Particle Image Velocimetry Measurements of Aeroacoustic Sources of a Shallow Cavity in a Pipeline
Bibliographic record
Abstract
The aeroacoustic sources generated by flow over a ducted shallow cavity in the presence of a longitudinal plane sound wave are examined at various Strouhal numbers and sound intensities. The cavity is exposed to high Reynolds number fully developed pipe flow. Extensive particle image velocimetry (PIV) flow measurements are performed to characterize the unsteady velocity field and finite element analysis is used to obtain the acoustic velocity field. Howe's aeroacoustic integrand is then used to compute the spatial and temporal distributions of the aeroacoustic sources resulting from the cavity shear layer interaction with the sound field. The results show two aeroacoustic sources separated by a sink along the cavity shear layer. This distribution is different from that reported for the closed side-branch resonance case, which shows a single source at the downstream corner and a sink at the upstream corner of the cavity. The effect of the upstream corner geometry in the present case is, therefore, expected to be different from the case of side-branch resonance. The time-averaged sound power distribution is computed and the total sound power per cycle is compared with the aeroacoustic source strength measured by means of the standing wave method (SWM) (Mohamed, S., Graf, H. R., and Ziada, S., 2011, “Aeroacoustic Source of a Shallow Cavity in a Pipeline,” ASME Paper No. PVP2011-57437). The merits of these two methods in determining the aeroacoustic sources are highlighted.
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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) | 0.000 | 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".