Effect of Various Surface Coatings on De-Icing/Anti-Icing Fluids Aerodynamic and Endurance Time Performances
Bibliographic record
Abstract
The Anti-icing Materials International Laboratory (AMIL) has been testing SAE AMS1424 and AMS1428 ground de-icing/anti-icing fluids for more than 30 years. With the introduction of new surface coatings and their investigation as potential passive ice protection systems, or for hybrid use with other methods, it is important to understand their interaction with the ground de-icing/anti-icing fluids prior to applications on aircraft. In this study, five different surface coatings, both commercially available and under development, have been tested under two current test methods used to qualify the ground de-icing/anti-icing fluids: The Water Spray Endurance Test (WSET) and the Aerodynamic Acceptance Test (AAT). The tests were performed on three existing commercial de-icing/anti-icing fluids. The results have shown that the coatings tested in this study can considerably reduce the endurance time of the fluids and affect their ability to spread and wet the test surface. Superhydrophobic 1 coating also reduced the aerodynamic penalties created by the Ref. Fluid. Surface coatings, no matter their nature, can impact the performances and behaviour of the fluids and should be thoroughly tested before their use in the industry. The conclusions and methodology of this study were used in the development of sections of the SAE AIR6232 Aircraft Surface Coating Interaction with the Aircraft Deicing/Anti-Icing Fluids standard.
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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) | 0.000 | 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".