Design and Development of Cost-Effective Equipment for Tribological Evaluation of Thermally Sprayed Abradable Coatings
Bibliographic record
Abstract
Abstract Thermally sprayed abradable coatings are essential for improving the performance of gas turbine engines. They act as a protective barrier between the stationary casing and rotating blades. Though a lot of research has been done on abradable coatings, little attention has been paid to comprehending wear mechanisms in the abradable-blade tip interaction. The goal of this project is to create a cost-effective test rig that can evaluate different thermally sprayed abradable coatings and understand how they interact with titanium blade tips under application-relevant conditions. Blade tip velocity, incursion rates, incursion depths, reaction forces, and interfacial temperatures are some of the inputs and outputs that the testing rig can provide. Aiming to validate the rig, this study examined the wear behavior of aluminum, thermally sprayed polyester, and AlSi-40Polyester abradable coating. The reaction forces for aluminum and polyester were overall higher when compared to AlSi-40Polyester. However, thermally sprayed polyester showed the highest interfacial temperatures of all materials tested. The difference in the reaction forces and interfacial temperature correlates well with the different wear mechanisms and thermal conductivities. Overall, the equipment showed to be a promising pre-screening methodology to evaluate and develop novel thermal spray abradable coatings.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".