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Record W4413391792 · doi:10.1115/omae2025-157104

Hydroelastic Dynamics and Vibration Characteristics of Partially-Submerged Hull Panels

2025· article· en· W4413391792 on OpenAlexaff
Ishan Neogi, Vaibhav Joshi, Rajeev K. Jaiman, Jasmin Jelovica

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHullVibrationDynamics (music)Marine engineeringStructural engineeringComputer scienceAcousticsEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract The interaction between free-surface waves and marine structures plays a critical role in underwater radiated noise (URN) generation and structural integrity, particularly for ships and submarines. Hydroelastic vibrations of hull panels under wave-induced dynamic loading represent a major URN source, with implications for marine ecosystems and acoustic stealth. This study investigates the hydroelastic response of partially submerged hull panels subjected to free-surface wave interactions, focusing on deformation dynamics, modal characteristics, and the impact of panel stiffening. A representative thin square panel, immersed to varying depths, is analyzed using a nonlinear finite element framework coupled with a phase-field method for free-surface modeling. Structural motion is resolved through hydroelastic eigenmodes derived from linear elasticity equations. Results quantify the influence of the steepness of the wave and the immersion ratio, revealing an exponential decrease in deformation with increasing steepness of the wave and a shift toward suction-dominated deformation modes at greater immersions. Replacing the idealized panel with a stiffened configuration demonstrates a 94% reduction in deformation amplitudes, highlighting the efficacy of structural reinforcement. These findings advance the understanding of hydroelastic vibration mechanics and provide actionable insights for noise-mitigating marine design.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.385

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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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