Cavitating wake dynamics and hydroacoustics performance of marine propeller with a nozzle
Bibliographic record
Abstract
Using high-fidelity computational fluid dynamics modeling, the current work studies the cavitating turbulent flow of a ducted marine propeller and explores the physical mechanisms underpinning the underwater radiated noise. We employ the standard dynamic large-eddy simulation for the turbulent wake flow and the homogeneous Schnerr–Sauer model for the cavitation process, while the Ffowcs Williams–Hawkings acoustic analogy is used for hydroacoustic modeling. The modeling framework is validated against available experimental data, capturing a distinctive double-helical tip vortex cavitation and its qualitative patterns along the vortex trajectory. In comparison to the noncavitating scenario, the pressure fluctuation on the propeller surface is more ordered but energetic under cavitating conditions due to the periodic nature of the sheet cavity. This is reflected in the thrust spectrum in the form of stronger low-frequency tonal peaks and medium-frequency broadband components, while the high-frequency broadband components are relatively weaker. We show that cavitation enhances the monopole noise source due to fluid displacement by the cavity along with the dipole and quadrupole noise sources associated with the propeller surface and wake turbulence effects. Tonal noise with frequencies corresponding to harmonics of the blade passing frequency is also increased. Cavitating structures increase the hydroacoustic energy of the radiated noise at all orientations, particularly downstream, with an increase in the sound pressure levels by up to 20 dB. Finally, the addition of a duct nozzle inhibits cavitation originating from the propeller surface and its accompanying acoustic energy, although cavitating/vortical structures are now observed at new locations around the nozzle system. As a result, the overall radiated noise power is reduced in the ducted propeller configuration.
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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".