Mechanical Modelling of Highly Heterogeneous Thermally Sprayed Abradable Coatings
Bibliographic record
Abstract
Abstract Abradable coatings are widely used in aerospace turbine engines to improve seal performance between rotating and stationary parts and thus the engine efficiency. As they are elaborated by thermal spraying of composite powders, these materials are highly heterogeneous. Their behaviour in working conditions is quite complex and not fully understood. This study contributes to a better knowledge of the mechanical behaviour of a range of abradable coatings. Finite Element calculations were derived from the analysis of micrographs and allowed to estimate the coefficients of an anisotropic elastic law of behaviour. Several models were investigated in the case of an AlSi-PE (Polyester) coating. As the PE % appeared to be a determinant factor, its influence was quantified ranging from 0% to 100%, using virtual micrographs deduced from the original ones. The computed Young's moduli Ex (in coating longitudinal direction) were quite consistent, considering the hypotheses made. An empirical relationship giving the variation law of Ex versus the PE % was suggested and applied to predict the modulus Ex of a real coating, which was close to the measured value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".