The Role Of Thermally Grown Oxide In The Failure Thermal Barrier Coatings For Gas Turbine Engine Applications
Bibliographic record
Abstract
Thermal barrier coatings (TBCs) are widely used in gas turbine engines for propulsion and power generation to maximize engine operating temperature and fuel efficiency. TBCs comprise primarily three major components: the ceramic top coat, the intermetallic bond coat and a thin layer of a thermally grown oxide (TGO) formed at the bond coat/top coat interface. It was demonstrated that the TGO layer plays a critical role in determining the TBC life time with many TBC failure events occurring with its direct involvement. However, the actual mechanisms that govern TBC degradation and failure are still not fully understood in terms of the TGO role and the effects of its properties on the failure process. Stress analysis and TBC life time modelling were used in this study to demonstrate that the TGO morphology, its specific mechanical and thermal properties significantly affect the degradation modes and failure behaviour of the entire TBC system while the particular failure mechanisms differ depending on the ceramic top coat fabrication process. In this paper, the effect of changes in the TGO layer during thermal cycling has been theoretically investigated for TBC with top coats fabricated by atmospheric plasma spray and electron be am physical vapour deposition and the differences in their degradation and failure have been discussed.
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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) | 0.000 | 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 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".