Direct Quantification of Dynamic Effects in Capillary Pressure for Drainage–Wetting Cycles
Bibliographic record
Abstract
The constitutive relationship between capillary pressure ( P c ) and wetting fluid saturation ( S w ), or retention curve, is needed to model multiphase flow in porous media. This relationship is usually measured under static conditions; however, transient flow is governed by a dynamic relationship between the P c and S w Differences in P c measured under static and dynamic conditions are due to dynamic effects typically defined as a product of a dynamic coefficient (τ) and the rate of change in S w To date, relatively few experimental studies have been conducted to directly quantify the magnitude of this effect. In this study, the magnitude of τ was quantified by measuring both static and dynamic retention curves in repeated drainage and wetting experiments using a field sand. The 95% confidence intervals for the static retention curves showed that the dynamic retention curves were statistically different. The measured τ for primary drainage generally increased with decreasing S w The measured τ values were also compared with those estimated using a different approach based on redistribution time. The measured and estimated τ were in close agreement when the redistribution times were 146 s for the wetting cycle and 509 s for primary and main drainage cycles. The shape of the τ– S w relationship was largely controlled by the slope of the static retention curve. Numerical modeling demonstrated that a log‐linear model relating τ and S w yielded the best match to experimental outflow results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".