Ice Gouge Interaction with Buried Pipelines Assessment Using Advanced Coupled Eulerian Lagrangian
Bibliographic record
Abstract
Abstract Ice gouging of the seabed is an issue in cold regions where ice or icebergscan contact the seabed. This creates a design challenge as pipelines must beburied to a sufficient depth to resist loads transferred through the soil tothe pipeline. However, as trenching and backfilling is expensive andtechnically challenging, a goal needs to be to optimize the depth ofburial. Conventional methods to evaluate the ice gouge loading on a buried pipelinerely on decoupled analysis of the ice-soil and soil-pipe responses, whichisbelieved to be a conservative approach and not adequatelyrepresentative. Inthis paper, ice gouging is investigated usingadvanced numerical modelling techniques to reduce this conservatism and betterquantify the loads experienced by the pipeline due to ice gouges. The Coupled Eulerian Lagrangian (CEL) finite element method is utilized in aseries of comparative studies based upon available centrifuge and full scaletest data. The CEL analysis results in relatively smaller sub-gougedeformation depth than the current guidance suggests, based on centrifugetests. The shallow sub-gouge deformation zone indicated appears to correlatewell with the limited amount of full scale test data available. If the lessconservative CEL result can be validated, significant burial depth reductionscan be achieved. CEL technology originally developed in the early 1960s however it has notbeen widely used until the late 1990s. The soil behaviour in this matter iscritical, thus the paper investigates the CEL capabilities to implementgeotechnical soil models i.e. Mohr- coulomb and Drucker-Prager yieldcriterion. Furthermore, the paper assesses the benefits and limitations of usingadvanced finite element methods to investigate ice-soil-pipe interaction, highlighting the constraints and the required further developments for thecurrent technology to enable the industry to better evaluate the risk of icegouging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".