Acoustic signature and wake structure investigation of a cavitating marine propeller operating in proximity to a rudder with an optimized leading-edge pattern
Bibliographic record
Abstract
The present study implements high-fidelity numerical modeling to investigate the cavitating flow around a marine propeller operating upstream of a rudder with an optimized wavy leading-edge (WLE), based on a NACA 634-021 profile bio-inspired by a pectoral flipper of a humpback whale (Megaptera novaeangliae). The aim of the study work is to identify the acoustic signature and wake structure of the propeller-rudder system, comparing it to that with a straight leading-edge (SLE) rudder. The propeller (INSEAN E779A model) operated under diverse marine maneuvering conditions (rudder angles of attack α = 0°, 10°, and 20°) with three distinct leading-edge patterns of the rudder. Large eddy simulations (LES) in conjunction with the Sauer cavitation method and the compressive volume of fluid (VOF) model were utilized to simulate the unsteady cavitating flow using the OpenFOAM platform. Additionally, the Ffowcs Williams–Hawkings (FW-H) acoustic analogy, continuous wavelet transform (CWT), and fast Fourier transform (FFT) were employed to predict and analyze the hydroacoustic response. We propose an optimized propeller-rudder configuration for minimizing the radiated sound levels, thus mitigating the harmful effects of noise pollution on marine ecosystems, while maintaining high propulsive efficiency over a wide range of operating conditions.
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 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".