Effect of blowing ratio on film-cooling effectiveness of ginkgo shaped holes: a numerical approach
Bibliographic record
Abstract
Modern gas turbine engines operate at high temperatures to improve thermal efficiency and power output. Increased rotor inlet temperatures increase the rate of heat transfer to the turbine blades, which requires sophisticated cooling schemes to keep the blade temperature at acceptable levels. This work is a numerical investigation of film cooling techniques as applied to gas turbines. The cooling performance of two differently shaped holes, namely, Ginkgo Forward and Ginkgo Reverse, were investigated in terms of centerline and local lateral cooling effectiveness, and a comprehensive comparison was made with the cooling performance of a cylindrical hole. The investigations were performed at a constant density ratio (DR = 2.0) and three different blowing ratios (BR = 1.0, 1.5, and 2.0). Under all of the operating conditions, the results demonstrated significant augmentation in centerline and lateral cooling effectiveness when the Ginkgo Reverse shaped hole was used, followed by the Ginkgo Forward and cylindrical cooling holes. For the shaped cooling holes, the low velocity gradient through the film alleviated the jet lift-off and turbulence, resulting in decreased entrainment of hot gas to the bottom surface. To conclude, the prominent lateral dispersal of the coolant due to the shaped cooling holes significantly enhanced thermal protection and the overall cooling performance.
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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 | 0.001 | 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".