensity at the particle edges. Thresholds for focus particle selection were determined by studying the evolution of the focus parameter and the error on particle size measurements from images containing droplets with * Corresponding author

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