Experimental Evaluation of Icephobic Coatings on a UAV Propeller Operated in an Icing Wind Tunnel
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">In this research, the performance of two commercially available icephobic coatings is evaluated on an 81% scaled-down version of the Bell Flight APT 70 drone propeller. Tests are performed in an icing wind tunnel (IWT) under selected severe icing conditions to test the ice protection capability of coatings against both glaze and rime ice. Two different coating formulations are used, one is a polydimethylsiloxane (PDMS) acetoxy terminated coating, the other an epoxy-silicone. The coatings were briefly characterized in terms of their surface roughness, water contact angle and ice adhesion reduction factor compared to aluminum using the centrifugal adhesion test (CAT). Blade sets were prepared for both coatings and a third uncoated set was tested for reference purposes. Tests in the IWT were performed to simulate a true airspeed of 35 m/s and a constant propeller rotational speed of 5 500 RPM. Two conditions of liquid water content (LWC) and droplet median volumetric diameter (MVD) were considered: LWC = 0.8 g/m<sup>3</sup>, MVD = 20 μm and LWC = 0.2 g/m<sup>3</sup>, MVD = 40 μm. The first condition was performed at static air temperatures of -5°C, -12°C and -20°C while the second was only performed at -5°C. The performance of the propeller is evaluated by means of the relative change in thrust coefficient, the torque coefficient and propeller efficiency. Tests were conducted such that operating conditions are maintained until vibration limits exceed the tolerated threshold to allow the possibility for multiple ice shedding events. Results demonstrated that the PDMS coating successfully reduced ice adhesion for all tests conditions while the epoxy-silicone only reduced ice adhesion for tests conditions at -5°C. The ice protection provided by either coating is shown to be insufficient to ensure safe flight under icing conditions due to significant propeller performance degradation and severe vibrations caused by non-symmetrical ice shedding.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".