Bibliographic record
Abstract
Ceramic thermal barrier coatings (TBCs) used in gas-turbine engines afford higher operating temperatures, resulting in enhanced efficiencies and performance. However, in the case of syngas-fired engines, fly ash particulate impurities that may be present in syngas can melt on the hotter TBC surfaces and form glassy deposits. These deposits can penetrate the TBCs leading to their failure. In experiments using lignite fly ash to simulate these conditions we show that conventional TBCs of composition 93wt% ZrO{sub 2} + 7wt% Y{sub 2}O{sub 3} (7YSZ) fabricated using the air plasma spray (APS) process are completely destroyed by the molten fly ash. The molten fly ash is found to penetrate the full thickness of the TBC. The mechanisms by which this occurs appear to be similar to those observed in degradation of 7YSZ TBCs by molten calcium-magnesium-aluminosilicate (CMAS) sand and by molten volcanic ash in aircraft engines. In contrast, APS TBCs of Gd{sub 2}Zr{sub 2}O{sub 7} composition are highly resistant to attack by molten lignite fly ash under identical conditions, where the molten ash penetrates ~25% of TBC thickness. This damage mitigation appears to be due to the formation of an impervious, stable crystalline layer at the fly ash/Gd{sub 2}Zr{sub 2}O{sub 7} TBC interface arresting the penetrating moltenfly- ash front. Additionally, these TBCs were tested using a rig with thermal gradient and simultaneous accumulation of ash. Modeling using an established mechanics model has been performed to illustrate the modes of delamination, as well as further opportunities to optimize coating microstructure. Transfer of the technology was developed in this program to all interested parties.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.970 | 0.977 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".