Hydroelastic Dynamics and Vibration Characteristics of Partially-Submerged Hull Panels
Bibliographic record
Abstract
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.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".