Using Non-Linear Finite Element Analysis to Analyze the Effects of Connection Designs on the Ice Strength of a Vessel
Bibliographic record
Abstract
Abstract As global temperatures rise, ice clears in the Arctic Ocean and the demand for Canadian Coast Guard presence in the Canadian Arctic increases. The Canadian government intends to acquire two new heavy icebreakers as part of the “Polar Icebreaker Project”. These icebreakers are to be built to IACS Polar Class 2 standards. Some classification societies require fully welded collars to support stiffener penetrations through deeper hull structure in the ice-strengthened region of the ship. This, however, is a labour-intensive, material-heavy, and thus costly way to manufacture these vessels. This study analyzes hull structural response for three alternative penetration support details and compares these with the base case for a fully welded collar. The goal is to determine if there is a more cost-effective and less material-intensive way to implement these penetrations while maintaining the stiffness of a fully welded collar. A three-dimensional model of the port side ice strengthened structure of a Polar Class 2 vessel was investigated. A design ice load pressure patch based on the International Association of Classification Society’s unified rules for polar class was applied to the structure using finite element analysis. Results show that two of the three alternative connection designs gave unsatisfactory performance but that alternative connection designs with less steel and less welding can maintain acceptable structural strength.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".