Hydroacoustic analysis of a full-scale marine vessel: Prediction of the cavitation-induced underwater radiated noise using large eddy simulations
Bibliographic record
Abstract
This numerical study provides insight into the mechanism of noise generation by a cavitating flow in the wake of a marine propeller under realistic operating conditions, which poses a significant threat to marine ecosystems. We examined a full-scale vessel with an entire hull and an isolated model-scale marine propeller (INSEAN E779A) with a maneuverable rudder under various highly turbulent inflow conditions that strongly affect the spectral characteristics of the radiated noise. Insight into the acoustic behavior was gained by employing a combination of the large eddy simulation (LES) treatment of turbulence and the Schnerr–Sauer volume of fluid cavitation model. The hydrodynamic solution was coupled with the Ffowcs Williams-Hawkings (FW-H) strategy for noise and vibration identification. We focused on the interactions between the characteristic cavitation patterns of marine propellers (sheet, tip, and hub cavities) and the dominant structures of the turbulent wake (tip, root, trailing edge, and hub vortices, as well as the distributed small-scale vorticity). The small-scale topological structures in the swirling wake of a propeller directly manifest in the radiated sound level and affect the intensity of multiple frequency ranges. Quantitative analysis of thrust, pressure fluctuations, and sound pressure levels (SPLs) demonstrates significant effects of blade loading, wake distribution, and cavitation development. The peak and average SPL distributions obtained through LES show lower dominant and higher average frequencies compared to those obtained by the FW-H method. The overall SPL obtained by LES were higher than those calculated using the FW-H acoustic analogy at all microphone locations. The overall noise was dominated by the low-frequency broadband noise, attributed to energetic helical vortices, and narrow-band peaks in the medium-high frequency range that originated from other sources, like cavitation structures.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".