Identification of Aeroacoustic Sources in a T-Junction
Bibliographic record
Abstract
This paper experimentally investigates the flow-sound interaction mechanisms in a T-junction combining the flow from its two co-axial side-branches into the central branch. The T-junction has a sudden area expansion at each side-branch entrance. Flow separation at these area expansions forms free shear layers which are shown to excite the acoustic mode(s) of the branches over several ranges of flow velocity, each of which results from the coupling of the acoustic mode with a different shear layer oscillation mode. Phase-locked particle image velocimetry is utilized to detail the unsteady flow field over the acoustic cycle for the oscillation mode which resulted in the strongest acoustic resonance. Finite element analysis is used to characterize the excited acoustic mode shape and its associated particle velocity field. In-depth analysis of the flow-sound interaction mechanism inside the T-junction is performed by means of Howe’s acoustic analogy. It is concluded that the flow-sound interaction mechanism in the entrance region of the T-junction produces a spatially alternating pattern of acoustic energy generation and absorption. This alternating pattern of energy exchange between the flow and sound fields results in a minimal amount of net acoustic power being generated in the entrance region. However, the increasing orthogonality between the acoustic particle streamlines and the flow streamlines near the exit of the T-junction at its center results in the majority of the generated sound power which sustains the acoustic resonance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".