Assessment of Fully Eulerian Phase-Field Framework for Ship Hydrodynamics With Ice Interaction
Bibliographic record
Abstract
Abstract The harsh and dynamic environment of the Arctic poses significant challenges for ship design, particularly concerning the interaction between ship hull/propeller system and ice. The development of efficient and robust ice-going ships that can navigate ice fields with minimal resistance and local load on the hull, is an emerging application of fluid-structure interaction. Traditional methods of modeling ship-ice interaction often fail to capture the complex dynamics involved. High-fidelity numerical models are imperative to accurately predict the complex and coupled dynamics to optimize the design and operating parameters of ice-going ships. The complex hydrodynamics and contact dynamics involved in the ship-ice interaction result in structures undergoing large translational and rotational motions. In this paper, we present a first-of-its-kind parallelized 3D finite element solver as a powerful tool to model the ship-ice interaction. The unified continuum approach enables simulating multiphase dynamics across fluid and solid domains on a fixed Eulerian mesh. Interfaces are captured via a novel interface-preserving and geometry-preserving phase field method. We assess the proposed fully-Eulerian solver by modeling and analyzing submerged steel-ice contact dynamics as a relevant benchmark problem. Finally, a demonstration of a realistic ship entering an ice field follows, where we compare the ship resistance in an ice field against that in open water.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".