A Wind Tunnel Experimental Study of Icing on NACA0012 Aircraft Airfoil with Silicon Compounds Modified Polyurethane Coatings
Bibliographic record
Abstract
Ice formation on the aerodynamic surfaces of an aircraft is regarded as a major problem in the aerospace industry. Ice accumulation may damage parts, sensors and controllers and alter the aerodynamics of the airplane, leading to a range of undesired consequences, including flight delays, emergency landings, damaged parts and increased energy consumption. There are various approaches to reducing ice accretion, one of them being the application of icephobic coatings. In this work, commercially available polyurethane-based coatings were modified and deposited on NACA 0012 aircraft airfoils. A hybrid modification of polyurethane (PUR) topcoats was adopted by the addition of nanosilica and three-functional spherosilicates (a variety of silsesqioxane compound), which owe their unique properties to the presence of three different groups. The ice accretion on the manufactured nanocomposites was determined in an icing wind tunnel. The tests were performed under three different icing conditions: glaze ice, rime ice and mixed ice. Furthermore, the surface topography and wetting behavior (static contact angle and contact angle hysteresis) were investigated. It was found that the anti-icing properties of polyurethane nanocomposite coatings strongly depend on the icing conditions under which they are tested. Moreover, the addition of nanosilica and spherosilicates enabled the reduction of accreted ice by 65% in comparison to the neat topcoat.
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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".