Assessment of AlSi-based abradable coatings with hBN and MoCr additives for aerospace conditions: A novel high-temperature rig approach
Bibliographic record
Abstract
ABSTRACT Abradable coatings are crucial for enhancing gas turbine efficiency and enabling sustainable aviation by reducing fuel consumption and protecting rotor components during blade-casing interactions. However, assessing their performance under relevant speed and temperature conditions remains challenging due to the cost and complexity of custom-built abradable rigs. This study addresses these challenges by upgrading an existing abradable test rig with a high-temperature module, supporting scalable materials testing under extreme gas turbine and hydrogen-compatible turbine conditions. It also evaluates the abradability performance of three thermally sprayed AlSi-based coatings at 300°C. (1) AlSi-Poly, with 40 wt% polyester as a baseline; (2) AlSi-MoCr, with similar polyester content plus small additions of molybdenum (Mo) and chromium (Cr); and (3) AlSi-hBN-Poly, with 6 wt% hexagonal boron nitride (hBN) and 20 wt% polyester. The inclusion of hBN, an eco-friendly solid lubricant, and MoCr, recognized for corrosion resistance, reflects growing interest in materials designed for energy-efficient turbines and Industry 4.0 aerospace systems. Abradability tests showed that AlSi-Poly and AlSi-MoCr outperformed AlSi-hBN-Poly at both temperatures based on lower reaction forces. All coatings exhibited reduced forces at 300°C due to thermal softening. AlSi-Poly and AlSi-MoCr demonstrated comparable abradability, with smoother wear tracks at room temperature that worsened at 300°C, along with increased dynamic interaction coefficient (Ft/Fn). In contrast, AlSi-hBN-Poly stood out for its thermal stability, higher roughness, and the lowest Ft/Fn. These findings also highlight the relevance of the high-temperature abradable rig as a cost-effective platform for pre-screening aerospace abradables under application-relevant conditions, bridging fundamental research and engine testing.
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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) | 0.001 | 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".