Simulation of Ice-Structure Interactions Using a Coupled SPH-DEM Method
Bibliographic record
Abstract
Ice-structure interaction (ISI) is a complex process, which requires a thorough understanding of the underlying physics to ensure safe operations in the ice-covered regions. Application of discrete element method (DEM) to compute ice loads on structures is a widely accepted approach, where the equations of rigid body motions are solved for all ice pieces in the computational domain. In most ISI simulations, the ice zone is assumed to be resting on a static water foundation omitting the hydrodynamic effects (added mass, water drag, wave damping) of the interacting bodies. This assumption can introduce erroneous results to simulations of the floating ice floes behavior, which in turn will incur uncertainties in planning ice management activities. In this paper, a smooth particle hydrodynamics (SPH) based computational fluid dynamics (CFD) code is coupled with a three-dimensional DEM model to take the hydrodynamic effects of the interacting bodies including the ice pieces into account. The ice zone is modeled as discrete elements, which allows computing interaction forces by considering contact laws. The water foundation is modeled using smooth particles, which are modelled with the Naiver-Stokes equations. Several applications of ship and offshore structures interacting with level ice and pack ice are simulated. A scenario of an offshore supply vessel operating in the marginal ice zone (MIZ) that is subject to wave forces is also simulated to show how this approach can be used for modelling complex real-world problems. This scenario is unique in a sense that it yields a multi-physics solution, where ice-structure-wave are all included in a single CFD simulation as a fully coupled analysis. The cost of the simulation is significantly reduced by running the computations on a Graphics Processing Unit (GPU) instead of a typical CPU workstation. Some of the initial results of ice-structure interactions are presented in this paper and a reasonable agreement with reduced scale model test results are found.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".