Modeling of Hydroacoustic Noise From Marine Propellers With Tip Vortex Cavitation
Bibliographic record
Abstract
Abstract Due to growing marine ecological concerns, there is an acute industrial need to significantly reduce the underwater radiated noise (URN) from marine propellers during ship operations. In this regard, high-fidelity fluid flow and hydroacoustic models are required for understanding the propeller noise generation and propagation in the ocean environment. Using high-fidelity CFD modeling, the present work aims to study the cavitating turbulent flow of a full-scale marine propeller and explore the physical mechanism underpinning the underwater radiated noise. We employ the standard dynamic large eddy simulation for the turbulent wake flow and the Schnerr-Sauer cavitation model, while the Ffowcs-Williams-Hawkings acoustic analogy is considered for the hydroacoustic modeling. For the current investigation, we consider a well-known Potsdam Propeller Test Case to analyze the turbulent cavitating flow and the associated hydroacoustic emissions. To begin, the modeling framework is validated using the available experimental data, and distinctive double-helical tip vortex cavitation and its qualitative patterns along the vortex trajectory are captured. In comparison to the non-cavitating condition, the pressure distribution on the propeller surface is more disordered for the cavitating condition, which is further reflected by a relatively stronger power of both low-frequency tonal peaks and high-frequency broadband components in the spectrum of thrust generation. Specifically, the generation of cavitation leads to the enhancement of the monopole noise source and the breakdown of cavitation bubbles as well as vortex structures in the turbulent wake. Furthermore, the tonal noise with the frequency corresponding to the harmonics of blade passing frequency is also enhanced. Generally speaking, the generation of cavitation structures enhances the hydroacoustics energy of URN at all orientations, especially in the downstream direction with sound pressure level increasing up to 20 dB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".