Effect of Ceramic Particle Reinforcement on the Erosion Resistance of Thermally Sprayed De-Icing Systems
Bibliographic record
Abstract
Abstract Developing effective heating systems to prevent ice accretion on the surface of wind turbine blades and aircraft wings is of great significance for extreme cold environments. However, due to high velocity impingement of water droplets and solid particles on the surface of these components, an appreciable degree of surface material degradation may occur. In this study, nickel-chromium-aluminum-yttrium (NiCrAlY) was chosen as a metal matrix material for a coating-based heating system. Pure ceramic powders, namely, alumina and titania, and a cermet powder, tungsten carbide-cobalt (WC-12Co), were mechanically admixed with NiCrAlY powder and deposited to fabricate reinforced metal matrix composite (MMC) coatings. The powders were deposited on cylindrical low carbon steel bars by using flame spraying. The specimens were placed in a wind tunnel to conduct a comparative investigation of their erosive wear resistance under water droplet impact. A cold spraying unit was used for solid particle impact erosion tests. The erosive wear rates were quantified by measuring mass loss. The experimentally obtained results showed noticeably lower wear rate in NiCrAlY-WC-12Co and NiCrAlY-titania coatings compared to the other coatings. The results suggest that certain MMC coatings could be effectively employed to decrease the erosion rate of coating-based heating elements.
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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".