Global Ice Loads on Arctic Drillships
Bibliographic record
Abstract
This paper reports on an exercise to predict ice loads on Arctic drill ships using analytical, model ice basin test-based empirical, and numerical methods. An example drillship has been employed, and pack ice has been considered in theoretical and numerical analyses. Two analytical models for predicting the ice force have been employed for the head-on ice-ship interaction scenario with low ice concentration. One model is that the ice load is estimated as the average rate of momentum transfer between ice floes and ship hull. Another model is that the ice load is estimated based on the calculation of the motion and drag of ice floes as they move around the ship. In numerical modeling, a parametric study has been conducted to simulate different ice structure interaction scenarios using the Particle-In-Cell (PIC) method. Different parameters including ice thickness, ice concentration, ice movement velocity, and ice movement direction have been investigated in the interaction modeling. Based on numerical results, a formula for calculating the ice load has been developed to reflect the role of the pertinent parameters on expected ice forces and movements of the drillship. The comparison of results from all ice load models and numerical modeling shows a reasonable agreement. This study serves to help bound estimates of load and also provides insights into the different methods of global ice load prediction for this particular application.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".