Bibliographic record
Abstract
The Spinning Rotor Blade (SRB) is an apparatus developed at Anti-icing Material International Laboratory in collaboration with Bell Helicopter Textron to study ice physics, low energy de-icing systems and hydro- or ice-phobic coatings use for small helicopters. The SRB is a 1/18 subscale model of a small helicopter with 0.78 m diameter rotor of two blades rotated by a 10 hp engine to a tip speed of 130 m/s. The blades are 6066-T6 extruded aluminum with a NACA0012 airfoil of 69.64 mm chord and 0.315 m long. The icing tests were conducted in AMIL's low speed closed loop Icing Wind Tunnel with a liquid water content of 0.84 g/m3, a median volumetric diameter of 26.7 ± 2.6 μm, an air speed of 15 ± 0.5 m/s and temperature of -15 ± 0.5°C. The power to rotate the SRB was 1 200 ± 120 W with a vibration of 2 ± 0.4 g; when the icing began, the power increased to 5 200 ± 1 400 W at a rate of 30 ± 4 W/s, suddenly, after 160 ± 50 s, the power decreased, indicating that a piece of ice of 70 ± 15 mm length and about 4 g of weight was shed at the blade tip. The SRB is able to perform reproducible ice shedding tests with similar behavior to helicopters at low cost with repeatability below 30% and sensibility of ±5% for temperatures ranging from -5 to -20°C. The ice adhesive shear stress estimated from the SRB II at -15°C was 0.21 ± 0.06 MPa for aluminum. It decreased linearly when the temperature increased. Also, the adhesive shear stress obtained for icephobic Coating A was 0.10 MPa which is 2.1 less adhesive than aluminum, but its icephobicity is insufficient to be safely used on helicopters. Some empirical correlations for ice thickness, freezing fraction and adhesive shear stress were found with the SRB-II and also a criterion was proposed criterion to quantify the icephobicity of coating efficiency for helicopters, but without fundamental parametric scaling equations to helicopter, they are not helpful for helicopter.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".