Enhanced Adiabatic Film Cooling Effectiveness by Varying Compound Angle
Bibliographic record
Abstract
Abstract Effusion cooling is the state-of-the-art cooling technology for gas turbines. However, the compound angle of effusion cooling holes is commonly set as a fixed value in prior studies. In particular, the compound angle is mostly set to zero in practice by assuming that highly directional miniature effusion cooling jets are aligned with the main flow. A recent study from our group has examined the directional effects of effusion cooling on adiabatic film cooling effectiveness (AFE) subjecting to a swirling main flow. It was found that a large compound angle initially facilitates a quick build-up of cooling film and the optimal compound angle reduced to smaller values downstream where the cooling film is further developed. The current study exploits the recent findings in the directional effects of effusion cooling by proposing novel effusion cooling designs with varying compound angles of cooling holes which are optimized for improving AFE along the main flow direction. In the present study, AFE was experimentally determined using Pressure Sensitive Paint (PSP) through invoking the heat/mass transfer analogy. A new effusion cooling design was evaluated by setting the compound angles of effusion cooling holes to three discrete values of 90, 60, and 30 degrees at three successive regions along the direction of the main flow. 2D AFE maps resulting from the proposed design were compared with those of conventional effusion cooling designs with fixed compound angles under identical conditions. It was found that the new effusion cooling design featuring varying compound angles produces a more uniform cooling film coverage and a small enhancement to AFE comparing to the conventional effusion cooling designs.
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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) | 0.002 | 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".