Numerical simulation of electrokinetic control of miscible viscous fingering
Bibliographic record
Abstract
Active control of viscous fingering (VF) is of critical importance for many industrial and experimental systems. Here, we numerically study the electrokinetic control of miscible VF using an externally applied electric field. Simulations for three intrinsically hydrodynamically unstable mobility ratios are carried out using two different configurations for each: case I where the high-viscosity resident fluid has higher electroosmotic mobility than the invading low-viscosity fluid and case II where the resident fluid has a lower electroosmotic mobility than the invading fluid. For both cases, the theoretical critical electric field value required to (de)-stabilize the interface is computed and electric fields around this value are applied in simulations. Qualitative results show that VF can be fully suppressed if an electric field is applied with an absolute value above the critical field strength. For case I, this means an electric field in the direction of the pressure-driven flow, while for case II, a field in opposite direction is required. Our quantitative analysis using interfacial and mixing lengths was used to support the qualitative findings. Even though any field strength applied in the right direction will reduce the instability, full suppression is only achieved if the absolute field strength is higher than the required critical field strength. The results from this work provide useful insights that can be applied to electrokinetically enhanced oil recovery, spreading of pollution zones in aquifers, band broadening in liquid chromatography, and electrokinetic soil remediation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".