Pore-scale study on shear rheology of wet granular materials
Bibliographic record
Abstract
We study pore-scale rheological phenomena in two-dimensional sheared wet granular materials. Simulations use a coupled cascaded lattice Boltzmann and discrete element method, to model the liquid–gas multiphase flows and multiple-solid-particle dynamics, respectively. The wet granular material is prepared by first filling a rectangular domain with solid particles and then partially filling the pores between the particles with the liquid phase. The material is then sheared based on standard Couette flow configuration, i.e., with lid-driven velocities U and -U on the top and bottom walls, respectively. The simulations show that the apparent viscosity of the system attains a minimum when the material is wet but not fully saturated, i.e., at a saturation of ∼0.10. Such an observation is coherent both for materials composed of monodisperse and polydisperse particles. Interestingly, this observation coincides with the experimental finding of the decrease in sliding friction on sand by adding a small amount of water. The underlying mechanism is elucidated based on the pore-scale study of liquid patch dynamics. It is shown that, with increasing liquid saturation, the rheology of the wet granular materials is affected by two competing effects: (i) a larger number of liquid patches appear leading to fluidization of the system and (ii) larger patches are formed, clogging the flow. The minimum apparent viscosity saturation of ∼0.10 coincides with the maximum of the product of the two factors: the number of liquid patches and ratio between the system height and largest patch height.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".