Experimental Investigation of Icing Effects on a Hovering Drone Rotor Performance
Bibliographic record
Abstract
A scaled version of the APT70 drone rotor, typical of small to medium UAV rotors, was tested in a 9-meter-high cold chamber for a wide range of icing parameters. The drone rotor used has four blades with varying chord and twist settings. The objective of this study was to investigate icing effects on the rotor aerodynamic performance, based on experimental data, for varying rotor speeds, precipitation rates, droplet sizes and air temperatures. Aerodynamic loads were measured using the built-in load cell, and data were compared to photographs taken during testing as well as ice thickness measurements at the end of tests. The impact of each test parameter and their variations on the degradation of the rotor’s performances was evaluated. The results show that larger droplets and lower RPMs and pitch angles generate a more rapid degradation of the performances due to the airflow around the blades and tip-vortex affecting the collection efficiency of the blades. With the smaller droplets, the air temperature did not affect the performance degradation, only the type of ice accumulation. However, with the larger droplets, degradation of the performances was less severe at warmer temperatures since almost no ice accumulated at the tip and droplets were expelled before freezing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".