Enhanced Experimental Testing of New Erosion-Resistant Compressor Blade Coatings
Bibliographic record
Abstract
Performance differences between bare 17-4PH steel V103 profile (NACA 6505 with rounded leading edge (LE) and trailing edge (TE)) gas turbine engine axial compressor blades, and those coated with either a chromium-aluminum-titanium nitride (CrAlTiN) or a titanium-aluminum nitride (TixAl1−xN) erosion-resistant coating were tested. A coating thickness of 16 μm was used, based on experimental results in the literature. Coatings were applied using arc physical vapor deposition at the National Research Council of Canada (NRC). All blades were tested under identical operating conditions in the Royal Military College of Canada (RMC) turbomachinery erosion rig. Based on a realism factor (RF) defined by the authors, this experimental rig was determined to provide the best known approximation to actual compressor blade erosion in aircraft gas turbine engine axial compressors. An average brown-out erosive media concentration of 4.9 g/m3 of air was used during testing. An overall defined Leithead–Allan–Zhao (LAZ) score metric, based on mass and blade dimension changes, compared the erosion-resistant performance of the bare and coated blades. Blade surface roughness data were also obtained. Based on the LAZ Score, CrAlTiN-coated blades performed at least 79% better than bare blades, and TixAl1−xN-coated blades performed at least 93% better than bare blades. The TixAl1−xN-coated blades performed at least 33% better than the CrAlTiN-coated blades. Extrapolation of results predicted that a V-22 Osprey tiltrotor military aircraft, for example, could fly up to 79 more missions with TixAl1−xN-coated compressor blades in brown-out sand concentrations than with uncoated blades.
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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) | 0.001 | 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".