SIIBED: Development of Numerical Models for Ice Loads on a Subsea Pipeline
Bibliographic record
Abstract
Abstract When designing subsea pipelines, flowlines or cables to traverse shallow offshore regions with icebergs, iceberg keel interactions may be of concern. Given sufficiently low contact rates, the possibility of laying the pipe or cable on the seabed without burial may be an option. Consideration is then needed regarding possible denting, buckling, or lateral forces which can result in high axial tensions. In previous analyses of keel interactions, the ice keels have been treated as rigid, under the assumption that the ice strength is significantly higher than the soil strength. Recent studies have shown that under rapid loading, soil resistance can be significantly higher than previously considered, while conservatisms in estimates of ice strength have been reduced over time. As a result, ice-pipe-soil interactions are being reassessed as part of a study "SIIBED: Subsea Ice Interaction Barriers to Energy Development" (Ralph et al., 2023). This paper discusses background and progress on one component of that study, the development of improved ice strength inputs for an overall ice-pipe-soil finite element model (Barrett et al., 2023). The paper includes a review of relevant literature and describes the use of different finite-element (FEA) techniques to better understand relevant ice failure processes. Calibration of the models is largely based on the results of a medium-scale test program using a novel test frame, RHITA (Rapid-High-capacity-Impact-Testing-Apparatus), which was designed and built especially for the project.
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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) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".