Experimental study of an ultrasonic anemometer for drone-based measurements
Bibliographic record
Abstract
Wind flow patterns around building features are crucial for the safe operation of drones and urban air mobility missions. Fixed anemometer stations and computational fluid dynamics simulations face limitations in measuring airflow behind buildings. The use of a drone-mounted ultrasonic anemometer addresses these limitations by providing high-resolution measurements, flexibility in positioning, and the ability to capture real-time localized wind patterns in complex urban environments. This paper presents a wind tunnel study using full factorial design of experiment on a low-cost drone-mountable ultrasonic anemometer to be used to measure urban wind fields. Measurements under uniform flow conditions, with reference instruments, validate the anemometer's performance in airspeed and direction measurements. The study also explores the anemometer's capabilities in measuring wake characteristics behind a cylinder in turbulent flow. The results demonstrate that using compact ultrasonic anemometers for drone-based anemometry in urban environments can yield adequate measurements within specific angle of attack ranges.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".