An advanced impingement/film cooling scheme for gas turbines – numerical study
Bibliographic record
Abstract
Purpose The present study aims to conduct a numerical investigation of a novel film cooling scheme combining in‐hole impingement cooling and flow turbulators with traditional downstream film cooling, and was originally proposed by Pratt & Whitney Canada for high temperature gas turbine applications. Design/methodology/approach Steady‐state simulations were performed and the flow was considered incompressible and turbulent. The CFD package FLUENT 6.1 was used to solve the Navier‐Stokes equations numerically, and the preprocessor, Gambit, was used to generate the required grid. Findings It was determined that the proposed scheme geometry can prevent coolant lift‐off much better than standard round holes, since the cooling jet remains attached to the surface at much higher blowing rates, indicating a superior performance for the proposed scheme. Research limitations/implications The present study was concerned only with the downstream effectiveness aspect of performance. The performance related to the heat transfer coefficient is a prospective topic for future studies. Practical implications Advanced and innovative cooling techniques are essential in order to improve the efficiency and power output of gas turbines. This scheme combines in‐hole impingement cooling and flow turbulators with traditional downstream film cooling for improved cooling capabilities. Originality/value This new advanced cooling scheme both combines the advantages of traditional film cooling with those of impingement cooling, and provides greater airfoil protection than traditional film cooling. This study is of value for those interested in gas turbine cooling.
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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) | 0.002 | 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".