LES Analysis of Vortex-Vortex Interaction and Associated Acoustic Signatures
Bibliographic record
Abstract
Abstract The rising levels of underwater ambient noise due to shipping have attracted the attention of international bodies such as the International Maritime Organization (IMO), International Towing Tank Committee (ITTC) and Vancouver Port, who aim to minimise radiated noise and reduce impact on marine creatures. A high-intensity turbulent noise not only brings negative impacts on a ship’s silent operation but also contributes to noise pollution that can disturb marine animals. Reconnection and breakdown of coherent vortices in propellers’ wake can produce significant pressure fluctuations, intensifying turbulent energy and contributing to a distinct noise signature. Relatively few studies have investigated the specific acoustic signatures resulting from vortex-vortex interactions. This study aims to understand these interactions and their associated acoustic signatures for a Vortex Reynold number, approximately of 2700, by examining the corresponding far-field acoustic signatures generated by these interactions. Contra-rotating and Co-rotating parallel vortex pairings with different separate distances under the same axial velocity had been modelled. These were conducted using Computational Fluid Dynamics (CFD) methods, where Large Eddy Simulation (LES) in combination with the Ffowcs Williams-Hawkings acoustic analogy (FW-H) were used to solve the hydrodynamic flow field and propagation of associated noises to the far-field. The unique acoustic signatures and hydrodynamic characteristics obtained from this investigation can provide an alternative perspective for understanding underwater acoustics, vortex-vortex interactions, and instabilities in incompressible conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".