An Experimental Apparatus for Icing Tests of Low Altitude Hovering Drones
Bibliographic record
Abstract
The icing facilities of the Anti-Icing Materials International Laboratory AMIL have been adapted to reproduce icing conditions on a Bell APT70 drone rotor, typical of small-to-medium UAV models. As part of an extensive icing test campaign, this paper presents the design and preliminary testing of the experimental setup and representative icing conditions calibration in the laboratory’s cold chamber. The drone rotor used has four blades with a diameter of 0.66 m and a maximum tip speed of 208 m/s. For the icing conditions, freezing rain and freezing drizzle were selected. A Liquid Water Content (LWC) calculation methodology for a rotor in hover was developed, and procedures to determine experimental LWC in the facility are presented in this paper. For the test setup, the cold chamber test section was adapted to fit the rotor and to control its ground clearance. Testing was aimed at studying the effect of rotor height h on aerodynamic performance, both with and without icing conditions. Results show no significant effect on the ground effect between h = 2 m and h = 4 m in dry runs, while the icing behavior can be largely influenced for certain conditions by the proximity of the precipitation source, which depend on the height of the rotor in these experiments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".