Evolution of underwater noise emission prediction technology for ship-propulsor combinations in an industry environment
Bibliographic record
Abstract
The prediction of underwater noise emissions by marine vessels has evolved significantly over the last decade as the interest shifted from exclusive military to civil use as a result of environmental concerns. Among different research groups, the best practices for the estimation with high-fidelity simulation methods based on hydrodynamic sound emissions are developed considering the complete vessel and propulsor. Here the current state-of-the-art computational fluid dynamics (CFD) approach, centered around propulsion units, is worked out in steps leading to the application for vessels. This consists of incompressible finite volume method flow simulations with large eddy turbulence modelling and volume-of-fluid phase capturing with a Schnerr-Sauer cavitation model in combination with an added permeable surface Ffowcs-Williams-Hawkings method for far-field acoustics. Many mechanistic arguments support it and the final validation cases lead to acceptable accuracy and enrich simulation data acquisition, however, few technical limitations reveal weaknesses compared to traditional approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".