Rayleigh–Taylor instability in porous media under sinusoidal time-dependent flow displacements
Bibliographic record
Abstract
Linear stability analysis and nonlinear simulations have been carried out to analyze the Rayleigh–Taylor instability in homogeneous porous media under time-dependent flow displacements. The flow processes consist of a sinusoidal time-dependent velocity characterized by its period (T) and amplitude (Γ) and ensure that the same amount of fluid is injected over a full flow period. A new, more efficient approach to determine instability characteristics has been developed for the stability analysis of these time-dependent injection flows and showed a growth rate that varies in time like the displacement velocity. The effects of the period T and amplitude Γ as well as the fluids’ viscosity (R) and density differences (ΔG) have been analyzed. Consistent with constant injection displacements, a larger ΔG leads to stronger instabilities. Furthermore, it is found that a larger R tends to attenuate the instability during extraction and soaking periods and to enhance it during injection. This study also revealed that for a given total injection time, the time-dependent flow can be less or more unstable than its constant injection counterpart. In particular, for Γ < −1, larger periods lead to stronger instabilities with longer more developed fingers. For Γ > 1, on the other hand, it is found that larger periods tend to attenuate the instability resulting in a smaller number of fingers and a more diffused front. Flows with unit amplitude (Γ = 1) exhibit the same qualitative trends as but are overall more unstable than their counterparts with Γ > 1.
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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 | 0.001 |
| 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".