Relative permeability characteristics necessary for simulating DNAPL infiltration, redistribution, and immobilization in saturated porous media
Bibliographic record
Abstract
This study presents a relative permeability‐saturation (kr‐S) constitutive model that incorporates the critical phenomena necessary for simulating the rates of nonwetting fluid infiltration, redistribution, and immobilization in a saturated porous medium. To develop a model validation data set, the migration of a dense, nonaqueous phase liquid (DNAPL) pool within a one‐dimensional, 1 m tall, saturated sandpack was monitored under alternating drainage and imbibition conditions. A light transmission/image analysis system, able to distinguish between connected‐phase and residual nonwetting phase (NWP) in the apparatus, measured the elevation of the top of the connected‐phase DNAPL pool as a function of time. Light transmission calibration curves, correlating fluid saturation to transmitted color at the macroscopic scale, were found to exhibit a functional dependence on saturation history that must be taken into account. Applying the calibration curves to captured images of the experiment provided a continuous sequence of fluid saturation profiles. Numerical simulations of the bench‐scale experiment, using model parameters measured independently at the macroscopic scale, predict within measurement uncertainty the observed timescales of DNAPL migration and immobilization. Additional simulations reveal that model validation for imbibition processes depends on properly accounting for NWP kr‐S hysteresis, including imbibition function curvature and the abrupt extinction of NWP relative permeability.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".