Impact of wettability on immiscible displacement in water saturated thin porous media
Bibliographic record
Abstract
The characterization of immiscible displacement processes at the pore scale is crucial in order to understand macroscopic behaviors of fluids for efficient use of multiphase transport in various applications. In this study, the impact of porous material wetting properties on gas invasion behavior at various gas injection rates was investigated for thin hydrophilic porous media. An experimentally validated two-phase computational fluid dynamics model was employed to simulate the dynamic fluid–fluid displacement process of oxygen gas injection into liquid water saturated thin porous media. A phase diagram was developed through a parametric characterization of the thin porous media in terms of the material hydrophobicity and gas flow rates. In addition to calculating the saturation of the invading gas, gas pressure variations were calculated and used to identify the locations of phase diagram boundaries. Non-wetting phase streamlines resolved at the microscale were visualized and presented as a novel indicator for identifying displacement regimes and phase diagram boundaries. It was observed that the crossover from the capillary fingering regime to the stable displacement regime occurred between contact angles of 60° and 80°. By increasing the gas injection rate, due to viscous instabilities, flow patterns transitioned from the capillary fingering and stable displacement regimes to viscous fingering regime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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".