A Numerical Study of Interaction Between Ice Particles and Complex Ship Structures
Bibliographic record
Abstract
Abstract This paper describes a numerical method of ship-ice interaction. Identifying an operational risk to an ice breaking vessel such as serious interference or sudden efficiency reduction by ice is necessary at early design stage. CFD-DEM coupled approach can be used for this prediction. Combining two numerical approaches, free floating ice particles can interact with the surrounding fluid as well as the rigid hull surface effectively. Ship-ice interaction becomes more complicated around the stern area considering the swirling flow of the propulsion device and the complex hull appendages. Using CFD-DEM method, simulation of ship-ice interaction during the astern operation was carried out. To evaluate the simulation performance, three example cases were conducted. The first and second case is the astern operation with/without the propeller action. For reducing the computational load, a virtual disc model for the propeller action is selected. Then, a simple sensitivity analysis on the ice friction coefficient was carried out to verify if the friction model is working appropriately. CFD-DEM simulation provides a promising result showing a similar ice jamming and pileup pattern with the physical model test. The suggested method can help to understand the ice movement effectively including the hydrodynamic force and the contact dynamics together.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".