The Hydrodynamic Noise Suppression of a Scaled Submarine Model by Leading-Edge Serrations
Bibliographic record
Abstract
High hydrodynamic noise is a threat to the survival of underwater vehicles. We investigated a noise suppression mechanism by putting leading-edge serrations on the sail hull of a scaled SUBOFF model, through numerical calculation and an experimental test. We found that the cone shape of leading-edge serrations can decrease the intensity of the adverse pressure gradient and produce counter-rotation vortices, which destroy the formation of the horseshoe vortex and delay the tail vortex. To achieve the optimum hydrodynamic noise reduction, we summarized the parameters of leading-edge serrations. Then, two steel models were built, according to the simulation. We measured the hydrodynamic noise based on the reverberation method in a gravity water tunnel. The numerically calculated results were validated by the experimental test. The results show that leading-edge serrations with amplitudes of 0.025c and wavelengths of 0.05h can obtain hydrodynamic noise reduction of at least 6 dB, from 10 Hz to 2 kHz, where c is the chord length and h is the height of the sail hull. The results in our study suggest a new way to design underwater vehicles with low hydrodynamic noise at a high Reynolds number.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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".