Vorticity shedding and acoustic resonance excitation in a tube array with different compactness
Bibliographic record
Abstract
This study experimentally investigates the mechanism of flow-excited acoustic resonance in an inline tube array, with emphasis on the effect of the number of tubes in the transverse and streamwise directions on triggering and sustaining acoustic resonance. The results show that the number of tubes in the transverse direction makes the tube array more susceptible to acoustic resonance excitation. While the number of tube rows in the streamwise direction contributes to the development of distinct flow structures, a minimum requirement of four tube rows to reproduce the aeroacoustic behavior expected in a full tube array configuration. Particle image velocimetry measurements using both phase-locked and modal decomposition techniques reveal intrinsic flow structures, dominated by low and high frequencies flow periodicities, and how they are influenced by the excitation of different acoustic cross-modes. Under off-resonance conditions, a dominant jet-like flow develops between the tube lanes, characterized by a symmetric vortex structure about the jet centerline. When resonance is excited by the higher-frequency periodicity, a co-shedding flow pattern emerges, sustaining organized jet flow. In contrast, excitation by the lower-frequency periodicity results in a quasi-single-body shedding pattern that suppresses jet formation. The results presented in this paper show clear evidence of the critical role that acoustic particle velocity plays in organizing the vorticity shedding in the tube array with respect to the excited acoustic cross-modes. These insights provide the foundation for implementing effective control strategies to mitigate flow-excited acoustic resonance in tube arrays.
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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) | 0.001 | 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".