Measurement and prediction of the relationship between capillary pressure, saturation, and interfacial area in a NAPL‐water‐glass bead system
Bibliographic record
Abstract
In this work, the constitutive relationship between capillary pressure ( P c ), saturation ( S w ), and fluid‐fluid interfacial area per volume (IFA) is characterized using computed microtomography for drainage and imbibition experiments consisting of a nonaqueous phase liquid and water. The experimentally measured relationship was compared to a thermodynamic model that relates the area under the P c − S w curve to the total IFA, a n , and the capillary‐associated IFA, a nw . Surfaces were fit to the experimental and modeled P c − S w − a n and P c − S w − a nw data in order to characterize the relationship in three dimensions (3D). For the experimental system, it was shown that the P c − S w − a n relationship does not exhibit hysteresis. The model is found to provide a reasonable approximation of the magnitude of the 3D surfaces for a n and a nw , with a mean absolute percent error of 26% and 15%, respectively. The relatively high mean absolute percent errors are primarily the result of discrepancies observed at the wetting‐ and nonwetting‐phase residual saturation values. Differences in the shapes of the surfaces are noted, particularly in the curvature (arising from the addition of scanning curves and presence of a n − S w hysteresis in the predicted results) and endpoints (particularly the inherent nature of thermodynamic models to predict significant a nw associated with residual nonwetting‐phase saturation). Overall, the thermodynamic model is shown to be a practical, inexpensive tool for predicting the P c − S w − a n and P c − S w − a nw surfaces from P c − S w data.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".