Micromechanical modeling of triphasic granular media
Bibliographic record
Abstract
This paper presents pore unit assembly-discrete element model (PUA-DEM), a pore-scale hydromechanical framework that resolves interactions between mobile granular particles and multiphase fluids in unsaturated granular media. The framework uniquely integrates DEM with pore-scale hydrodynamic models to capture unsaturated flow dynamics, while leveraging a two-way coupling mechanism to ensure bidirectional fluid-grain feedback through stabilized domain partitioning. Further innovations include a dynamic pore-merging and retriangulation algorithm that enhances computational efficiency for large-scale systems. Validated against experimental data for glass beads and Ottawa sand, PUA-DEM accurately reproduces critical hydromechanical phenomena-including capillary/viscous fingering, wetting-induced granular swelling/collapse, and quasi-static deformation-under diverse saturation and loading regimes. Numerical case studies reveal how capillary forces and wetting fluid saturation collectively govern granular response, from pore-scale meniscus evolution to macroscale flow instabilities. By bridging pore- and particle-scale physics, PUA-DEM advances predictive modeling of partially saturated granular systems, offering transformative insights for geohazard mitigation, sustainable agriculture, pharmaceutical manufacturing, and energy-related engineering applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".