Investigation of concentration‐dependent diffusion on frontal instabilities and mass transfer in homogeneous porous media
Bibliographic record
Abstract
Mass transfer plays an important role in influencing the efficiency of miscible displacements in solvent‐based processes in enhanced oil recovery. The mass transfer rate because of the pure molecular diffusion is very slow. However, this process can be greatly enhanced by the appearance of frontal instabilities called viscous fingering mechanisms, which are beneficial for improving the mixing and mass transfer between the injected solvent and oil. Instead of a piston‐like displacement, the interface between solvent and oil is very convoluted with intricate finger‐like patterns of the less viscous solvent intruding into the highly viscous oil. This intrusion significantly increases the surface area of contact of the two fluids and leads to more efficient mass transfer and mixing. Experimental measurements on the diffusion coefficients of two miscible fluids indicate that, instead of a constant diffusion coefficient (CDC), a concentration‐dependent diffusion coefficient (CDDC) is more realistic. In the present study, a CDDC relation in which the diffusion coefficient is exponentially proportional to concentration is adopted. Its effect on the development of frontal instabilities is examined through highly accurate nonlinear numerical simulations. The differences between the CDDC case and the widely assumed CDC case are discussed. Furthermore, the enhancement of frontal instabilities on mass transfer when the CDDC is considered is investigated at various mobility ratios and Peclet numbers. The special characteristics for the CDDC case indicate its important role in miscible displacements. Eventually, the relation of breakthrough time to parameters is correlated to accurately predict the breakthrough time in any CDDC scenario.
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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".