Probabilistic Analysis of Local Ice Loads on a Lifeboat Measured in Full-Scale Field Trials
Bibliographic record
Abstract
This paper presents an analysis of local ice loads measured during full-scale field trials conducted in 2014 with a totally enclosed motor propelled survival craft (TEMPSC) in controlled pack ice conditions. These data were collected as part of an ongoing research program that aims to identify the limitations of conventional TEMPSC operating in sea ice environments and to provide insight as to how these limitations might be extended. During the 2014 trials, local ice loads were measured at two locations on the TEMPSC's bow area. These loads were the most severe measured to date and corresponded to an average ice floe mass that was approximately 1.25 times the mass of the fully loaded TEMPSC. The event-maximum method of local ice pressure analysis was used to analyze these field data to improve understanding of the nature of ice loads for such interactions and to evaluate the suitability of this approach for design load estimation for TEMPSCs (i.e., lifeboats) in ice. The event-maximum method was adapted for the present application, so as to link exceedance probabilities with design load levels for a given scenario. Comparison of the 2014 results with a previous analysis of 2013 field trials data supports earlier conclusions that these interactions are highly influenced by kinetic energy, since more massive ice floes are observed to impart significantly higher loads on the lifeboats. Illustrative examples examining the influence of ice concentration and sail-away distance have also been provided. The work establishes links between extreme loads and the exposure of the lifeboat to ice for different operating conditions. Based on this work it is concluded that the event-maximum method provides a promising approach for establishing risk-based design criteria for lifeboats if field data are available which adequately represent ice conditions encountered during the design life of the lifeboat.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".