An investigation of airflow patterns created by high-clearance sprayers during field operations
Bibliographic record
Abstract
Field experiments and computational fluid dynamics (CFD) simulations were carried out to investigate the levels of turbulence that developed in the wake of high-clearance agricultural sprayers. Ultrasonic anemometers were mounted behind the booms of two sprayers to measure local airflow for various treatments of travel speed, lateral location along the booms, longitudinal location behind the booms, and ambient wind conditions. The primary metric used in this investigation was the turbulence kinetic energy (TKE). Significant differences were found in the TKE values for all the factors investigated. CFD modeling was performed to gain an understanding of the incremental contribution of various components of the sprayer to the TKE levels. Simulating the rotation of large agricultural tires indicated some level of turbulence in their wake suggesting they should not be ignored when assessing the wake of the sprayer. Simulation results also suggest that both the geometry of the sprayer and its travel speed influenced the airflow patterns. The investigation of the sprayer tractor with rotating tires revealed a large increase in TKE levels in the wake of the implement when increasing the travel speed from 2 to 8 m/s. The boom created an additional obstruction to the airflow and its own contribution to the levels of TKE, with localized impact where multiple members of the truss structure meet. This research established necessary baseline information and methodologies to support future efforts to better understand the impact of large sprayers operated at high speed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".