A Numerical Study of Initiation and Migration of Trapped Oil in Capillaries with Noncircular Cross Sections
Bibliographic record
Abstract
To clarify the initiation and migration mechanisms of a discontinuous oil phase in pores, a numerical study was performed to interpret the starting phenomenon and flowing rules of oil trapped in capillaries that have noncircular cross sections. In this study, capillaries with three different cross sections were used to investigate the deformation law of oil and the pressure drop across these microchannels at different displacement velocities by computational fluid dynamics (CFD). The geometrical structure of the microchannels was precisely controlled, and the migration process of the oil, which is too small to be observed by direct experimentation, was assessed and quantitatively analyzed. By analyzing the shape of the trapped oil after reaching a steady state at different velocities, the nonstart and start conditions could be distinguished and the accuracy of the numerical method was verified by a comparison with an analytical method (the MS-P method). Two aspects of oil migration in noncircular microchannels were observed in combination with previous studies: there is a driving force on the cross section of the oil drop and a viscous force at the oil-water interface in the corners, and the more irregular the pore section is, the more easily the trapped oil will migrate. Additionally, the influence of the microchannel cross-sectional shape on the non-Darcy flow of a discontinuous oil phase was clarified. It can be concluded that the presence of the non-Darcy flow in pores arises because trapped oil, as a discontinuous phase, cannot be separated from the capillary wall without reaching critical velocity.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".