Assessment of AlSi-based abradable coatings with hBN and MoCr additives for aerospace conditions: A novel high-temperature rig approach
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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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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 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| 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 it