Numerical predictions of underwater radiated noise from a non-cavitating model-scale propeller
Bibliographic record
Abstract
Propeller-induced acoustic noise from marine vessels is the largest source of anthropogenic underwater radiated noise (URN) and a significant threat to marine ecosystems. Under typical operation, cavitation dominates the URN emissions. Cavitation, which is a pressure-driven phase-change process that results in the violent formation and collapse of vapor bubbles in the wake of the propeller, is often unavoidable during realistic, full-scale operating conditions. However, at a model scale, inducing cavitation requires a depressurized flow facility that makes acoustic measurements difficult due to confinement effects. Numerical simulation is, therefore, appealing as a tool for predicting URN, but the simulation of the cavitation phenomenon and the associated acoustics involves considerable uncertainty and a range of potential sources of error. In propeller operation, cavitation frequently occurs in the core of the vortices shed from the tips of propeller blades. In the present work, we developed a delayed detached-eddy simulation (DDES) of a model-scale ship with the focus on predicting fluctuating pressure due to shed vorticity. The solution is compared to hydrophone measurements from non-cavitating tow-tank experiments. Finally, we numerically introduce cavitation at model scale in the numerical solution and examine its effects.
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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 | 0.001 |
| 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".