Numerical investigation of the influence of a hole imperfection on film cooling effectiveness
Bibliographic record
Abstract
Purpose The purpose of this paper is to report a numerical investigation of jet‐cross‐flow interaction in the presence of imperfection inside the injection hole with application to film cooling of turbine blades. Design/methodology/approach The work includes the prediction of the thermal and hydrodynamic fields by solving the Reynolds Averaged Navier Stokes and energy equations using the finite volume method with a body‐fitted hexahedral unstructured grid. The turbulence field is resolved by use of the k‐epsilon turbulence model. Findings The computational results show a dramatic and rapid decrease of the film cooling effectiveness when the obstruction is superior to 50 per cent. It is found that when the obstruction is close to the exit hole, the thermal protection is significantly reduced. Research limitations/implications The present numerical investigation is simply directed towards a qualitative investigation of hole imperfection effects on film cooling. Practical implications The motivation comes from several industrial applications such as film cooling of gas turbine components and fuel injection. One of the main challenges of using film cooling is the blockage of holes by particles ingested by the engine during landing/take off or due to application of thermal barrier coating or due to combustion particles as well as inaccuracies that result from drilling of holes. Originality/value The main goal of the present study is to conduct a numerical parametric investigation rather than reproducing the exact Jovanović's experimentation.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".