Enhancing Crashworthiness of Aluminum Fishing Boats with Stiffener Plate Configurations
Bibliographic record
Abstract
Collisions involving fishing vessels pose a significant threat to maritime safety, often resulting in structural damage, loss of life, and environmental harm.This study investigates the crashworthiness of aluminum fishing boat hulls with the integration of stiffener plates to enhance structural resistance during collisions.Crashworthiness, defined as the ability of a structure to absorb impact energy, was analyzed through finite element simulations using ANSYS software.This study investigates the crashworthiness of aluminum fishing boat hulls with stiffeners to enhance structural resistance during collisions.The simulations modeled collision scenarios at different speeds (20 and 30 knots), with various stiffener configurations.The results showed that the inclusion of stiffeners increased energy absorption (EA) by up to 83%, underscoring the importance of stiffener design.These findings highlight a potential reduction in collision damage and improvements in maritime safety, offering practical guidelines for safer fishing vessel construction.Nonlinear structural responses were observed under high-speed impacts, underscoring the importance of optimized stiffener design.This research provides critical insights into the design of safer fishing vessels, offering practical recommendations for improving maritime safety and minimizing collision-related risks.Future work will include experimental validation of the simulation results to ensure the reliability of these findings for real-world applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".