Considerations on the Use of Hydrophobic, Superhydrophobic or Icephobic Coatings as a Part of the Aircraft Ice Protection System
Bibliographic record
Abstract
Ice adhesion on critical aircraft surfaces is a serious potential hazard that runs the risk of causing accidents. For this reason aircraft are equipped with active ice protection systems (AIPS). AIPS increase fuel consumption and add complexity to the aircraft systems. Reducing energy consumption of the AIPS or replacing the AIPS by a Passive Ice Protection System (PIPS), could significantly reduce aircraft fuel consumption. New coatings with superhydrophobic properties have been developed to reduce water adherence to surfaces. Superhydrophobic coatings can also reduce ice adhesion on surfaces and are used as icephobic coatings. The question is whether superhydrophobic or icephobic coatings would be able to reduce the cost associated with AIPS. To address this concern, this paper reviews the current knowledge on superhydrophobic and icephobic coatings, proposes a parameter to quantify the coating hydrophobicity level and presents methods to adapt available experimental data to aircraft applications. A second important question is whether the durability of superhydrophobic coatings is adequate for aircraft applications. Most of the available coatings that can reduce AIPS power consumption show poor erosion resistance and therefore have no practical use. To help coating manufacturers develop coatings adapted to aircraft applications, aircraft erosion test guidelines are presented. Experiments have demonstrated that superhydrophobic coatings can significantly reduce AIPS power consumption; the savings depend on the hydrophobicity level of the coating. However, erosion resistance is the biggest challenge for manufacturers and designers of coatings. Superhydrophobic and icephobic coatings need to be developed to satisfy erosion requirements. Ice phobic coatings performance needs to be improved as well.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".