Structural Response of Ice-Going Ships Using a Probabilistic Design Load Method
Bibliographic record
Abstract
Ships operating in ice-covered waters experience intense loads from ice features, particularly multiyear ice. Therefore, their structures have to be able to withstand these loads, making structural design paramount. Current formulations of ice class rules do not fully account for the probabilistic nature of ice loads, i.e. scale effects for local ice pressures captured in full-scale measurements. Furthermore, ice class rules do not consider route-specific ice conditions when calculating the design load, i.e. the exposure of the vessel to ice crushing determined by the number and duration of rams. An approach to arctic ship design based on probabilistic methods was developed by Jordaan and co-workers in 1993 and is described in this paper. The approach is used to estimate extreme design loads based on the annual interaction events and the design strategy (target exceedence criteria). The objective of this paper is to select an appropriate ice class for a vessel navigating along the northern sea route, and to compare the design requirements with those determined using the probabilistic approach based on measured data and expected exposure. Local hull pressures have been measured using the USCGC Polar Sea for a range of ice conditions including first year and multi-year ice. Impact conditions similar to those expected along the Northern Sea route were selected and corresponding pressurearea parameters used for input into the probabilistic approach discussed above. This paper will compare the design and response of an exemplary stiffened panel using the described approach to requirements given in Finnish Swedish Ice Class Rules. A case study structure will be analyzed using Finite Element Method for a chosen exposure scenario and target safety level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".