Inertial effects in cyclic time‐dependent displacement flows in homogeneous porous media
Bibliographic record
Abstract
Miscible displacements in homogeneous porous media are investigated through numerical simulations for cyclic time‐dependent velocities considering inertial effects. It is found that the velocity's models, cycle period, and amplitude have significant impacts on the flow, however the effects greatly depend on whether inertia is considered or not. Generally, cyclic extraction‐injection (E/I) processes are always less unstable than constant injection and their injection‐extraction (I/E) counterpart, regardless of the strength of inertia. Instabilities of E/I processes are reduced as the velocity period or amplitude increases, and these attenuating effects increase with the Reynolds number. However, in the case of I/E processes, the effects of the period and the amplitude are strongly dependent on inertia. For non‐inertial cases, larger periods or amplitudes lead to more unstable flows, while for their inertial counterparts, the effects are found to be non‐monotonic. In particular, as the period increases, instabilities first decrease and then increase. A period‐stabilizing range is identified in which the displacements of the time‐dependent injection velocities are actually less unstable than those of the constant injection flow. When the period is further increased to the period‐destabilizing range, the flows tend to become more unstable. Moreover, the velocity amplitude can attenuate or enhance the instabilities depending on whether the period is within the period‐stabilizing or period‐destabilizing range. Furthermore, stronger inertial effects are found to greatly reduce the instabilities and to expand the period‐stabilizing range.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".