Influence of steam-rich environments on the high temperature tribological behavior of Inconel 718 for sustainable aviation
Bibliographic record
Abstract
The aerospace industry has been looking for solutions to minimize emissions of pollutants into the environment. In this direction, replacing fossil fuels is a promising strategy. The use of hydrogen as a fuel has been shown to be a promising alternative due to its cleaner combustion, with the potential to reduce harmful emissions. Hydrogen primarily produces water during combustion, which becomes steam at high temperatures inside a gas turbine engine. However, there is limited research on the behavior of nickel-based alloys, which are widely used in gas turbine engines, in hydrogen and steam-rich environments. The interactions between steam at tribological interfaces within these engines remain poorly studied. Therefore, this study investigates the high temperature tribological behavior of Inconel 718 under steam conditions. Experiments were conducted to understand the wear mechanisms and the effects of temperature and steam on Inconel 718, using an experimental setup for producing and applying superheated steam to the samples during the sliding test. Subsequent analyses were conducted with a 3D measuring laser microscope, scanning electron microscopy (SEM) and Raman spectroscopy. The results revealed that the coefficient of friction decreases with increasing temperature, while wear increases with temperature. Additionally, the presence of steam exhibited a mild influence on wear and friction characteristics.
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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) | 0.001 | 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".