Considering Surface Roughness Effects in a Triangular Pore Space Model for Unsaturated Hydraulic Conductivity
Bibliographic record
Abstract
Quantifying the unsaturated hydraulic conductivity of a porous medium has been a great interest in the fields of hydrology, environmental engineering, and petroleum engineering. Previous research has shown that rough surfaces enhance liquid retention and conductance of flow in the form of liquid film. We present a pore‐scale‐based water retention and hydraulic conductivity model considering surface roughness effects. In the proposed model, a porous medium is simplified as a bundle of statistically distributed capillaries with triangular cross‐sections. Surface roughness effects are characterized by a roughness factor, which accounts for increased film thickness under relatively wet conditions due to capillary effects and increased film area under relatively dry conditions. The model significantly improved the prediction of hydraulic conductivity across the entire range of matric potentials for the illustrative soils compared with the van Genuchten–Mualem model (VGM), while maintaining the same number of adjustable parameters. The improved performance of the proposed model demonstrates the advantage of incorporating surface roughness in the pore‐scale‐based models. Furthermore, sandy soils and loams showed distinct roughness factors and pore‐size distribution functions. Sandy soils tended to have smaller roughness factors and greater mean pore sizes than loams.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".