A Numerical Study on Increasing Film Cooling Effectiveness Through the Use of Sister Holes
Bibliographic record
Abstract
Film cooling has been the primary focus of turbine blade cooling research for the past half century. However, as engines become more powerful, more effective non-traditional means of cooling become necessary. The current study branches out into a new scheme for film cooling; sister holes. The geometry of the current work makes use of three cylindrical holes inclined at 35° to the horizontal: one primary injectant hole bound by two sister holes. Numerical simulations were run with blowing ratios varying from M = 0.2 to M = 1.5, using the realizable k-ε turbulence model with near-wall modeling. The results were analyzed for both adiabatic thermal effectiveness as well as vortex production due to flow mixing. In general, sister holes offer significant advantages in thermal protection over their single hole counterparts both laterally and along the centre-line, particularly in regions close to the hole. Simulations showed that the laterally averaged adiabatic thermal effectiveness increased by a factor of 1.35 for M = 0.2 up to a factor of 1.62 for M = 1.5. Similarly, the centre-line effectiveness increased by a factor of 1.22 at M = 0.2 up to a factor of 1.68 at M = 1.5. These benefits are heavily weighted by the near-hole region; however, increases are evident throughout the computational domain. This sister hole technique offers significant advantages with minimal penalties, making it a valuable candidate for future blade cooling applications.
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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".