The controlled relaminarization of low velocity ratio elevated jets-in-crossflow
Bibliographic record
Abstract
The controlled suppression of instabilities, or “relaminarization,” in low velocity ratio and Reynolds number elevated jets-in-crossflow (JICFs) is discussed for jet-to-crossflow velocity ratios (R) less than 1.5 and jet Reynolds numbers (Red) less than 2000. The principal control method is via a synthetic jet from an annular slit coaxially surrounding the jet flow. Effects of forcing the JICF jet shear layer are studied using photographs obtained by smoke flow visualization, single point hot-wire measurements, and field measurements using a form of image correlation velocimetry on the smoke images. The unforced JICF is unstable in two R regimes separated by a window of stability centered around the velocity ratio R=1.13. In the lower unstable regime where there is formation of strong jet shear layer instabilities, the synthetic jet can suppress them. A mechanism for the suppression phenomenon is proposed. The synthetic jet reduces the local Reynolds number of a wakelike profile on the upstream side of the JICF between the free stream and jet flow below critical values for growth of an unstable flow structure. The control mechanism has many similarities to base bleed suppression of von Kármán vortex shedding in supercritical bluff bodies. A continuous stream from the same annular slit does indeed have a similar suppression effect to synthetic jet forcing.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".