The Effect of Turbulence Intensity on Film Cooling of Gas Turbine Blade From Trenched Shaped Holes
Bibliographic record
Abstract
This paper reports a computational investigation on the effects of mainstream turbulence intensity on film cooling effectiveness from trenched holes over a symmetrical blade. Computational solutions of the steady, Reynolds-Averaged Navier-Stokes equations are obtained using a finite volume method with k – ε Turbulence model. Whenever possible, computational results are compared with experimental ones from data found in the open literature. Computational results are presented for a row of 25 deg forward-diffused film hole within transverse slot injected at 35 deg to AGTB symmetrical blade. Four blowing ratios, M = 0.3, 0.5, 0.9 and 1.3 are studied together with four mainstream turbulence intensities of Tu = 0.5%, 2%, 4% and 10%. Results indicate that the trenched shaped holes tend to give better film cooling effectiveness than that obtained from discrete shaped holes for all blowing ratios and all turbulence intensities. The trenching of shaped holes has changed the optimum blowing ratio and also the location of re-attachment of separated jet at high blowing ratios. Moreover, it has been found that the effect of mainstream turbulence intensity for trenched shaped holes is similar to that obtained for discrete shaped holes with the exception that the sensitivity of film cooling effectiveness to turbulence intensity has decreased for trenched shaped holes.
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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".