Assessment of Propeller Cavitation Inception Speed Based on Onboard Vibration and Underwater Acoustic Data
Bibliographic record
Abstract
The level of underwater radiated noise (URN) caused by marine transportation has been steadily rising in recent decades, reaching a point where it is recognized as a significant environmental issue. This increase in noise can be attributed to the growing number and larger sizes of commercial vessels. The detrimental effects of underwater noise have been observed in various species, including mammals, fish, and invertebrates. The URN emitted by ship primarily consists of three types of noise: machinery noise, propeller noise, and hydrodynamic noise. At low speeds, machine noise is the dominant factor, while at high speeds, propeller noise begins as the main source of noise, particularly when propeller cavitation becomes more pronounced. It is crucial to identify the cavitation inception speed (CIS) in order to reduce propeller noise and mitigate its impact on the surrounding marine environment. This abstract discusses the use of onboard vibration data to estimate the cavitation inception speed and detect its occurrences. By analyzing the vibration data generated by the propeller, unique frequency patterns generated by cavitation can be identified. In this context, Detection of Envelope Modulation On Noise and cyclic modulation coherence algorithms are employed along with vibrational levels to indicate the occurrence of cavitation. The accuracy of the algorithms is assessed using onboard vibration data collected onboard the research ship Coriolis II as well as to underwater acoustic data recorded by the Marine Acoustic Research Station. The ship is equipped with a twin screw propeller with four blades. The results show that the two algorithms of the estimation of the CIS is fairly accurate and reliable. The use of onboard vibration data has the potential to become a standard practice for detecting propeller cavitation,estimating the CIS and potentially reducing underwater noise pollution in the maritime industry.
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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".