Passive control of deep cavity shear layer flow at subsonic speed
Bibliographic record
Abstract
Passive control of the flow over a deep cavity at low subsonic velocity is considered in the present paper. The cavity length-to-depth aspect ratio is L/H = 0.2. particle image velocimetry (PIV) measurements characterized the flow over the cavity and show the influence of the control method on the cavity shear layer development. It is found that both the “cylinder” and the “shaped cylinder”, placed upstream from the cavity leading edge, result in the suppression of the aero-acoustic coupling and highly reduce the cavity noise. It should be noted that the vortical structures impinge at almost the same location near the cavity downstream corner with and without passive control. The present study allows to identify an innovative passive flow control method of cavity resonance. Indeed, the use of a “shaped cylinder” presents similar suppression of the cavity resonance as with the “cylinder” but with less impact on the cavity flow. The “shaped cylinder” results in a smaller shear layer growth than the cylinder. Velocity deficiency and turbulence levels are less pronounced using the “shaped cylinder”. The “cylinder” tends to diffuse the vorticity in the cavity shear layer and thus the location of the maximum vorticity is more affected as compared to the “shaped cylinder” control. The fact that the “shaped cylinder” is capable of suppressing the cavity resonance, despite the vortex shedding and the high frequency forcing being suppressed, is of high interest from fundamental and applied points of view.
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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".