Modeling of a full scale DP in ice scenario using an advanced ice dynamics model
Bibliographic record
Abstract
This paper presents a comparison between a recently completed physical model test program, a full scale expedition and predictions using an NRC-OCRE Ice Dynamics Model (IDM), for a representative scenario of dynamic positioning of an ice management vessel. This work aims at enhancing the confidence in the numerical results, and to make it possible to extrapolate ice basin tests and field observations to a wider range of conditions. Full scale data were obtained during field trials of a dynamically positioned multi-purpose vessel, Magne Viking (MV). The collected data include thrust values, ship motion and observations of ice conditions. In parallel to the field measurements, physical model tests examined ice-vessel interactions and ship performance. In this paper, a representative trial scenario was selected from the full scale expedition for modelling in NRC-OCRE’s ice basin as well as for numerical modeling using the IDM code for predicting the ice forces on the hull. A comparison of the thruster forces and vessel movements derived from the three studies is presented in this paper. This work indicates that the numerical simulations can predict the full-scale stationkeeping scenarios with reasonable accuracy. Thus, numerical simulations may be used to examine many practical scenarios which cannot be obtained in the basin and in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".