Computer simulation of factors affecting drift from a forestry airblast sprayer
Bibliographic record
Abstract
Sidahmed, M.M. and Brown, R.B. 2002. Computer simulation of factors affecting drift from a forestry airblast sprayer. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 44: 2.272.35. Previous studies have demonstrated the adequacy of the computational fluid dynamics code (FLUENT) for simulating spray dispersal and deposition. In this study FLUENT was used to determine the effects of weather and operating parameters on the behavior of droplets and consequences on drift and deposition with an airblast forestry sprayer (Algonquin). The investigated parameters included initial droplet sizes (Do=20-300 :m), temperature (T= 293 and 301 K), relative humidity (RH= 40, 69, and 80%), airblast outlet velocity (U = 30 and 48 m/s), and droplet projection angle (2 = 0, ±22.5, and ±45). Temperature and RH had a pronounced influence on final droplet size, but had little effect on the horizontal distance traveled by droplets with Do>70 :m. Conversely, U and 2 had a significant effect on horizontal distance traveled by droplets with Do>70 :m, but had little influence on final droplet size. Maximum possible swath ranged from 15 to 21 m for the variation of parameters investigated. Low RH caused excessive evaporation and should be avoided even at low temperature. High RH and low T conditions retarded evaporation while increasing both deposition within a spray swath and particle and airborne drift. High airjet speed and abovehorizontal projection of droplets reduced deposition while increasing total particle and airborne drift. In all cases, drift would be drastically reduced by selecting nozzles that produce fewer small droplets (<110 :m) and orienting the nozzles downward (around –22.5).
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".