Hysteresis of liquid adsorption in porous media by coarse-grained Monte Carlo with direct experimental validation
Bibliographic record
Abstract
The effects of path-dependent wetting and drying manifest themselves in many types of physical systems, including nanomaterials, biological systems, and porous media such as soil. It is desirable to better understand how these hysteretic macroscopic properties result from a complex interplay between gasses, liquids, and solids at the pore scale. Coarse-Grained Monte Carlo (CGMC) is an appealing approach to model these phenomena in complex pore spaces, including ones determined experimentally. We present two-dimensional CGMC simulations of wetting and drying in two systems with pore spaces determined by sections from micro X-ray computed tomography: a system of randomly distributed spheres and a system of Ottawa sand. Results for the phase distribution, water uptake, and matric suction when corrected for extending to three dimensions show excellent agreement with experimental measurements on the same systems. This supports the hypothesis that CGMC can generate metastable configurations representative of experimental hysteresis and can also be used to predict hysteretic constitutive properties of particular experimental systems, given pore space images.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".