Influence of Ship Speed and Heading Profiles on Fatigue Damage Accumulation for a Naval Vessel
Why this work is in the frame
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Bibliographic record
Abstract
Ship speed and heading distributions are essential inputs for spectral fatigue analysis, and both may depend on wave conditions. Because rough-weather operational changes are rarely well defined, uncertainties in these distributions can introduce error in fatigue assessments. The influence of speed and relative heading distribution on fatigue estimates has not been thoroughly examined in the existing literature. This study investigates the influence of ship speed and relative heading distributions on fatigue damage accumulation of two sister naval ships. To represent uncertainties, 16 different operating profiles were used, including a baseline profile created from operator surveys and measurements. Fatigue damage estimates are calculated from a spectral analysis of four structural locations near midship. A linear frequency-domain seakeeping code provides the wave loads. The corresponding stresses are calculated using linear finite element analysis. Efforts to maintain seakeeping quality and crew readiness are reflected in the baseline profile with rough-weather speed and course changes. Ignoring these operational changes leads to reductions in estimated fatigue damage of up to 34% relative to the baseline estimate. This nonconservative result emphasizes the importance of understanding how operators manage rough wave conditions.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 it