Influence of impact speed on water droplet erosion of TiAl compared with Ti6Al4V
Bibliographic record
Abstract
Water Droplet Erosion (WDE) as a material degradation phenomenon has been a concern in power generation industries for decades. Steam turbine blades and the compressor blades of gas turbines that use water injection usually suffer from WDE. The present work focuses on studying erosion resistance of TiAl as a potential alloy for turbine blades compared to Ti6Al4V, a frequently used blade alloy. Their erosion behaviour is investigated at different droplet impact speeds to determine the relation between erosion performance and impact speed. It is found that the relationship is governed by a power law equation, ER ~ V(n), where the speed exponent is 7-9 for Ti6Al4V and 11-13 for TiAl. There is a contrast between the observed speed exponent in this work and the ones reported in the literature for Ti6Al4V. It is attributed to the different erosion setups and impingement conditions such as different droplet sizes. To verify this, the erosion experiments were performed at two different droplet sizes, 464 and 603 μm. TiAl showed superior erosion resistance in all erosion conditions; however, its erosion performance exhibits higher sensitivity to the impact speed compared to Ti6Al4V. It means that aggressive erosion conditions decrease the WDE resistance superiority of TiAl.
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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 | 0.001 |
| 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".