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Data-based modeling of propeller tip-vortex cavitation noise for realistic acoustic ship signature

2025· article· en· W4413343936 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Acoustics · 2025
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsNexen (Canada)
FundersMinistry of Oceans and FisheriesDefense Acquisition Program AdministrationSejong UniversityKorea Institute of Marine Science and Technology promotionKorea Research Institute for Defense Technology Planning and Advancement
KeywordsCavitationPropellerVortexAcousticsSignature (topology)Noise (video)Marine engineeringPhysicsAerospace engineeringEngineeringMechanicsComputer scienceMathematicsGeometryArtificial intelligence

Abstract

fetched live from OpenAlex

Propeller tip-vortex cavitation (TVC) noise significantly influences the acoustic signature of a navigating vessel; however, its detailed simulation has not been extensively developed thus far, owing to the complexity of TVC noise. This paper proposes an advanced data-based method for generating propeller TVC noise to enhance the realism of passive sonar simulators. Generative adversarial networks are applied to randomly create waveforms of TVC noise sources, and a probabilistic representation of TVC occurrences is adopted to continuously generate them depending on the ship speed. The proposed method is validated using the performance metrics of statistical measures and auditory features. The signals simulated by the proposed method effectively represent the time–frequency characteristics of the propeller TVC noise depending on the ship speed.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.265
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it