Manufacturing of Durable Riblet Coating for Green Aviation
Bibliographic record
Abstract
Aerodynamic drag reduction via biomimetic surface engineering with structured riblet coatings on aircraft, or the so-call ‘Sharkskin’ with periodic micro-ridges aligned with the direction of airflow, has been identified as a high return-on-investment with an immediate impact on the green house gas (GHG) emission reduction for existing and new aircraft. It offers a potential drag reduction of up to 10% under turbulent flow conditions. As such, riblet technologies have been intensively developed over the past decades. Nevertheless, riblet technology is not yet been widely adopted and critical challenges remain, such as coating material durability and 2D, 3D riblet manufacturing to ensure widespread application on aircraft. National research Council Canada has recently developed a new hydrophobic, erosion-resistant polyurethane coating that exhibits outstanding erosion resistance against high-speed sand particles and rain droplets and excellent weatherability. To explore its potential as a robust riblet coating, this work focuses on the development of a continuous production method of riblet thin films using a reactive extrusion sheet cast process. The reaction kinetics of the polyurethane was first investigated by a combination of techniques including (infrared spectroscopy (FTIR), dynamic scanning calorimetry (DSC) and rheology) to define process parameters such as extrusion temperature profiles and residence time. Thin film production was then demonstrated on a mini-lab extruder, followed by a successful scale-up using a pilot-scale twin-screw extruder. Micro-riblets were also successfully embossed on the thin film by pressing the cast film against a roller with a micro-riblet-structured surface.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".