Computational Study of Aerial Sprays Used for Forest Protection
Bibliographic record
Abstract
The efficacy of pesticide sprays is strongly dependent on the accuracy of the droplet size spectrum. Over estimates of droplet size may result in excessive drift or ineffective doses, while under estimates of droplet size result in excessive releases. This situation is not only bad for the environment; it incurs large operating costs (spray usually accounts for 30% of total cost). This paper describes the study of droplet sprays commonly used in the agriculture and forestry management. It combines experimental wind tunnel testing and Computational Fluid Dynamic (CFD) methods to develop a fundamental understanding of droplet generation and dispersion in the wake of the atomizer spray system. The results will assist designers of spray technology and applicators in delivering pesticide to its target. The CFD models that are developed and calibrated will further allow the wind tunnel data to be generalized; thus, allowing less wind tunnel testing and eventually direct simulation of droplet dispersion in aircraft wakes. The CFD models are developed for the poly-dispersed sprays released from a Micronair AU4000 atomizer (a standard atomizer used for forest protection) at an airspeed of 67 m/s. Simulations are performed using a Lagrangian (droplet phase) - Eulerian (fluid phase) procedure and include droplet drag/body forces and turbulent dispersion of droplets. The Base-line Reynolds Stress Model (BSL RSM) turbulence model is used to compute turbulence levels in the air phase. The CFD simulations include the sprayer and a large portion of the wind tunnel geometry in order to facilitate in validation. The computational results are compared to full scale experimental measurements of pressure, gas phase velocity, droplet velocity, and droplet size spectra measured using Phase Doppler Interferometry (PDI) and Hotwire Anemometry. Measurements are available along radial lines at 0.5, 1, 2 and 4 m downstream of the atomizer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".