Prediction of Ship Acoustic Signature Due to Fluid Flow
Bibliographic record
Abstract
The contribution of flow noise to the radiated acoustic signature of CFAV Quest is of interest. Quest is the research ship used by Defence R&D Canada as a quiet platform. It is difficult to identify the flow noise component in an acoustic ranging so there is interest in predicting the flow noise as a first step towards extracting it from range measurements. Below propulsor cavitation inception speeds, machinery-induced noise dominates. While flow noise does not usually dominate in the presence of machinery-induced noise or propulsor cavitation, it is unclear what fraction of the total noise power flow noise constitutes. A direct numerical simulation for a complex ship geometry was impractical so an alternative approach was sought. An immersed boundary method was used to model the presence of the ship in the flow domain. The unsteady flow field was calculated using a finite volume method over an unstructured Cartesian grid. The flow field around Quest in straight and level flight was calculated at Reynolds numbers between 1.8×108 and 4.3×108, corresponding to a full-scale speed range of 4 to 10 knots. Results from such flow field predictions become the hydrodynamic sources in the integrals of Lighthill’s acoustic analogy to predict the far-field acoustic signature from the flow past the hull. These far-field predictions consist of computing the propagation and radiation of the hydrodynamic sources. This assumes noise generation and its propagation are decoupled. Under certain circumstances, knowledge of the predicted flow component helps to extract it from a standard acoustic ranging.
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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".