Dimensionless Groups of Parameters Governing the Ice-Seabed Interaction Process
Bibliographic record
Abstract
Abstract Prediction of subgouge soil deformation during an ice gouging event is a challenging design factor in Arctic subsea pipelines. An accurate assessment of ice keel–seabed interaction requires expensive model testing and large deformation finite element analysis. Proposing reliable analytical/empirical solutions needs a deep understanding of the key parameters governing the problem. In this study, dimensional analysis of subgouge soil deformations was conducted and eight dimensionless groups of parameters were identified to facilitate proposing potential new solutions. A comprehensive dataset was established for horizontal and vertical subgouge deformations in both sand and clay seabed. Using the identified dimensionless groups, linear regression (LR) models were developed to estimate the horizontal and vertical deformation. Moreover, a sensitivity analysis (SA), as well as an uncertainty analysis (UA), was carried out to identify the superior LR models and the most influential parameter group. A high range of correlation coefficient (R), Nash-Sutcliffe efficiency coefficient (NSC), and variance accounted for (VAF) along with a low range of errors was achieved for the best LR model. The results of the superior LR models were also compared with the existing empirical equations. The study showed that the shear strength parameters of the seabed soil and the ratio of gouge depth to gouge width are the governing dimensionless parameters to model the horizontal and vertical subgouge soil deformations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".