Penetration and Mixing Characteristics of Inclined Jets in Crossflow at Low Momentum Flux Ratios
Bibliographic record
Abstract
Abstract This study investigates the factors affecting low momentum jets that are injected at an angle relative to a crossflow stream, which is relevant to film-cooling technologies. Quantitative measurements of the jet fluid concentration were obtained based on planar laser induced fluorescence (PLIF) from acetone vapor that was seeded into the jet. The jets were injected at four different axial locations downstream of the leading edge of a flat plate, resulting in different boundary layer thicknesses at the injection location. At each location, the jet-to-crossflow momentum flux ratio was varied from 0.5–5. The jet centerline trajectories were affected not only by the momentum flux ratios, but also by the approaching crossflow boundary layer thickness, with the jets penetrating the least for the thickest boundary layers. Measurements of the jet fluid concentration along the jet centerline showed an exponential decay rate of −1.3 across all cases. However, the behavior in the immediate vicinity of the jet depended on the boundary layer thickness, with thicker boundary layers resulting in a slower decay. Hence, the concentration profiles were shifted relative to the injection point depending on the injector position on the plate. The concentration profiles perpendicular to the jet axis were self-similar when scaled with the profile half-width.
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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 | 0.001 | 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 | 0.001 |
| 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".