A fully Lagrangian DEM-MPS mesh-free model for ice-wave dynamics
Bibliographic record
Abstract
This paper develops and evaluates a novel three-dimensional fully-Lagrangian (particle-based) numerical model, based on the hybrid discrete element method (DEM) and moving particle semi-implicit (MPS) mesh-free techniques, for modeling the highly-dynamic ice-wave interactions. Both MPS and DEM belong to mesh-free Lagrangian (particle) techniques. The model considers ice-wave dynamics as a multiphase continuum-discrete system. While the MPS solves the continuum equations of free-surface flow in a Lagrangian particle-based domain, the DEM uses a multi-sphere Hertzian contact dynamic model to simulate the ice floes motion and interaction. The hybrid model predicts the motion and collision of ice floes as well as their interaction with water, boundaries, and any obstacle in their way. Considering the mesh-free Lagrangian nature of both DEM and MPS, the developed model has an inherent ability to predict the free-drift (absence of internal stress) movements of the ice floes, e.g., sliding, rolling, colliding, and piling-up, in violent free-surface flow. A small-scale and challenging experiment based on dam-break flow over dry and wet beds with floating block floes, which mimics the characteristics of an idealized jam release, has been conducted to provide useful and comprehensive quality data for the validation of the proposed model, as well as other numerical models. Experimental and numerical results of the free-surface profile and the position of the blocks are compared. The results show the ability of the model to numerically reproduce and predict the complex three-dimensional dynamic behavior of wave-ice floes interaction. Overall, this study is a first effort toward developing an ice-wave dynamic within a fully Lagrangian framework (i.e. both flow hydrodynamics and ice dynamics in the Lagrangian particle-based system), and its results can be extended to bring an in-depth understanding of the physics of the real-scale ice-wave or river ice dynamic problems in the future.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".