Analysis of Two-Phase Film Cooling Mechanisms and Mist Concentration Effects for Enhanced Performance Using Laid-Back Fan-Shaped Holes
Bibliographic record
Abstract
Film cooling plays a crucial role in protecting gas turbine engine components from extreme temperatures.Recent research has highlighted the potential benefits of introducing water mist into film cooling holes.This study investigates the influence of varying mist droplet concentrations while maintaining a constant droplet size of 5μm.The cooling process is analyzed using the k-ε turbulence model with enhanced wall treatment.Key parameters such as the blowing ratio, momentum flux ratio, and jet vorticity are examined to assess their impact on cooling performance.To simulate the behavior of mist droplets, we employ the discrete phase model with a stochastic tracking approach, allowing for the detailed tracking of individual droplets within the flow field.Mist concentrations of 2%, 4%, 7%, and 10% are evaluated.The results indicate that at a blowing ratio of 1, a higher mist concentration of 10% enhances cooling effectiveness.At a BR of 2, the same mist concentration promotes deeper penetration of the coolant jet into the mainstream flow.In the far downstream region, higher mist concentrations aid in the development of the coolant film along the flat surface, further improving film cooling effectiveness.Additionally, the study highlights the significance of vortex structures generated by crossflow interactions, which play a vital role in coolant-mainstream mixing and 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 | 0.000 |
| 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".