Design Modifications to Minimize Tip Vortex Cavitation in Marine Propellers
Bibliographic record
Abstract
Abstract Underwater radiated noise (URN) from marine vessels poses significant threats to marine ecosystems, necessitating effective mitigation strategies. Propellers are a primary source of this noise, and reducing their emissions is essential for quieter ship operations. Cavitation, the vaporization of liquid water in low-pressure regions forming during the operation of propellers, is one of the major contributors to propeller noise. Among various forms of cavitation, the onset of sheet cavitation is delayed on modern propellers by the improvements in blade design, causing tip vortex cavitation (TVC) to often appear earlier. The current study focusses on mitigating TVC through geometric modifications of the propeller blades. The blade geometry is fine-tuned through bending and twisting; bending is performed in the direction normal to the blade’s sectional chord-lines, altering its rake and skew distribution. Twisting is applied through modification of the radial pitch distribution. In this work, a computational toolbox - MultiProp, is developed to transform conventional propeller geometries into modified designs based on specified bending and twisting parameters. An integrated performance index (IPI) is proposed, combining TVC behavior and hydrodynamic efficiency to assess propeller performance. Numerical simulations comparing conventional and modified propellers reveal that these geometric changes effectively mitigate TVC without compromising hydrodynamic performance. The proposed modifications can be incorporated during the design stage as a passive TVC mitigation strategy, forming the basis for a multi-objective optimization framework to design efficient, low-noise propellers.
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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) | 0.001 | 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 | 0.001 | 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".