Super-Hydrophobic Coatings as a Part of the Aircraft Ice Protection System
Bibliographic record
Abstract
This paper reviews the current knowledge on super-hydrophobic coatings (SHC). Using an ideal super-hydrophobic surface patterned with identical cylindrical flathead posts forming a square network with constant periodicity, models are proposed to explain SHC, wear and ice adherence on SHC. The models demonstrate that SHC based on Cassie-Baxter state improve the bead mobility compared to SHC based on Wenzel state and more suitable for aircraft application. Their erosion resistance can be improved by increasing the post height and the hydrophobic material thickness. Their ice adhesion reduction factor (IARF) is better but SHC based on Cassie-Baxter state have a limitation to reduce ice adherence dependence on the surface pattern and IARF of the hydrophobic material. The bead mobility is calculated from advancing and receding water contact angles (WCA). The airflow test is recommended to measure the advancing and receding WCA because the test is more representative of phenomena observed for aircraft flight in icing clouds. Experiments show that no ridge forms when a wing anti-icing system operating in running wet mode is used in combination with SHC. The reason is not well understood but water shedding is assumed to be the main driver. If the water film breaks into beads, the beads roll on the surface due to the bead sphericity resting on SHC and the nonuniform velocity profile over the wing surface. A possible explanation for the water shedding is that the rolling creates an aerodynamic force that lifts the bead off the wing.
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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 | 0.000 |
| 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".