Stability of double-diffusive double-convective miscible displacements in porous media
Bibliographic record
Abstract
The present study offers a paradigm on the stability of two-component miscible displacements in a homogeneous porous medium. The components have, in general, different mobility ratios, may diffuse at different rates, and are convected at different speeds. As a result, one of the components may lag behind the other. For the adopted transport models, it is found that the differences in the rates of diffusion and the lag between the two component fronts resulting from the differences in the speeds of convection can modify radically the instability characteristics. In particular, an unstable single-component displacement is always made more unstable by the presence of a second unfavorable component. The instability of the same flow is, on the other hand, attenuated by the presence of a favorable, less diffusive lagging component. However, this same flow instability can actually be enhanced when the favorable lagging second component is more diffusive. Furthermore, the larger the lag between the two components is, the more unstable the flow is when the lagging component is favorable to the displacement. An opposite trend is found when the the lagging component is unfavorable to the displacement. Finally, changes due to the lag between the two components fronts are stronger for a less diffusive lagging component and weaker for a more diffusive one. For illustration, these results are discussed in the special context of a thermal displacement where mass and heat are transported in the porous 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".