Investigating the role of propeller geometry and surface characteristics in UAV ice accretion: An experimental study
Bibliographic record
Abstract
A lab based experimental study of atmospheric ice accretion on UAV propellers with different geometric and surface characteristics was conducted to study the ice accretion physics and resultant changes in propeller thrust and electrical power consumption. These experiments were conducted at the Anti-icing Materials International Laboratory (AMIL) Icing Wind Tunnel (IWT) at the Université du Québec à Chicoutimi (UQAC), Canada. The experimental icing conditions are determined in accordance with the 14 CFR Part 29 Appendix C for rotorcraft operating at altitudes below 10,000 feet. In this study the influence of following four geometric parameters and two surface characteristics of UAV propeller on ice accretion is analysed: 1) propeller diameter, 2) propeller pitch, 3) propeller chord length, 4) propeller winglets, 4) propeller surface finish and 6) Icephobic coatings. The analysis of results shows that the change in these features does not significantly impact the nature and shape of ice accretion but mainly influence the surface area affected by ice accretion. The thrust coefficient and electrical power coefficients vary considerably with change in propeller geometric features. The variation in propeller blade surface characteristics has a significant impact on the ice shedding characteristics of UAV propeller blade. Considering the high-power requirements of active ice mitigation techniques for UAV propellers, the results obtained from this study can be employed to develop a passive/hybrid ice mitigation approach and further optimize the geometric parameters of UAV propeller blades for efficient operations in icing conditions.
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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) | 0.000 | 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".