An Experimental Investigation of the Convective Heat Transfer on a Small Helicopter Rotor with Anti-Icing and De-Icing Test Setups
Bibliographic record
Abstract
Successful icing/de-icing simulations for rotorcraft require a good prediction of the convective heat transfer on the blade’s surface. Rotorcraft icing is an unwanted phenomenon that is known to cause flight cancelations, loss of rotor performance and severe vibrations that may have disastrous and deadly consequences. Following a series of experiments carried out at the Anti-icing Materials International Laboratory (AMIL), this paper provides heat transfer measurements on heated rotor blades, under both the anti-icing and de-icing modes in terms of the Nusselt Number (Nu). The objective is to develop correlations for the Nu in the presence of (1) an ice layer on the blades (NuIce) and (2) liquid water content (LWC) in the freestream with no ice (NuWet). For the sake of comparison, the NuWet and the NuIce are compared to heat transfer values in dry runs (NuDry). Measurements are reported on the nose of the blade-leading edge, for three rotor speeds (Ω) = 500, 900 and 1000 RPM; a pitch angle (θ) = 6°; and three different radial positions (r/R), r/R = 0.6, 0.75 and 0.95. The de-icing tests are performed twice, once for a glaze ice accretion and another time for rime ice. Results indicate that the NuDry and the NuWet directly increased with V∝, r/R or Ω, mainly due to an increase in the Reynolds number (Re). Measurements indicate that the NuWet to NuDry ratio was always larger than 1 as a direct result of the water spray addition. NuIce behavior was different and was largely affected by the ice thickness (tice) on the blade. However, the ice acted as insulation on the blade surface and the NuIce to NuDry ratio was always less than 1, thus minimizing the effect of convection. Four correlations are then proposed for the NuDry, the NuWet and the NuIce, with an average error between 3.61% and 12.41%. The NuDry correlation satisfies what is expected from heat transfer near the leading edge of an airfoil, where the NuDry correlates well with Re0.52.
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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 | 0.001 |
| 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".