Effects of flow rate and pore size variability on capillary barrier effects: a microfluidic investigation
Bibliographic record
Abstract
Capillary barrier effects (CBEs) have been applied in capillary barrier systems as effective means of protecting underground regions from wetting. However, the microscale behavior of CBEs and related influencing factors are not well understood. This study utilized microfluidics to investigate effects of flow rate and pore size variability on CBEs at the microscale. Imbibition processes of water displacing air were imposed on three water-wet microfluidic chips with different degrees of pore size variability and injection rates. The obtained results demonstrated that the increasing injection rate changed water invasion pattern from finger growth to compact displacement. The CBEs could increase Swf (water saturation of fine sections at the onset of breakthrough) by up to 44%. The increase in Swf became smaller at a higher injection rate but was insensitive to the pore size variability within the investigation range. This study also elaborated the specific effect of inertia on pore body invasions under different pore characteristics. Among the materials with close average pore size, those with more uniform pore sizes are recommended for the construction of capillary barrier systems. For capillary barrier systems with robust CBEs, increasing the thicknesses of coarse layers has insignificant effect on reducing deep percolation.
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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 | 0.000 |
| 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".