Application of Upcrossing Rate Methodology to Local Design of Icebreaking Vessels
Bibliographic record
Abstract
Abstract Design of icebreaking vessels or ice-capable vessels must include consideration of extreme local ice pressures and exposure. Application of probabilistic methods used for data analysis, and then directly applied in design, with consideration of exposure is most useful. The Maximum Event (ME) Method is formulated for cases where ramming of ice is the dominant event. The peak pressures on a hull panel through the ram duration for each ram event is modelled. Data for each panel area are ranked and an exponential distribution fit to the tail of the ranked data. For design, we are then concerned with the maximum of n events expected in a specified period of time (e.g., a year). The random occurrence of ice along a route will also translate in to an annual number of expected impacts depending on ice and vessel dimensions. Vessel Ice classes would correspond to an annual number of impact events. This approach was used during the Arctic Shipping Pollution Prevention Regulation (ASPPR) revisions to validate maximum forces for different class vessels (e.g., a CAC4 vessel would be designed for 10-15 rams per year). For continuous-type interactions (e.g., a large floe crushing around a stationary vessel, or continuous icebreaking) an alternative approach is to use the Up-crossing Rate (UCR) Method. The number of local pressure upcrossings above a specific threshold on a particular panel area within a specified time (e.g., one year of operation) is determined. Exceedance curves for the UCR for increasing pressures on incremental panel areas are determined including an exponential fit to the tail of the distribution. For design, one only needs duration of interactions with the specified ice conditions through the year. The methodology was exercised to estimate local pressure parameters for transit segments during the ODEN 1991 trials. The greater the number of impact events and the longer the event duration, the greater the local pressures on panel areas. These high pressure zones occur and disappear, randomly shifting in location and intensity as fracture and spalling processes reshape the interaction area. For future development of the ISO design code, it is recommended that two modeling approaches be considered, the traditional ME method for short duration events and the UCR method for continuous interactions. The UCR method provides a simple means to design icebreaking vessels for extreme local pressures during continuous interactions. The method is also attractive for design of a stationkeeping vessel operating in broken ice where modeling individual floe interactions is not practical.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".