Effect of water vapor on tribological performance of Inconel 718 at different conditions for next-generation gas turbine engine
Bibliographic record
Abstract
Nickel-based superalloys (e.g., Inconel 718) are widely used in the aerospace industry due to their high temperature constancy in terms of mechanical properties and chemical stability. With the recent advances in sustainable aviation, there is a strong desire to better understand their compatibility with hydrogen combustion or water vapor in gas turbine engines. Therefore, this study investigated the tribological behavior of Inconel 718 under dry and water vapor conditions at various temperatures. The reciprocating ball on flat type tribometer was used to perform friction tests on Inconel 718 against alumina and Inconel 718 counterballs in the absence or presence of water vapor at room and elevated temperatures. The results showed that water vapor caused a reduction in the friction and wear at room and elevated temperatures against the Alumina mating surface, when compared to the tests performed under dry conditions. The low friction and wear in water vapor was attributed to the formation of an aluminum trihydroxide (bayerite—Al(OH) 3 ) tribofilm. On the other hand, Inconel 718 vs. Inconel 718 showed higher wear in the presence of water vapor at RT compared to that in dry conditions. Conversely, water vapor decreased wear at HT compared to HT dry. The higher wear was attributed to the lack of sufficient lubricious oxides formation on the wear tracks for Inconel 718 vs. Inconel 718 at HT.
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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) | 0.002 | 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".