Propeller cavitation on small craft: Underwater noise measurements and visualisation from full-scale trials
Bibliographic record
Abstract
Propeller cavitation is a significant contributor to vessel underwater radiated noise (URN). It is often assumed to be the major contributor for large vessels at higher speeds, but very little work is available in the literature on the role of cavitation on small boat propellers. In this work, data from two trials are presented to show how cavitation develops on small boats and how this contributes to the overall sound levels. Camera footage is combined with hydrophone measurements to determine the cavitation inception speed and this shows that tip vortex cavitation can appear at 5 knots. The emergence of cavitation is accompanied by a sharp rise in the URN levels. Cavitation due to gas bubbles being pulled close to the propeller blades is observed at speeds as low as 4 knots, leading to either bubble collapse close to the blades or the tip vortex cavitating downstream of the propeller. Wavelet analysis is used to investigate the makeup of the high frequency noise, providing insights into the types of cavitation that are present and how they scale with speed. This shows that high frequency noise from cloud cavitation increases far more substantially with speed than for tip vortex cavitation. • Camera footage shows the cavitation pattern on an outboard propeller. • The cavitation inception speed on all three boats is no more than 6 knots. • Tip vortex cavitation predominates on both outboard-powered vessels. • High frequency noise scales weakly with speed when tip vortex cavitation predominates. • High frequency noise scales strongly with speed when cloud cavitation predominates.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".