High-fidelity modeling of cavitating flow around a marine propeller with bio-inspired wavy rudder configurations: Radiated noise analysis using hydro-acoustic analogies
Bibliographic record
Abstract
This study implements high-fidelity modeling to analyze the hydrodynamic and hydroacoustic characteristics of bio-inspired marine vessel rudders, modeled after the fins of a humpback whale. The focus is on tubercled leading-edge rudders (TLER), with an amplitude of 5% and the wavelengths of 25% and 50% of the mean chord length, compared to a straight leading-edge rudder. Simulations are performed at the deflection angles α = 0°, 10°, and 20° using the large Eddy simulation turbulence model and the Schnerr–Sauer cavitation model, in conjunction with a volume of fluid cavity tracker, combined with the Ffowcs Williams-Hawkings hydroacoustic analogy within the OpenFOAM framework. The goal is to optimize the propulsion system and reduce radiated noise from the propeller/rudder system under cavitation conditions. The study examines the impact of propeller/rudder flow dynamics on parameters such as sound pressure level, thrust, acoustic spectrum levels, rudder force, and separation/spanwise flow. Key focuses include the formation, development, and dissipation of dominant wake morphologies—such as spiral tip, root, hub, propeller, and rudder trailing edge vortices—and cavitation features, including sheet, hub, and tip cavitation along the propeller surface, as well as rudder surface cavitation. Interactions between these features, noise generation mechanisms, and their impact on identified acoustic frequency modes are examined for various TLER designs and maneuvering conditions to improve acoustic efficiency. Phenomena, such as counter-rotating vortex pairs, low-pressure zones, separation, and reattachment near the TLER, short-wave instabilities, tip vortex breakdown, vortex pairing with mutual inductance, progressive wake weakening, and minor meandering, are analyzed across low-, medium-, and high-frequency ranges within narrow-band and broadband spectral characteristics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".