Estimating Ship Underwater Radiated Noise from Onboard Vibrations
Bibliographic record
Abstract
The acoustic signature of an Orca-class training vessel (Patrol Craft Training, PCT) Moose from the Royal Canadian Navy (RCN) was measured at the RCN’s Patricia Bay acoustic range on Vancouver Island, British Columbia, Canada. The acoustic range trials included accelerometer measurements on the ship hull and in the engine room and hydrophone measurements at approximately 100 m from the ship. The trials were carried out at the ship speed range of 3 to 20 knots. The test data from all the trial runs was used to derive, evaluate and validate the method of estimating ship underwater radiated noise from onboard vibrations. In the investigation, the runs were split into two sets: a training set and a testing set. A least squares approximation, AQV (average quadratic velocity) SL (source level) correlation, was then applied to the training set data to formulate a transfer function to estimate the underwater radiated noise from onboard vibrations. The AQV is calculated from accelerometer measurements (vibration levels) and SL is obtained from the hydrophone measurements. The third octave frequency band (from 10 Hz to 10 kHz) SL estimations of the testing set runs (using the transfer function and AQV) are within 1 to 3 dB of SL from the hydrophone measurements. This study demonstrates a capability of monitoring underwater radiated noise from ships using only onboard vibration levels which may be of interest for future projects relating to the reduction of shipping noise against a threshold in acoustically sensitive environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".