Tribological evaluation of Cu-based abradable coating
Bibliographic record
Abstract
Abradable seal are fundamental for mitigating leakage between rotating and stationary components across diverse segments of gas turbine engines. In the low-pressure stage, these strategically designed sacrificial materials exhibit preferential wear during interaction with the blades, avoiding damage, thereby reducing the clearance and improving overall engine efficiency. The intricate microstructure of abradable seals is tailored for demanding operational environments. Generally, abradables comprise a metallic matrix, a self-lubricating non-metallic phase, and an optimized porosity fraction. However, more recent developments for next-generation abradable seals have transitioned from elemental aluminum to advanced composites, prioritizing robust resistance to degradation mechanisms. For instance, Cu-based materials have recently been receiving particular interest due to their potential benefits in terms of high ductility and improved wear resistance. Thus, this study aims to compare the tribological performance of Cu8.5Al1Fe10Polyester abradable coatings to the established AlSi-polymer baseline material, focusing on sliding and erosive wear mechanisms. The Cu-based and the baseline abradable were deposited by atmospheric plasma spray (APS) reaching a thickness of more than 3 mm, and their tribological behavior was evaluated under reciprocating and unidirectional motions. Microstructural analysis via scanning electron microscope (SEM) revealed a typical layered structure with pores and polyester present in the Cu matrix, exhibiting higher hardness than the AlSi-polymer abradable baseline. Results from sliding wear behavior presented a variation in the coefficient of friction and an increased erosion wear resistance by Cu-based coating at lower angles. These findings provide valuable insights for improving current abradable systems and developing next-generation materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".