Numerical Investigation of Abradable Coating Wear Through Plastic Constitutive Law: Application to Aircraft Engines
Bibliographic record
Abstract
In the field of turbomachines, better engine performances are achieved by reducing possible parasitic leakage flows through the closure of the clearance distance between blade tips and surrounding casings. Together with new technologies involving higher casing conicity for improved compression rates, direct contact is now considered as part of aircraft engines normal life. In order to avoid possibly catastrophic scenarios due to high contact efforts between the rotating and static components, implementation of abradable coatings has been widely recognized as a robust solution offering several advantages: reducing potential damage to the incurring blade as well as adjusting operating clearances, in-situ, to accept physical contact events. Nevertheless, the process of wear undergone by abradable coatings is not well understood and its consequences are still under investigation. In the present work, its macroscopic behavior is numerically approximated through a piecewise linear plastic constitutive law which allows for real time access to the current abradable layer profile. First results prove convergence in time and space of the proposed approach and show that the frequency content of the blade response is clearly affected by the presence of abradable coatings. It seems that opening the clearance between the blade tip and the casing during wear leads to large amplitudes of motion far from the usual linear conditions provided by the well-known Campbell diagrams.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".