Numerical simulation of ship motion and non-linear sea loads of a modern frigate in regular waves
Bibliographic record
Abstract
Ship design, especially design of warships, requires precise predictions of sea loads in extreme wave conditions. In recent years, with the enlargement tendency of ships, the stiffness of ship hull has become weaker, resulting in more significant non-linear springing and whipping loads, which should be paid more attention to. This paper presents a numerical study of non-linear sea loads of a modern Canadian frigate in regular head seas based on a time-domain non-linear hydro-elastic prediction program. Non-linear Froude-Krylov forces, slamming forces and restoring forces are taken into consideration. Transfer matrix method is used to solve natural vibration of dry mode as an input of the hydro-elastic program. The numerical results are provided for comparison with model tests conducted by McTaggart et al. This numerical study will also be a part of the benchmark study of MARSTRUCT, which will provide a reference for an appropriate uncertainty analysis procedure in the future.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".