A 3-Dimensional Eulerian Finite Element Model for Ice Scour
Bibliographic record
Abstract
The main objective of this paper is to present a Finite Element (FE) numerical model of the ice scour process. The FE model is developed to study the soil deformation and transport process around the scouring ice and to investigate the effects of the ice scour on a pipeline buried or laid in a trench cut on the seabed. The focus of this paper is on the scours caused by ice ridges commonly observed in the Beaufort Sea. The developed FE model is a new application of the Arbitrary-Lagrangian-Eulerian (ALE) method to a soil mechanics problem involving very large deformations. Soil material, originally positioned in front of the ice ridge, is transported forward and sideways through the FE mesh and deposited in the berms formed on both sides of the scour. The soil material below the scour depth similarly moves across the mesh simulating the subscour effect. An inviscid CAP plasticity constitutive model is used to model the soil material. This paper focuses on the interaction between the ice ridge and the seabed. It describes soil transport process involved during the interaction. The soil deformation field obtained from the model is compared with the empirical deformation functions commonly used in current design methods. Future papers will report on the interaction between the ice ridge, the infill in the pipeline trench, and the pipeline; the influence of the soil properties of the trench and the seabed will also be studied.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".