Ship Frame Research Program: a numerical study of the capacity of single frames subject to ice load
Bibliographic record
Abstract
This report presents results of a finite element analysis of ship frames subject to ice loads. The analysis covers the full range of frame behavior, from elastic, through yield, through the formation of initial mechanisms, through large deformations. The behaviors often include some local instabilities (buckling). The analyses continue until the total central deformation reaches about 10% of the frame span. The parameters include: · frame profile: Angle, Tee, Flat · frame span: · load length: patch (trans.), uniform (long-l) · web thickness: · flange thickness: · end brackets: with, without. The ANSYS finite element program was used in this study [1]. The aim of the study is to determine the validity of the limit state equation employed in the IACS new Unified Requirements for Polar Ships [2]. In particular, the study focuses on the reasons why some frames may not behave in accordance with the limit state equations, with local buckling and tripping as key issues. The report builds upon the work presented in [3]. In the present draft of the UR, there are no explicit tripping requirements. There are local buckling requirements, though they are essentially the same local buckling requirements employed widely in classification requirements for open water ships. This report examines the possible need for tripping requirements in the Polar Rules and the possible need for changes to the local bucking requirements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".