Investigation of Iceberg Hydrodynamics
Bibliographic record
Abstract
Abstract As offshore oil and gas developments increase in northern areas such as the Grand Banks and the Arctic region, the operators face challenging conditions. Icebergs are among one of the challenges for both surface and subsea structures if they drift toward those facilities. Prediction of the iceberg drift and dynamic response to any towing process requires a good understanding of hydrodynamic effects induced by currents, waves, tow lines, etc. A reasonable estimation of added mass and RAOs are other prominent parameters required when modeling iceberg dynamics is of interest. Having access to the high resolution full 3D iceberg profiles collected in 2012 (Younan et al. 2016), it is now possible to investigate iceberg hydrodynamics using numerical and experimental methods. This paper presents an overview of the numerical simulation results and lessons learned during various hydrodynamic simulations such as decay analysis, towing, and iceberg-structure interaction. The Diffraction Model and Computational Fluid Dynamics (CFD) are the tools utilized in these simulations. The conclusions provide key findings and suggestions for future analysis of iceberg hydrodynamics.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".