Influence of nanoparticles on the dynamics of miscible Hele-Shaw flows
Bibliographic record
Abstract
In this study, we have made an attempt to address how the nanoparticle flows may affect the hydrodynamic instability around a miscible front. In order to explore the role of nanoparticles in such flows, a linear stability analysis was performed to examine the impact of nanoparticle addition for an already unstable miscible displacement. The growth rates of the temporal modes of the instability are determined for different profiles or physical properties of nanoparticles. The results reveal that the diffusion of either the carrier fluids or nanoparticles initially has destabilizing effects, but demonstrates stabilizing effects at longer times, as the cutoff spectrum is initially shifted to larger wavenumbers, but shifted back later. It was found that deposition of nanoparticles into the medium stabilizes the miscible front, such that the maximum growth rates and cutoff wavenumbers increase continuously.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".