Pore network simulation of the dissolution of a single‐component wetting nonaqueous phase liquid
Bibliographic record
Abstract
Soil wettability has been recently recognized as a factor that can dramatically influence the dissolution behavior of residual nonaqueous phase liquids (NAPL). A NAPL that wets the solid surface is trapped within the smaller pores and along the corners of pores invaded by water (the nonwetting phase). We present a two‐dimensional network simulator of wetting NAPL dissolution, inspired by observations of this process in transparent glass micromodels. The network model idealizes the pore space as a network of cubic pores connected by square tubes, following respective distributions. In accordance with micromodel observations, capillary equilibrium is assumed to exist between NAPL‐water interfaces along pore corners and within pores. Advection and diffusion of the organic dissolved in the aqueous phase, as well as dissolution mass transfer from residual NAPL, are explicitly accounted for in the model. Pores filled with NAPL are invaded at a rate which is controlled by mass transfer from dissolving thick NAPL films in pore corners and in order of increasing entry capillary pressure, resulting in quasi‐static drainage and fingering of the aqueous phase. Loss of NAPL continuity due to rupture of thick NAPL films and heterogeneity are found to affect profoundly the dissolution behavior, resulting in concentration tailing. The network simulator reproduces qualitatively the behavior observed in column experiments with oil‐wet media.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".