Enhancing Sustainability in Marine Structures: Nonlinear Energy Sink for Vibration Control of Eccentrically Stiffened Functionally Graded Panels Under Hydrodynamic Loads
Bibliographic record
Abstract
The research examines the impact of nonlinear energy sinks (NES) on the reduction in the nonlinear vibratory responses of eccentrically stiffened functionally graded (ESFG) panels exposed to hydrodynamic loads. To simulate real marine environments, hydrodynamic forces, such as lift and drag that change with velocity, have already been determined experimentally using Matveev’s equations for a particular ship. The material composition of both the panel and the stiffeners varies across their thickness. The stiffeners are modeled using Lekhnitskii’s smeared stiffener approach. Additionally, analytical approaches implement the classical shell theory (CST) with considerations for geometric nonlinearity, along with the Galerkin method for calculations. The P-T method is subsequently employed to determine the nonlinear vibratory behavior of ESFG panels. In this method, the piecewise constant argument is used jointly with the Taylor series expansion, which is why it is named the P-T method. The findings reveal that NES can effectively dissipate vibrational energy, contributing to the extended service life of marine structures while reducing the need for frequent maintenance. This study supports sustainability objectives by increasing energy efficiency, lessening structural fatigue, and improving the overall environmental impact of marine vessels and infrastructure.
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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".