An Empirical Method for the Estimation of Towing Resistance of a Life Raft in Various Sea States
Bibliographic record
Abstract
Current IMO regulations require life rafts to be tow tested only in calm water. In real evacuation situations, life rafts are deployed in the prevailing environmental conditions, with wind and waves. Added wave resistance is small at low wave heights but increases nonlinearly with increased wave height. If life rafts are to be towed in moderate seas (up to 4 m significant wave height), tow force estimates based only on calm water tow resistance become less reliable. Tow patches, towline, towing craft etc. also need to be designed to withstand dynamic wave loading in addition to mean load. Therefore, mean tow force, tow force variation and maximum tow force are important. A full-scale 16-person, commercially available, SOLAS approved life raft was towed in the tank, in upwind, head seas with significant wave height of 0.5 m. The measured tow force showed that it could be treated as a linear system with wave amplitude, by demonstrating that tow force is mainly inertial and follows a Rayleigh distribution. Therefore, extreme-value statistics used for waves can be applied to developing equations for predicting tow force. A method is proposed to predict life raft tow force at different tow speeds and in various sea states, with waves and wind. The method involved using tank experiments to obtain tow force response for one sea state. The information can then be used to predict life raft tow force in wind and waves for different sea states. Three equations are proposed to demonstrate that a simple tank experiment could provide valuable information necessary to empirically estimate the mean tow force, tow force variation and maximum tow force for a specific life raft in different sea states. The equations are developed for upwind, head seas. These equations were extensively validated using tow force measured in the tank. They were partially validated with limited sea trial data, by towing the same 16-person life raft and a 42-person life raft in upwind, head seas with significant wave height of 1.3 m. The equations were able to predict maximum tow forces to within 15% of the measured.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".