Modeling the generation and propagation of hydrodynamic hull noise near the ocean surface
Bibliographic record
Abstract
Hydrodynamic hull noise is an important consideration for determining the detection envelope of SONAR domes mounted to surface vessels. In order to model the generation of this noise by a moving ship, a hybrid computational hydro-acoustic modeling methodology has been developed by combining the Lighthill-Curle acoustic analogy with the Numerical Wind Tunnel computational fluid dynamics code. This model has been shown in previous work to significantly over-predict the experimentally observed far field sound of Canadian Forces Auxiliary Vehicle Quest in at-sea acoustic trials. This deficiency is shown to be due in part to neglecting the Lloyd’s Mirror interference effect of the sea surface in the Lighthill-Curle equations. By utilizing a method of images solution to the acoustic analogy to simulate the Lloyd’s Mirror interference, an average sound pressure level improvement of 25 dB was obtained. This solution is compared and contrasted to a model utilizing the Lloyd’s Mirror interference of a simple source, and a normal mode sound propagation model, and is shown to be superior for transmission ranges up to 2 km.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".