Bibliographic record
Abstract
Effusion cooling has been a popular technology integrated into the design of gas turbine combustor liners. A staggering amount of research was completed that quantified performance with respect to operating conditions and cooling hole geometric properties; however, most of these investigations did not address the influence of the manufacturing process on the hole shape. This study completed an adiabatic wall numerical analysis using the realizable k–ϵ turbulence model of a laser-drilled hole that had a nozzled profile with an area ratio of 0.24 and five additional cylindrical, nozzled, diffusing, and fileted holes that yielded the same hole mass flow rate at representative engine conditions. The traditional methods for quantifying blowing ratio yielded the same value for all holes that was not useful considering the substantial differences in film cooling performance. It was proposed to define hole mass flux based on the outlet y-cross-sectional area projected onto the inclination angle plane. This gave blowing ratios that correlated to better and worse cooling performance for the diffusing and nozzled holes, respectively. The diffusing hole delivered the best film cooling due to having the lowest effluent velocity and greatest amount of in-hole turbulent production, which coincided with the worst discharge coefficient.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".